Intra-seasonal Variability and Physical Characteristics of Break and Active Phases in the Mainland Indochina Southwest Monsoon

Intra-seasonal Variability and Physical Characteristics of Break and Active Phases in the Mainland Indochina Southwest Monsoon | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Intra-seasonal Variability and Physical Characteristics of Break and Active Phases in the Mainland Indochina Southwest Monsoon KYAW THAN OO, Kazora Jonah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4936295/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The term ``break`` is traditionally applied only to dry spells occurring after the monsoon onset in the region. Simply put, the daily rainfall of the monsoon is paused over the region, for a few days, called a “break spell.” The researchers have suggested that the standardized anomalies of three consecutive days of rainfall should prevail to categorize the active and break spells. This study examined break spells and active spells on the inter-annual, intra-seasonal, and decadal scales by examining the frequency and spatial distribution of daily rainfall occurrences of different intensities linked to break and active events over the mainland Indochina region. The difference in the vertical meridional circulation between the active spells with moist convection and intense break events with heat through circulation was explained by various atmospheric parameters. La Niña encourages more break days than active days, and the distinction in vertical meridional circulation between intense break events with a heat trough type circulation and active spells with moist convection is crucial for developing suitable prediction tools. Monsoon Rainfall Monsoon Break MSWM Myanmar Rainfall Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 1 Introduction The mainland Indochina is situated in the north of Southeast Asia (SEA), sandwiched between India, China continent and South China Sea. The Bay of Bengal (BoB) can be found on its west shore. But also its experiences two distinct monsoons: the Mainland Indochina northeast monsoon (MNEM) from November to April and the Mainland Indochina southwest monsoon (MSWM) from May to October. Rainfall, which is a crucial component of a monsoon climate, has a big impact on managing water resources and agricultural production (Aung et al., 2017 ; Endo et al., 2009 ; Oo, 2022 ). In mainland Indochina, agriculture accounts for 60% of the GDP and is mostly dependent on MSWM rainfall, which is the main water source (Ghosh et al., 2016 ; Satyanarayana et al., 2020 ). There is a typical monsoon climate in eastern Bay of Bengal region and the MSWM affects nearly 75% of the annual rainfall during the raining season (as global terms called summer). The rain patterns are non-uniform and subject to high variability over annual time scale (Oo, 2023 ). Additionally, intra-seasonal and inter-annual variations in rainfall patterns are possible, posing major risks to agriculture, water resources, and the long-term sustainability of a region (Ren et al., 2017 ; Sein et al., 2021 ). According to Oo et al., ( 2019 ); Zaw et al., ( 2021 ), extreme weather conditions are linked to the Indian and Pacific Oceans' larger-scale air circulations, particularly El Niño southern oscillation (ENSO). In the mainland Indochina, long-term climatic variance is significant for climate research because it influences seasonal to inter-annual climate variability. The variability dominant modes of the mean MSWM rainfall over study area with positive anomalies in the northern and southern regions and negative anomalies central region by using EOF analysis are already exhibited in previous studies (Sein & Zhi, 2016 ). Figure 1 . a shows climatological southwest monsoon daily rainfall over Myanmar, the annual pentad daily rainfall shows in Fig. 1 . b . Further, Fig. 1 . c explained seasonal latitudinal shifting of longitudinal average (90°E-105°E) MSWM daily rainfall (mm day − 1 ) (day of the year) during 1981–2020,which may well explained the clear pattern of MSWM. During the onset stage of MSWM, the ITCZ moves quickly northward, reaching about 20°N as the Southern Hemisphere (SH) ’s Hadley cell suddenly becomes stronger and deeper into the Northern Hemisphere (NH), while the NH’s Hadley cell almost disappears (Fig. 2 ). This change is accompanied by a sudden augmentation and strengthening of upper-level tropical easterlies wind, which accompany the westerly subtropical jet’s northward movement over the NH, and a strengthening of the SH’s subtropical jet (Fig. 2 . a ). This seasonal transition in precipitation and winds is consistent with previously reported observations for the South Asian Monsoon (e.g., Webster&Yang, 1992 ; C Li, 1996 ; Trenberth & Shea, 2005 ). It is crucial to remember that the seasonal transition in Hadley circulation is similar, as a weak or negligible summer cell occurs rather than the traditional equinoctial pattern of two Hadley cells alternating at the equinox during the summer season (Dima & Wallace, 2003 ; Nguyen et al., 2013 ; Shaw, 2014 ). These parallels suggest that the MSWM mainly projects over the zonal mean Hadley cell (e.g., Gadgil, 2003 ; Privé & Plumb, 2007 ; Schneider & Bordoni, 2008 ). A monsoon trough zone fluctuated in rainfall between heavy and light rainfall amount, and Blanford ( 1886 ) firstly explained the nature of pressure distributions and circulations in these phases of contrasting rainfall conditions. Based on the synoptic features over monsoon core region of India and the first established criterion for break spells in the southwest monsoon season, Ramamurthy ( 1969 ) used long-term data spanning 80 years (1888–1967) to conduct a complete investigation of monsoon breaks. Break days were identified using similar criteria are used in the Ramamurthy ( 1969 ) and P. V. J. S. Gadgil ( 2003 )’s work also. Additionally, the two periods of break and active of India summer monsoon (ISM) rains have been the subject of extensive research (D. R. Sikka, 1980 ; Goswami et al., 2003 ; M Rajeevan, 2010 ; Pai et al., 2016 ; J. S. V Krishnamurthy, 2008 ; Webster et al., 1998 ). It is common to identify the dry and wet seasons of the ISM in the area of the seasonal monsoon trough (R Krishnan, 2000 ; P. V. J. S. Gadgil, 2003 ). There are a lot studies regarding the ISM break, however the MSWM area still needs a proper definition to define break and active events. The MSWM's active phase is described as the time when periods of heavy rainfall occur across the entirety of Myanmar, the center region (C) being the exception as shown in Fig. 1 . The break phase occurs when there are periods of typically dry weather and insufficient daily rainfall. According to Wang et al., ( 2009 ), floods for the active and droughts for the break could occur respectively, and lasting for extended periods, which could have an effect on society's economy. MSWM rainfall variability is not only important for agricultural crop production and at the inter-seasonal, but also detailed analysis are need for the long-term levels planning. To define the exact characteristic of monsoon break, this study's initial objective was to identify and record all break and active phases during a 40-year period (1981–2020) by using the criteria of M Rajeevan, ( 2010 ). Based on the daily gridded rainfall data's standardized anomaly, the mean characteristics of each break spells and active spells are computed. To avoid the influence onset and withdrawal physical mechanism, the break and active events of only the peak monsoon intensity months (PMIM) (July and August) are considered. The standardized anomaly of 3 consecutive day rainfall values over the main monsoon core zone are denoted that either above + 1 (active) or below − 1 (break) for the three consecutive days, depending on the event type. One of previous study has also shown a downscaling of the south west monsoon's break and active phases (Narayana Rao et al., 2016 ; Prathipati et al., 2021a ). It is, however, impossible to infer a reliable average feature from these studies, which make conclusions based on one or two cases or a few years of data, and only a few have stressed the need for a comprehensive climatological analysis of break and active spells across space and time. In this work, we have attempted to implement a thorough analysis using the 40 years of data from both stations observation data and gridded datasets, as well as the disparities between the composite patterns linked to the both spells in terms of the composite mean distributions of rainfall event frequencies (Fig. 3 ) . The study also examined the differences values between the composite monsoon rainfall patterns of all events. In addition, the study examined the distribution of lower to upper atmospheric wind patterns and sea level pressure (SLP). The data and methodology utilized in this investigation are described in Section 2. The study's numerous findings are discussed in Section 3, 4 and 5 before its summary and conclusions are summarized in Section 6. Further, the author hope that the circumstances of this study output will be useful as the basis for forecasting and monitoring drought and floods in study region. 2 Data and Method The high-resolution ECMWF reanalysis version 5 (ERA5) for the period 1981–2020 served as the primary data set for the current investigation (Hersbach et al., 2020a ). Moreover, meteorological data from 79 stations of Department of Meteorology and Hydrology, Myanmar (DMH) around Myanmar was gathered for validation, particularly daily mean sea level pressure (MSLP) and daily rainfall data (Zin et al., 2017 ). Daily rainfall data from CPC gauged based data are also used to analysis break and active MSWM events (Takahashi & Yasunari, 2006 ). For the composite analysis, the daily means of relative humidity (RH), temperature, zonal and meridional winds (U,V) at different levels, and total precipitation, MSLP and outgoing long rang radiation (OLR) are considered from the ERA5 for the period 1981–2020. For testing the differences in the composite rainfall/OLR anomalies and frequency of daily rainfall events between active spells and break spells conditions, two-sample t-test was used. Then, we estimated annual standardized values for each parameter for statistical analysis. Frequency is a measure of the number of occurrences of a particular score in a given set of data. The climatological is the examination of each month and season to determine the mean proportion of occurrences of meteorological factors in the study area. Thus, we can calculated that the frequency of the weather element is divided by the total number of observation times and then multiplied by 100% to get the frequency percentage (Carlson & Winquist, 2014 ). Frequency Percentage = \(\:\frac{\text{F}\text{r}\text{e}\text{q}\text{u}\text{e}\text{n}\text{c}\text{i}\text{e}\text{s}\:\text{o}\text{f}\:\:\text{e}\text{l}\text{e}\text{m}\text{e}\text{n}\text{t}}{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{o}\text{f}\:\text{O}\text{b}\text{s}\text{e}\text{r}\text{v}\text{a}\text{t}\text{i}\text{o}\text{n}\:\text{T}\text{i}\text{m}\text{e}\text{s}\:}\) * 100 There are several statistical relationships for perceiving the relationships between two variables which are expressed in both linear and non-linear equations. The changing trend of break spells and active spells frequency over study area was evaluated by linear trend estimation test at a 95% confidence level. This approach has the advantage that it does not require any assumption on the data distribution. In additional, the correlation coefficient (CC) is the major statistic for explaining the connection between variables. This index shows the degree and direction of correlation between a variable and its environment. The following equation is used to determine the Pearson CC (r) value: $$\:r=\frac{n\left(\sum\:xy\right)-\left(\sum\:X\right)\left(\sum\:y\right)}{\sqrt{[n\sum\:{x}^{2}-{\left(\sum\:x\right)}^{2}\left]\:\right[n\sum\:{y}^{2}-\:{(\sum\:y)}^{2}]}}$$ One can also calculate p-value and compare it with 0.05 significance level. If p-value is smaller than 0.05, the Pearson CC can be considered to be statistically significant and the null hypothesis rejected in bias of the alternate hypothesis. The other statistical calculation of mean, median and trend method are also used in this study. 2.1 Definition of Break spells and Active spells To define break spells and active spells, M Rajeevan, ( 2010 ) presented a criterion based on the standardized rainfall anomaly (less than − 1 or greater than + 1) for at least 3 consecutive days over the monsoon core regions. As a point of comparison,, Pai et al., ( 2016 ) identified the both break and active days by using the similar criteria (rainfall) as M Rajeevan, ( 2010 ) for 114 years between 1901 and 2014. Due to the heat trough circulation characterizes long intense breaks similar while humid convection regimes characterize weak spells (M Rajeevan, 2010 ), the anomalous daily rainfall is a major criterion to find the break and active phases during MSWM seasons. Sub-seasonal fluctuations in monsoon rainfall occur across the Indian-Indochina subcontinent resulting from the position and severity of the monsoon trough as well as the internal dynamics of the MSWM circulation (Sein et al., 2022 ). Ramamurthy, ( 1969 ); P. J. S Gadgil, ( 2003 ); TN Rao, ( 2009 ) have shown that these spells are not uniform over core monsoon zone and other rest of regions. Thus, Narayana Rao et al., ( 2016 ) applied Rajeevan et al., (2010) technique to identify active/break spells for small homogeneous regions to study climatological characteristics and their vertical structure during active and break spells. Thus, we also applied the three consecutive day’s rainfall anomalies over the monsoon core regions to define break spells and active spells. 3 Result and Discussion The average rainfall across all of mainland Indochina is used as the basis for several studies on monsoon breaks. The current study employs the CPC normalized daily gridded rainfall anomaly data to identify break spells and active spells (Hersbach et al., 2020b ). However, the analysis by V. Krishnamurthy & Shukla, ( 2000 ) showed that the dominant mode in the daily rainfall has anomalies by the regions. Thus, the intra-seasonal variations are not equal distributed throughout the regions and the average for the whole study area is not representative of different sub-regions. Similarly, all-Myanmar rainfall are not also coherent over the whole region (Lwin, 2000 ). The monsoon season's rainfall anomaly pattern's (JJAS) most noticeable characteristic is the significantly lower rainfall total over east and central Myanmar's plains. High amount of average daily rainfall occur over northern hill and over western and southern coastal regions of mainland Indochina (Fig. 1 . a ). This rainfall pattern is alike to the dominant intra-seasonal mode of V. Krishnamurthy & Shukla, ( 2000 ). In identifying break and active events, we adopt criteria that are generated from the rain fluctuations between break spells and active spells over the monsoon core region: west (W) and south (S). Here the northern parts (N) are ignored even though it has excess rainfall due because the rainfall variation of this region is influenced by not only monsoon wind but also western disturbances from middle Asia (Oo, 2022 ). The core regions are generally defined as W (16◦N to 23.0◦N, and 91.0◦E to 96.0◦E) and S (11◦N to 19.0◦N, and 96.0◦E to 99.0◦E) (Fig. 1 . a ). The average rainfall anomaly of these two regions is consider. The tropical convergence zone (TCZ) establishes itself over the monsoon core zone at the end of the MSWM's onset phase. During PMIM, the TCZ fluctuates primarily in this region. The monsoon begins to leave the northern portion of this zone by early September (Oo, 2023 ). The term "break" is typically used to describe dry periods (fewer rainfall days) that start after the monsoon's core has formed. The onset and withdrawal stages of the MSWM are examples of seasonal transitions that should involve changes in some systems related to varying rainfall intensity. Hence, only the PMIM, have been taken into account in order to identify break spells and active spells. While considering this zone, care was taken not to include the east lee-ward side of mountain range, where substantial amount of rainfall is received during the monsoon breaks. This core monsoon zone is quite similar to the geographical region taken into consideration by (A Kulkarni, 2009 ; N Singh, 2010 ; Rajeevan et al., 2010b ; Ramamurthy, 1969 ; P. J. S Gadgil, 2003 b; TN Rao, 2009 ) for identifying both spells. Following by the methods of Prathipati et al., ( 2021b ) and Rajeevan et al., (2010), there is a total of 142 events of rainfall, 74 active and 67 break spells are identified over study region during 1981–2020 (Fig. 3 ). A dynamic spatial correlation between the mean rainfall (1981–2020) during July–August and lower atmospheric zonal wind (850 hPa level) are also exhibited in Fig. 4 . a and b . The southwest flow wind (Fig. 4 . a ) and non-homogeneous rainfall zone (Fig. 4 . b ) was significantly found in monsoon seasonal dynamic pattern. This show the variability of monsoon wind influence to the rainfall positively over monsoon core region and negatively over central dry zone of Myanmar, and southeastern Mainland Indochina region. 3.1 Statistical Features of Break spells and Active Spells This section evaluates the several statistical properties of both spell between 1981 and 2020. It also examined for the daily, intra-seasonal, inter-annual and inter-decadal scales and compared to examine climate shift if any in the all spells events. Table − 1 - A statistical properties of MSWM monsoon days. The study's identification of break spells and active spells for the years 1981 to 2020 is presented in the appendix, respectively. As seen in the appendix tables, for 40 years (1990, 1991, 1992, 1994, 1997, 2006, 2017 and 2018) there were no significant break events. Similarly, for 5 years (1983, 1985, 1988, 1989, and 2012) there were no significant days of active spells. During the total period, there were 67 break spells (total 291days) of duration varying from 3 to 12 days (37 spells (168 days) in July, 30 spells (123 days) in August). Similarly, there were 75 active spells (total 297 days) of duration varying from 3 to 12 days (44 spells (171 days) in July, 31 spells (126 days) in August) Table − 1 . The average break spells duration was 6.7 days (3.7 days in July, 3.0 days in August) and the active spells was long 8.6 days (4.3 days in July, 3.1 days in August) during same study period. The lengthiest break spell (duration of 12 days) was occurred in 1996 and 2016. The lengthiest break spell for seasonal (total two-months) (duration of 18 days) was occurred in 1998 and 2016. In the same period, the lengthiest active spell (duration of 12 days) was occurred in 1994. And for seasonal, 24 days duration of active spell was occurred in 1994. To investigate the spatial spread of rainfall anomalies with low level monsoon wind (850hPa) related to the both spell over mainland Indochina, composite daily rainfall maps of the 291 break days and 297 active days were examined (Fig. 5 . a & b ). As seen in the Fig. 5 . a & b , in terms of both break and active spells was discovered that the composite daily rainfall anomaly patterns were completely similar each other. During the break days, the rainfall along the west and south coast and over most areas of monsoon core zone were less with weak southwest monsoon wind, which the far northern along the Himalayas foothill were exceed are found clearly. The reverse pattern are exhibited during active phases. This pattern confirm with the previous monsoon rainfall studies (Oo, 2022 ; Sein et al., 2015 ). The Fig. 6 . a-c show the frequency histograms of the duration of both spell during the periods 1981–2020, 1981–2000 and 2001–2020. During both 20 years the halves (1981–2000 and 2001–2020) almost equal number of break spells (32 and 35) occurred. However, there was rise of about 7% for the active spells (total 40 spells) during 2000–2020 contrast to that during first half (1981–2000) (total 30 spells). Furthermore, it can be noted that the two periods, the short (3–6 days) and intermediate (6–9 days) spell durations made up the increase in the frequency of active spells (35 to 40) during the second half. In the long duration (≥ 9 days) side, the number of spells in use substantially dropped to 0 between the first and second halves. As opposed to that, the frequency of break spells of intermediate and long lengths occurred about equally in both halves of the study period. However, in the short spells (3–6 days) side, the frequency of break spells showed significant decrease to 6 spells from first to second half. The bar plot of both break spells and active spells durations for the entire period of 1981–2020 is displayed in Fig. 6 . c . This graph shows that short-duration both spells were more common than long-duration spells, with roughly 69 percent of break spells and 76 percent of active spells spanning the duration of 3 to 6 days. From 2000 to 2020, there were no spells active with a duration of less than 13 days (Fig. 6 . c ). While only 3 percent of the break times were longer than 13 days. This finding suggest that the exceeding of monsoon rainfall which similar with previous projection studies (Oo et al., 2023 ). 3.1.1 Lagged Rainfall and Monsoon wind The purpose of this section is to discuss the evolution of composite breaks and active phases during MSWM. A lagged composite simulation of the daily rainfall anomaly for lags ranging from − 12 to + 12 days (17-June-1985 to 8-July-1985) is used to explain the modifications to both spells respectively. The halfway point of the break/active time is referred to as Lag.0. There are some interesting characteristics in the modifications to the break spells. Nine days before the break spell (at Lag − 9 and − 6), a large part of the study area has positive rainfall anomalies whereas there is a belt just to the south of about 22°N with positive anomalies and the southern coast also has positive anomalies of rainfall Fig. 7 . In the next ten days (Lag.0), this north-south band of positive anomalies is sudden turn to negative anomalies. Around a week from Lag-3 to Lag + 3, at lag.0 the pattern found significantly the break phase with negative anomalies over the main monsoon core regions and positive anomalies over the northern mountain range regions as well as some eastern part where are associated with the monsoon trough migrating. By lag + 6 days, the region of positive anomalies reinforces over monsoon core west and southern coastal regions of eastern BoB region and when lag + 9, negative anomalies occur over most of the northern region. In contrast, nine days before the active (at Lag − 9 days), negative rainfall anomalies appear over the western and southern coastal part of the monsoon zone (Fig. 8 ). The positive monsoon wind which increases and slowly expand northeastwards. The whole monsoon zone has negative rainfall anomalies at Lag − 3 while the distant north, northeastern, and southeastern regions have positive anomalies. The same pattern (albeit with more intense negative anomalies) characterizes the break at Lag 0. From Lag + 3 days, negative anomalies over the peninsula spread northward and westward and subsequently cover the monsoon zone and the west coast by Lag + 6 and + 9. At Lag + 12, negative anomalies are restricted to the Northern Mountain ranges, while large positive anomalies are observed along the west and south coast. This characteristic may also be noticed in the development of V. Krishnamurthy & Shukla, ( 2008 )'s breaks and active spells explanation. The rainfall anomaly composite over the MSWM region for breaks discussed in this study is comparable to that of Lwin, ( 2000 ); Ramamurthy, ( 1969 ). While the present study used higher resolution (0.25° × 0.25°) data, while Ramamurthy, ( 1969 ) used rainfall data of meteorological subdivisions. The composite of rainfall for break spells is almost a reverse pattern of the active composite. The rainfall anomalies are shown to be uniform over the central monsoon zone and along the western and southern coast during breaks and active spells. However, the positive and negative anomalies are significantly seen over western Myanmar and, southern peninsula, vice versa for both phases. 3.1.2 Decadal Variation Decades wise, variation in break days observed during 1981–1990 and 1991–2000 showed maximum break days (81 days) and minimum break days (66 days). Table − 2.a & b show the decadal distribution for the number of occurrence of break & active days respectively during the period 1981–2020. The progression of events throughout the course of the two months (July-August) is shown by dividing July and August months into 3 equal periods by 10 days (hereafter called as D10) each (11days for last period of each month). Table − 2 Decade wise 10 day period statistics for (a) Break and (b) Active days (1981–2020). In the Table − 2.a , the decadal distribution of break days shows that during each of the months, first D10 is most prone for actives. However, when both the months taken together, the first D10 of July followed by last D10 of August recorded highest occurrence of break days (21% & 24% respectively of the season). The decadal variation explained that the peak occurrence of break days during the two-first decades (1981–1990 & 1991–2000) at the first D10 of July. However, during the last 2 decades (2001–2010 & 2011–2020), the supreme number of break day found on the last D10 of the August. It's also noteworthy that there was no even one break day during the first D10 of August throughout the last ten years (2011–2020). Decades wise, variation in break days observed during 1981–1990 and 1991–2000 showed maximum break days (81 days) and minimum break days (66 days). In case of active events ( Table − 2.b ), 55% of the total 297 active days was in July with an average of 14 days per season and 45% was in August with an average of 11 days per season. The decadal variation shows that during 4 decades (1991–2000, 1941-50, 1961-70 & 2011–2020), highest number of active days was observed in the last D10 of July and the first D10 of August. The monthly variance of active days by decade reveals that throughout the course of four decades, July had a higher number of monsoon active days than August. In terms of decadal variation, 1991–2000 had the maximum peak number of active days (27 days), whereas 2001–2020 and 2011–2020 had the minimum number (3 days). 3.2 The different of atmospheric parameter Composite atmospheric parameter maps of the 291 break days and 297 active days were studied to find at the anomalous geographical pattern of rainfall over Myanmar associated with breaks and active events. In this section, we have mainly discussed the physical parameter of three atmospheric variables that the break, active and different values between these two spells by using ERA5 along with the characteristics of the Intra-seasonal variability (break, active spells). We also looked at the various rainfall categories based on threshold frequencies over Myanmar in order to better understand the variability of rainfall. 3.2.1 Sea Level Pressure (SLP) In this part, we've examined the composite average SLP patterns of break spells and active spells as well as their variations over mainland Indochina and the surrounding area (Fig. 9 ). In the break conditions, weakening of heat lows and the monsoon trough, as well as the disappearance of the offshore trough, are seen (Fig. 9 . c ). These changes have a direct impact overall monsoon circulation, particularly the low-level jet The heat low that stretches from Pakistan to the coast of Myanmar and passes across north India during active periods (Fig. 9 . d ). The pattern indicates that the intensity of the low-pressure area is less intense during these spells (below 1000 hPa). Both the break period and active period of the monsoon can be seen the trough is lie over north and middle India (Fig. 9 . a ). The western coastal area of Myanmar has significant rainfall occurrences because of these troughs. The monsoon circulation produces more rain than usual across mainland Indochina, especially over the core monsoon area, and low-pressure troughs, depressions of BoB and the meridional pressure gradient changes between the north Indochina and South Ocean modulate it. In addition, there is a strengthening of low-pressure areas over northern Myanmar, which intensifies the flow of southwestern winds from the BoB to the mainland Indochina region. This heat low, which stretches from Pakistan to Myanmar, weakens and becomes discontinuous over middle inland of India during breaks. It then moves into the Himalayas foothills region, bringing more rain to northern foothills region and dry central Myanmar during breaks than it does during active spells. The pressure contrasts between the break and active spells show that the monsoon trough, which has a reach in the northwest India to northern central Myanmar across the head of the BoB, is also called the heat low (BoB). The pressure differences clearly show an increase in the strength of the seasonal monsoon trough over the upper northern area of BoB is greater intensity in the break phase compared to active (-3 to -5.5 hPa) (Fig. 9 . b ). Further, the pressure differences are positive over far southern India and the southeastern Indochina peninsula. It clearly indicates that the analysis exhibits a good skill in reproducing the observed variations of pressure distributions during break spells and active spells. 3.3 Dynamic Anomalies 3.3.1 Lower tropospheric (850hPa) winds The two low-level jets are crucial in regulating rainfall variability because they carry moisture with two dominating cores from the southern Bay of Bengal (BoB) to the mainland Indochina and northern Myanmar. We have investigated the variations of mean wind direction patterns for the two spells at 850 hPa (approximately at 1.5 km from surface to ground) (Fig. 10 ). The significant two dominating Monsoon Low Level Jets (MLLJ), Somali Jet (SMLJ) with a peak (15–30 m/s) and Bay of Bengal Jet (BOBJ) with a peak (12–25 m/s) during both spells are exhibited in Fig. 10 . a . The weak (strong) magnitude of MLLJ during break (active) over each sea area provides a clear indication about the transportation of moisture from the BoB to mainland Indochina by directly enhancing (declining) precipitation over Myanmar. The spatial pattern and the strength of southwesterly MSWM wind over the Arabian Sea (ARBS) and BoB are in good agreement with climatology MSWM season average during 1981–2020 (Chakraborty & Agrawal, 2017 ; Viswanadhapalli et al., 2019 ). In active spells, spatial distribution of low-level 850 hPa wind, from SMLJ with peak (15–30 m/s) extended and enforce into BOBJ core (over the domain of 5°N-20°N, 85°E-100°E) elongated in a southwest direction with core speed around 10 to 25 m/s with slight latitudinal/northward extent up to 20°N during active phase (Fig. 10 . b) . However, similar pattern with weak intensity are found during break phases especially on BoB. Additionally, in active phases, the wind flow shows moderately strong south westerlies over both seas till to South China Sea. Moreover, the south-westerlies at lower level are replaced by westerly wind flow resulting in the Ekman transport (increase in wind speed and height) influence (Prathipati et al., 2021a ). 3.3.2 Mid tropospheric (500hPa) winds The discrepancies between the two spells' 500 hPa mean wind patterns are exhibited at (Fig. 11 ). The mid tropospheric circulation present a broad anomalous anti-cyclonic circulation over the Middle East regions and veering along the adjoining two sea areas in the both break spells and active spells (Fig. 11 ). But during break phases, the African easterly jet's accompanying wind speeds suddenly increase (AEJ) between 30°-50°E, resulting in no cyclonic activity, i.e., weak convective activity over Myanmar, and driving all northesast winds towards East Africa. During break spells, weak in both west and easterly winds over mainland Indochina, and there is no strengthened wind associated with levar-channeled flow over BoB (Fig. 11 . a ). Apart from the winds associated with MSWM, one of the interesting features noticed in active spells about the cyclonic circulation over northeastern Indian and veering along the adjoining sea areas is signifying found (Fig. 11 . b ). When monsoon troughs or lows are found at the middle tropospheric level, there is an active monsoon circulation interwoven with cyclonic circulation above the north head of BoB. Moreover, the wind pattern differential over that area is in agreement with the above analysis. Thus, it may be one of the synoptic features influencing the unprecedented rainfall over the seasonal monsoon trough area during active spells. A cyclonic veering with the maximum wind above the eastern parts of that trough (shown in Fig. 9 ) plays a significant role during the monsoon active phase, according to changes in mid-tropospheric wind between the both spells. 3.3.3 Upper tropospheric (200hPa) winds Moreover, we examine the differences between break spells and active spells by the composite mean of upper atmospheric (200hPa layer) wind pattern (Fig. 12 ). The Tropical Easterly Jet (TEJ), which has a maximum strength of more than 25 m/s and is located between 15°N and 5°S is the dominant wind flow at 200 hPa upper troposphere. It plays major role in both spells especially over East Indian Ocean (EIO) region (Fig. 12 ). Apart from this, the anti-cyclonic circulation over northern Indian continent also call the Asian monsoon anticyclone (AMA) by Amemiya & Sato, ( 2020 ) takes some important role that the longitudinal movement's variability of the anticyclone center and its longitudinal dimension is significantly varied in both spells. In break spell the significant jet flow is only extend form 40°E to 98°E and lay between 25°N to 30°N (Fig. 12 . a ). Although, the core dimension of AMA is notably shift easterly to northeasterly during active phase of MSWM (Fig. 12 . b ). While the AMA core position also changes over northern India continent, the core of TEJ centered also change over mainland Indochina, especially upper Myanmar (20°-25°N, 90°-110°E) at 200hPa wind. This prominent feature is well indicating the shifting of easterly jet towards the south from its mean position in active phases and the intrusion of subtropical westerlies into lower latitudes during the break phases. Analyzing vertical windshear (the differential of 200 hPa to 850 hPa) provides valuable insights into monsoon behavior, including onset, intensity, and breaks (Chen et al., 2015 ; Shun & Chan, 2008 ). The significant convective shear cells are found over Arabian sea and BOB during both spells. However, there was weak shear cell are divided into two cells over far north and south edge of BOB during break phases (Fig. 13 . a) . The latitude–vertical cross sections of 850–hPa to 200hPa two component winds (include both the horizontal and vertical wind components) average over 90° E–105° E during 1981–2020 for both break and active phases with their 40 years average are displayed (Fig. 13 . c,d and e) . Black dash lines show depth of tongues, red-solid lines show the width of easterly wind core and red-dash lines show location of peak level of MSWM wind (Fig. 13 . c,d and e) . During break, the core of easterly wind gradient is significantly weak than active and separation into two cores other than the climatology and active phase as mention above. Moreover, the depth and vertical top level of monsoon wind over monsoon core region of Myanmar (10°-20°N) also vary with each phase. These features are well simulated in Fig. 13 . Therefore, the weaker and deviated pattern from mean TEJ during the break are noted and substantially greater TEJ/MLLJ over upper/lower troposphere are discovered in the active spells. Base on the above analysis, the inter-annual statistical relation value of lower, mid and upper tropospheric wind, rainfall with break spells and active spells are performed by Pearson correlation coefficient analysis and results are explained in Table − 3 . During break days, the strong or moderate negative correlation results are found that the break spells with 850hPa Core (average 850hPa wind over 90°-105°E and 5°-20°N), 500hPa Core (average 500hPa wind over 90°-105°E and 5°-20°N), Rainfall(all) (rainfall average over 90°-105°E and 10°-30°N), Rainfall Core (rainfall average over 91°-99°E and 11°-23°N) and BOB Wind (850hPa wind average over 90°-100°E and 10°-15°N). However positive for 200hPa Core (average 200hPa wind over 115°-135°E and 0°-10°N). For active spells, apart from 200hPa Core, other parameter shows a positive correlation. Both results significantly level of 95–99% by the two-tailed test of significances test. This explained the role of both spells with circulation of upper and lower atmospheric wind, over the rainfall variation of study region. It also found that the anomalous tropical easterly jet wind is a key player of intraseasonal monsoon intensity variation. 3.4 Thermodynamic Anomalies To analyze the variations in the pattern of MSWM rainfall between both break and active phases (resulting in the convective patterns), composite maps of the OLR anomalies are shown for break periods and active periods in Fig. 14 . a & b . The average of both break and active period composites OLR anomalies patterns are shown significantly negative and positive values during break and active period respectively. The break spell is characterized by negative OLR anomalies from far north Myanmar and southern China to the west Pacific and the eastern part of the equatorial Indian Ocean, as well as significant positive anomalies over the main monsoon core zone, the equatorial western Pacific, and the Pacific central. Thus, over 70–130°E, the quadrupole pattern described by (Annamalai & Slingo, 2001 ) is seen. Negative OLR anomalies are present in the active composite over the central and western equatorial Pacific, additionally the primary monsoon core zone. Positive anomalies are present over the eastern portion of the equatorial region of Indian Ocean. The highest variation in OLR anomalies between two events was seen over across the BoB to South China Sea (negatively) and the equatorial eastern Indian Ocean (positively). The weak positive values also result over far northern Myanmar and southern China. The study area is a critical area, which is clearly seen as physically linked to both break and active cycle of the MSWM with the ITCZ pattern and it has been a well-study area to investigate the ITCZ variation. 3.4.1 Relationship with Tropical SSTs Over Pacific & Indian Oceans Sea surface temperature (SST) is important tools for monitoring and analyzing climate variability and change (Zhang et al., 2011 ). And previous studies already prove that SST are the best climate predictor for monsoon system (Ding & Wang, 2005 ; G. Huang et al., 2010 ; Webster et al., 1998 ). The spatial composite SST anomalies explained similar pattern by positive/negative patterns of equatorial Pacific regions (Fig. 14 . c and d ). Warming (cooling) SST anomalies was observed over equatorial Pacific during active (break) phases of MSWM. To further examine the association of SST on the both break and active events, we examined the frequency distribution of spells over study area associated with both phases of El Niño Southern Oscillation (ENSO) Index. During the period 1981–2020 there were 9 El Niño years and 11 La Niña years by the NOAA's primary index named the Oceanic Niño Index (ONI) (B. Huang et al., 2017 ). The histograms of the both spells of various time spans during these SST anomaly years show homogeneous distribution with spells for long time span (Fig. 15 ). Long-lasting break spells do occur more frequently in La Niña phases than to El Niño. Trough the El Niño (La Niña) phases, there were total 54 (120) break days and 96 (54) active days with an average 6 (11) break days and 11 (5) active days per season respectively. This demonstrates unequivocally that La Niña supports more break days than active days, as the mean frequency of occurrence of break days during La Niña is higher than El Niño time as mention above. Similarly, the reverse pattern is found for active days that more active days in El Niño than La Niña years otherwise El Niño favors days that are more active. For future approach, we also find the best dominant index to predict the spell events that excess or less spell year for both event. Table − 4 show the significant CC values of well know climate indices with break and active days during 1981–2020, such as Indian Monsoon Index (IMI), South Asian Monsoon Index (SAMI), Western North Pacific Monsoon Index (WNPMI), El Niño Modoki Index (EMI), Tropical Pacific SST (Nino3-4), Southern Oscillation Index (SOI), Dipole Mode Index (DMI), Tropical Northern Atlantic Index (TNA), North Atlantic Oscillation (NAO), North Pacific pattern (NP) by previous studies. All values are analysis by Pearson technique and considering only 95–99% significant level by two-tail test. As the results most are highly correlated with not only global climate system but also weather of small regions. Thus, we performed the test and result show that moderate to slight strong negative correlation values are found DMI, Nino-3.4, EMI and WNPMI with positive correlation with SOI TNA and NP during break days. While SOI show slight strong negative correlation during active days, Nino3-4, EMI and DMI show moderate correlation values. Table − 4- Correlation coefficient values for break and active days with climate indices 4 Summary and Conclusions Our study assesses the statistical variability of the break and active spells by the data capturing of the synoptic features of MSWM. The semi-permanent features of ITCZ and weak gradient trough along the offshore of the western coastal of Myanmar during Break spells, and northward migration of ITCZ as well as induction of the meridional pressure gradient in active spells are well simulated in study. The temporal and spatial distribution of rainfall intensity is also investigated for both break and active days, using the average daily-normalized rainfall over the monsoon core region, which is consistent with intra-seasonal variance. The strong correlation values between the summer (JJAS) rainfall over this core zone and the monsoon rain suggests that this area is crucial for both intra-seasonal and inter-annual monsoon variation. As a result, 68% of the break events with 76% of the active events are the short duration event (about 3–6 days). Only a small portion, 3% of break events and 1% of active spells are lasted around a week or more. There are typically 7 days in both spell of July and August. The number of break and active days correlates strongly with monsoon rain. No discernible trends in the days of break events or active events throughout the MSWM season can be seen in the break and active days time series analysis. However, there is slight increasing trend for active events. Normal monsoon trough that inter tropical convergence zone extending from Indian region to Myanmar Monsoon core region during the active monsoon conditions indicate the variation of the anomalous convective rainfall and low-level circulation over Asia-Pacific region. Life period of these synoptic scale systems is also of 3–6 days. While the break circumstances are brought on weak gradient of the monsoon trough laying from middle India to the far north-western BoB, and it result that the large-scale subsidence over the central dry zone region by a strong rising motion over convective areas nearby. By immediately increasing (declining) rainfall over Myanmar, the strong (weak) amplitude of MLLJ while active (break) across each sea gives a clear indicator regarding the transit of moisture distribution from the BoB to Indochina mainland. The weakened (intensified) mid-tropospheric wind circulation linked to MLLJ is accurately approximated during break (active) phases. A notable and important aspect of active periods is the occurrence of anomaly cyclonic circulation over northeastern India and bending along the adjacent sea areas, as well as anomaly anti-cyclonic circulation over the northwestern Arabian Peninsula regions and bending along the adjoining two sea areas. The shifting of easterly jet towards the south from its mean position in active spells and the intrusion of subtropical westerlies into lower latitudes in the break period. During break spells, TEJ/MLLJ will be weaker and dislocated over upper/lower troposphere, but during active spells, they will be substantially stronger. We explained that during break/active spells, spatial patterns significantly deviate from those of inter-annual variability, particularly those linked to the majority of meteorological variables. A moderate to slight negative correlation was found with well-known climate indicies, such as: DMI, Nino-3.4, EMI, and WNPMI during break days, while a positive correlation was observed with SOI TNA and NP during break days. On intra-seasonal periods, the signal over the eastern portion of the equatorial Pacific Ocean is comparable to that on inter-annual time scales. The study clearly showed that La Niña encourages more break days than active days because the occurrence of break days is increasing during La Niña than El Niño phases. The study is the first to demonstrate the distinction in meridional vertical circulation between strong break events with heat trough circulation and moist convective active spells over the studied region. To develop effective forecast methodologies, it is necessary to understand the mechanisms that govern the transitions in time and space from a heat low circulation to a convective moist regime. Future research should extend these findings to encompass the entire MSWM system. Declarations Data Availability Source Data Reanalysis rainfall, component winds, OLR, and Mean Seal Level Pressure netcdf4 data for this study were downloaded from the ECMWF data portal. And this is a fifth-generation ECMWF reanalysis dataset with a geographical resolution of 0.25 0.25 for global climate parameters over the previous decades are used to support the findings of this study are included within the article. Data is now freely available from 1950 to the present by registration at ECMWF. The actual monthly rainfall observation data from 79 observation stations used to support the findings of this study was provided under permission by Myanmar's Department of Meteorology and Hydrology (DMH) and hence cannot be freely distributed. Requests for access to these data should be made to the Director-General of DMH, Myanmar. https://www.moezala.gov.mm/ Software availability Open Grads (OpenGrADS - Home), Climate data operator (https://code.mpimet.mpg.de/ ) and IBM SPSS are mainly used for this study. Among of these first two are opensource application for everyone. Conflicts of Interest I declared that there is no potential conflict of interest with any of the following statements. For any component of the submitted work, the author received no cash or services from a third party (government, commercial, private foundation, etc). (including but not limited to grants, data monitoring board, study design, manuscript preparation, statistical analysis, etc.). The author is not affiliated with any entity that has a direct or indirect financial interest in the manuscript's subject matter. The author was involved in the following aspects of the project: (a) idea and design, or data analysis and interpretation; (b) authoring the article or critically reviewing it for essential intellectual content; and (c) approval of the final version. This work has not been submitted to, and is not currently being reviewed by, any other journal or publishing venue. The author has no patents that are broadly relevant to the work, whether proposed, pending, or issued. The author received no payment or services from a third party for any aspect of the submitted work (government, commercial, private foundation, etc). (including but not limited to grants, data monitoring board, study design, manuscript preparation, statistical analysis, etc.). Funding Statement This research and publishing are being carried out using self-funding. ORCID Kyaw Than Oo https://orcid.org/0000-0003-1727-3462 Author Statement Kyaw Than Oo : Conceptualization, Methodology, Data curation, Writing- Original draft preparation. Visualization, Investigation. Kazora Jonah : Reviewing. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work the author(s) used plagiarism checker and grammerly in order to check the plagiarism and grammer mistakes. After using this tools, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication. Acknowledgment The researcher expresses special thanks to all Professors who approve and support this research and Nanjing University of Information Science for support to come out this research. I would also like to extend my gratitude to Professor Haishan Chen from Nanjing University of Information Science and Technology, for supervisor this paper and his others, support during this research. The author acknowledges heartfelt thanks to the scientists of the ECMFW for supporting ERA5 datasets and the Department of Meteorology and Hydrology for support the data of Myanmar. Additionally, the author would like to thank three reviewers for their constructive and insightful reviews and comments which have significantly helped to improve the manuscript. References Kulkarni A, S. S. R. K (2009) Spatial variability of intra-seasonal oscillations during extreme Indian monsoons. Int J Climatol 29:1945–1955 Amemiya A, Sato K (2020) Characterizing quasi-biweekly variability of the Asian monsoon anticyclone using potential vorticity and large-scale geopotential height field. Atmos Chem Phys 20(22):13857–13876. https://doi.org/10.5194/ACP-20-13857-2020 Annamalai H, Slingo JM (2001) Active/break cycles: Diagnosis of the intraseasonal variability of the Asian Summer Monsoon. Clim Dyn 18(1–2):85–102. https://doi.org/10.1007/s003820100161 Aung LL, Zin EE, Theingi P, Elvera N, Aung PP, Han TT, Oo Y, Skaland RG (2017) Myanmar Climate Report. Norwgian Meterological Inst 9:105 Blanford HF (1886) Rainfall of India. Mem Ind Met Dept 2:217–448 Li C, M. Y (1996) The onset and interannual variability of the Asian Summer Monsoon in relation to land-sea thermal contrast. J Clim 9:358–375. https://doi.org/10.1175/1520-0442(1996)0092.0.co Carlson K, Winquist J (2014) Introduction to statistics and frequency distribution. Introduction Statistics: Act Learn Approach, 1–32 Chakraborty A, Agrawal S (2017) Role of west Asian surface pressure in summer monsoon onset over central India. Environ Res Lett 12(7). https://doi.org/10.1088/1748-9326/AA76CA Chen X, Wang Y, Zhao K (2015) Synoptic flow patterns and large-scale characteristics associated with rapidly intensifying tropical cyclones in the South China Sea. Mon Weather Rev 143(1):64–87. https://doi.org/10.1175/MWR-D-13-00338.1 Sikka DR, G. S (1980) On the maximum cloud zone and the ITCZ over India longitude during the southwest monsoon. Mon Wea Rev 108:1840–1853. https://doi.org/10.1175/1520-0493(1980)1082.0.co Dima IM, Wallace JM (2003) On the seasonality of the Hadley Cell. J Atmos Sci 60(12):1522–1527. https://doi.org/10.1175/1520-0469(2003)0602.0.CO;2 Ding Q, Wang B (2005) Circumglobal teleconnection in the Northern Hemisphere summer. J Clim 18(17):3483–3505. https://doi.org/10.1175/JCLI3473.1 Endo N, Matsumoto J, Lwin T (2009) Trends in precipitation extremes over Southeast Asia. Sci Online Lett Atmos 5(1):168–171. https://doi.org/10.2151/sola.2009-043 Gadgil S (2003) The Indian Monsoon and its variability. Annu Rev Earth Planet Sci 31(1):429–467. https://doi.org/10.1146/annurev.earth.31.100901.141251 Ghosh S, Vittal H, Sharma T, Karmakar S, Kasiviswanathan KS, Dhanesh Y, Sudheer KP, Gunthe SS (2016) Indian Summer Monsoon Rainfall: Implications of Contrasting Trends in the Spatial Variability of Means and Extremes. PLoS ONE 11(7):e0158670. https://doi.org/10.1371/JOURNAL.PONE.0158670 Goswami BN, Ajayamohan RS, Xavier PK, Sengupta D (2003) Clustering of synoptic activity by Indian summer monsoon intraseasonal oscillations. Geophys Res Lett 30(8). https://doi.org/10.1029/2002GL016734 Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A, Soci C, Abdalla S, Abellan X, Balsamo G, Bechtold P, Biavati G, Bidlot J, Bonavita M, Thépaut JN (2020a) The ERA5 global reanalysis. Q J R Meteorol Soc 146(730):1999–2049. https://doi.org/10.1002/QJ.3803 Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A, Soci C, Abdalla S, Abellan X, Balsamo G, Bechtold P, Biavati G, Bidlot J, Bonavita M, Thépaut JN (2020b) The ERA5 global reanalysis. Q J R Meteorol Soc 146(730):1999–2049. https://doi.org/10.1002/QJ.3803 Huang B, Thorne PW, Banzon VF, Boyer T, Chepurin G, Lawrimore JH, Menne MJ, Smith TM, Vose RS, Zhang HM (2017) Extended reconstructed Sea surface temperature, Version 5 (ERSSTv5): Upgrades, validations, and intercomparisons. J Clim 30(20):8179–8205. https://doi.org/10.1175/JCLI-D-16-0836.1 Huang G, Hu K, Xie SP (2010) Strengthening of tropical Indian Ocean teleconnection to the Northwest Pacific since the mid-1970s: an atmospheric GCM study. J Clim 23(19):5294–5304. https://doi.org/10.1175/2010jcli3577.1 Krishnamurthy V, Shukla J (2000) Intraseasonal and interannual variability of rainfall over India. J Clim 13(24):4366–4377. https://doi.org/10.1175/1520-0442(2000)0132.0.CO;2 Krishnamurthy V, Shukla J (2008) Seasonal persistence and propagation of intraseasonal patterns over the Indian summer monsoon region. Clim Dyn 30(4):353–369. https://doi.org/10.1007/s00382-007-0300-7 Lwin T (2000) The Prevailing Synoptic Situations in Myanmar. Rajeevan M, S. G. J. B (2010) Active and break spells of the Indian summer monsoon. J Earth Syst Sci 119(3):229–247 Singh N, A. R (2010) The wet and dry spells across India during 1951–2007. J Hydrometeorol 11:26–45 Narayana Rao T, Saikranthi K, Radhakrishna B, Vijaya B, Rao S (2016) Differences in the Climatological Characteristics of Precipitation between Active and Break Spells of the Indian Summer Monsoon. J Clim 29(21):7797–7814. https://doi.org/10.1175/jcli-d-16-0028.1 Nguyen H, Evans A, Lucas C, Smith I, Timbal B (2013) The hadley circulation in reanalyses: Climatology, variability, and Change. J Clim 26(10):3357–3376. https://doi.org/10.1175/JCLI-D-12-00224.1 Oo KT (2022) Interannual Variability of Winter Rainfall in Upper Myanmar. J Sustain Environ Manage 1(3):344–358. https://doi.org/https://doi.org/10.3126/josem.v1i3.48001 Oo KT (2023) Climatology Definition of the Myanmar Southwest Monsoon (MSwM): Change Point Index (CPI) . 2023 (Fig. 2) Oo KT, Haishan C, Jonah K (2023) Climate Change Impact on the Trigger of Natural Disasters over South-Eastern Himalayas Foothill Region of Myanmar: Extreme Rainfall Analysis. International Journal of Geophysics , 2023 , 2186857. https://doi.org/10.1155/2023/2186857 Oo KT, Oo KT, Oo KT, Dipole IO (2019) How El-Niño / La-Nina & India Ocean Dipole (IDO) Influence on Myanmar Rainfall Pai DS, Sridhar L, Ramesh Kumar MR (2016) Active and break events of Indian summer monsoon during 1901–2014. Clim Dyn 46(11–12):3921–3939. https://doi.org/10.1007/S00382-015-2813-9/METRICS Prathipati VK, Viswanadhapalli Y, Chennu VN, Dasari HP (2021a) Study of Active and Break Spell Phenomena of Indian Summer Monsoon Using WRF Downscaled Data. Pure appl Geophys 178(10):4195–4219. https://doi.org/10.1007/s00024-021-02837-5 Prathipati VK, Viswanadhapalli Y, Chennu VN, Dasari HP (2021b) Study of Active and Break Spell Phenomena of Indian Summer Monsoon Using WRF Downscaled Data. Pure appl Geophys 178(10):4195–4219. https://doi.org/10.1007/S00024-021-02837-5/METRICS Privé NC, Plumb AR (2007) Monsoon dynamics with interactive forcing. Part I: Axisymmetric studies. J Atmos Sci 64(5):1417–1430. https://doi.org/10.1175/JAS3916.1 Krishnan R, C. Z. M. S (2000) Dynamics of breaks in the Indian summer monsoon. J Atmos Sci 57(9):1354–1372. https://doi.org/10.1175/1520-0469(2000)0572.0.co Rajeevan M, Gadgil S, Bhate J (2010a) Active and break spells of the Indian summer monsoon. J Earth Syst Sci 119(3):229–247. https://doi.org/10.1007/s12040-010-0019-4 Rajeevan M, Gadgil S, Bhate J (2010b) Active and break spells of the indian summer monsoon. J Earth Syst Sci 119(3):229–247. https://doi.org/10.1007/S12040-010-0019-4/METRICS Ramamurthy K (1969) Monsoon of India: Some aspects of the ‘break’ in the Indian southwest monsoon during July and August. Forecasting Manual 1–57 IV 18.3, India Met. Dept. Ren YY, Ren GY, Sun XB, Shrestha AB, You QL, Zhan YJ, Rajbhandari R, Zhang PF, Wen KM (2017) Observed changes in surface air temperature and precipitation in the Hindu Kush Himalayan region over the last 100-plus years. Adv Clim Change Res 8(3):148–156. https://doi.org/10.1016/J.ACCRE.2017.08.001 Gadgil S (2003) P. V. J. On breaks of the Indian monsoon. Proc. Indian Acad. Sci. (Earth Planet. Sci.) , 112 , 529–558 Gadgil S, P. J (2003a) On breaks of the Indian monsoon. J Earth Syst Sci 112:529–558 Gadgil S, P. J (2003b) On breaks of the Indian monsoon. Proc Indian Acad Sci (Earth Planet Sci) 112:529–558 Satyanarayana GC, Dodla VBR, Srinivas D (2020) Decreasing southwest monsoon rainfall over Myanmar in the prevailing global warming era. Meteorol Appl 27(1). https://doi.org/10.1002/MET.1816 Schneider T, Bordoni S (2008) Eddy-mediated regime transitions in the seasonal cycle of a hadley circulation and implications for monsoon dynamics. J Atmos Sci 65(3):915–933. https://doi.org/10.1175/2007JAS2415.1 Sein ZMM, Ogwang B, Ongoma V, Ogou FK, Batebana K (2015) Inter-annual variability of May-October rainfall over Myanmar in relation to IOD and ENSO. J Environ Agricultural Sci 4:28–36 Sein ZMM, Ullah I, Saleem F, Zhi X, Syed S, Azam K (2021) Interdecadal variability in myanmar rainfall in the monsoon season (May–october) using eigen methods. Water (Switzerland) 13(5). https://doi.org/10.3390/w13050729 Sein ZMM, Zhi X (2016) Interannual variability of summer monsoon rainfall over Myanmar. Arab J Geosci 9(6). https://doi.org/10.1007/S12517-016-2502-Y Sein ZMM, Zhi X, Ullah I, Azam K, Ngoma H, Saleem F, Xing Y, Iyakaremye V, Syed S, Hina S, Nkunzimana A (2022) Recent variability of sub-seasonal monsoon precipitation and its potential drivers in Myanmar using in-situ observation during 1981–2020. Int J Climatol 42(6):3341–3359. https://doi.org/10.1002/JOC.7419 Shaw TA (2014) On the role of planetary-scale waves in the abrupt seasonal transition of the Northern Hemisphere general circulation. J Atmos Sci 71(5):1724–1746. https://doi.org/10.1175/JAS-D-13-0137.1 Shun CM, Chan PW (2008) Applications of an infrared Doppler lidar in detection of wind shear. J Atmos Ocean Technol 25(5):637–655. https://doi.org/10.1175/2007JTECHA1057.1 Takahashi HG, Yasunari T (2006) A climatological monsoon break in rainfall over Indochina - A singularity in the seasonal march of the Asian summer monsoon. J Clim 19(8):1545–1556. https://doi.org/10.1175/JCLI3724.1 Rao TN, K. U. T. S. D. R (2009) Differences in draft core statistics from the wet spell to dry spell over Gandaki, India (1358N, 7928E). Mon Weather Rev 2009:4293–4306 Trenberth KE, Shea DJ (2005) Relationships between precipitation and surface temperature. Geophys Res Lett 32(14):1–4. https://doi.org/10.1029/2005GL022760 Krishnamurthy V, J. S (2008) Seasonal persistence and propagation of intraseasonal patterns over the Indian summer monsoon region. Clim Dyn 30:353–369 Viswanadhapalli Y, Srinivas CV, Basha G, Dasari HP, Langodan S, Venkat Ratnam M, Hoteit I (2019) A diagnostic study of extreme precipitation over Kerala during August 2018. Atmospheric Sci Lett 20(12):12. https://doi.org/10.1002/asl.941 Wang B, Ding Q, Joseph PV (2009) Objective definition of the Indian summer monsoon onset. J Clim 22(12):3303–3316. https://doi.org/10.1175/2008JCLI2675.1 Webster PJ, Magaña VO, Palmer TN, Shukla J, Tomas RA, Yanai M, Yasunari T (1998) Monsoons: processes, predictability, and the prospects for prediction. J Geophys Research: Oceans 103(C7):14451–14510. https://doi.org/10.1029/97jc02719 Webster PJ, Yang S (1992) Monsoon and Enso: Selectively Interactive Systems. Q J R Meteorol Soc 118(507):877–926. https://doi.org/10.1002/qj.49711850705 Zaw Z, Fan ZX, Bräuning A, Liu W, Gaire NP, Than KZ, Panthi S (2021) Monsoon precipitation variations in Myanmar since AD 1770: linkage to tropical ocean-atmospheric circulations. Clim Dyn 56(9–10):3337–3352. https://doi.org/10.1007/s00382-021-05645-8 Zhang X, Alexander L, Hegerl GC, Jones P, Tank AK, Peterson TC, Trewin B, Zwiers FW (2011) Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdiscip Rev Clim Change 2(6):851–870. https://doi.org/10.1002/wcc.147 Zin EE, Aung LL, Zin EE, Theingi P, Elvera N, Aung PP, Han TT, Oo Y, Skaland RG (2017) Myanmar Climate Report. Norwgian Meterological Institute , 9 , 105. http://files/679/MyanmarClimateReportFINAL11Oct2017.pdf Additional Declarations No competing interests reported. Supplementary Files Supplement.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4936295","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":343507318,"identity":"99d1aa79-eac6-4eb9-9076-e3b1c09be69a","order_by":0,"name":"KYAW THAN OO","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYBACCRDB2AAi2Q8YfABSfAwMzIS1HARr4UkonAEUYCNBC4PBZx5itEjOSH4m/XGHXR4/e0PiZpuKw3Vs7M2HDRhqbKJxaZGWSDOTOHgmuViy5+Bh45wzhyXYeI4lJzAcS8ttwKFFTiLB7MbBNubEDTcS0oxz24BaJHKMDzA2HMajJf0bUEs9SIv5b0titEhL5IBsOQzSYmDMCNWSgE+LZM+b8h9n244nzuw5k2DYcyZdsg3oF4MEPH6ROJ6+2aCyrTqxn739gMGPCmt+fmCISXyoscGphUEgAZsoVkEY4D+AT3YUjIJRMApGARAAAEqsXTUWl+9mAAAAAElFTkSuQmCC","orcid":"","institution":"Nanjing University of Information Science \u0026 Technology","correspondingAuthor":true,"prefix":"","firstName":"KYAW","middleName":"THAN","lastName":"OO","suffix":""},{"id":343507319,"identity":"e96f884b-e1ad-467a-af81-38e2906476dc","order_by":1,"name":"Kazora Jonah","email":"","orcid":"","institution":"Nanjing University of Information Science \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Kazora","middleName":"","lastName":"Jonah","suffix":""}],"badges":[],"createdAt":"2024-08-19 06:30:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4936295/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4936295/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63137410,"identity":"d8dfb649-ec3d-46f8-9a3a-8358d63614b3","added_by":"auto","created_at":"2024-08-23 14:42:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":737369,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Climatology annual accumulated rainfall over Myanmar, (b) the annual pentad daily rainfall over MSWM region (between 10°-30°N and 90°-105°E), and (c) Seasonal latitudinal shifting of longitudinal area average (90°E-105°E) MSWM daily rainfall (mm day−1) (day of the year) during 1981-2020 (red line denoted mean onset date of MSWM). (Oo, 2023)\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4936295/v1/1c802cae7c34fd68229d66a7.png"},{"id":63139557,"identity":"b2666aef-00cc-4e70-9a82-7985e4b06bba","added_by":"auto","created_at":"2024-08-23 15:06:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":831196,"visible":true,"origin":"","legend":"\u003cp\u003eClimatological (1981-2020) composites centered on onset date, averaged over 90-105◦E: (a) zonal (200 hPa) wind (m s−1), (b) meridional (200 hPa) wind (m s−1), (c) zonal (850 hPa) wind (m s−1), and (d) meridional (850 hPa) wind (m s−1).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4936295/v1/b53ce083e2c4695dd42d142f.png"},{"id":63137974,"identity":"0adfecd5-9f4f-4c80-8f02-32b383ac6a1a","added_by":"auto","created_at":"2024-08-23 14:50:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":169914,"visible":true,"origin":"","legend":"\u003cp\u003e(a) The record of the monsoon break and active events during 1981–2020. The green and orange curve lines denote, respectively, the MSWM onset and withdrawal dates for each year. And (b)their anomalies time series.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4936295/v1/5b94e6519fbb1b12ab27a852.png"},{"id":63138761,"identity":"eb49cbc3-c9fa-4947-aeaa-432352d23c54","added_by":"auto","created_at":"2024-08-23 14:58:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":658129,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Correlation composite map of (a) MSWM regional rainfall with 850 hPa wind at each grid and, (b) average 850 hPa wind over the study region with rainfall at each grid. The contour lines are regression values of each. White dotted indicate a 95% confidence level based on a Student’s t-test. Rectangles denote the study region of rainfall and 850 hPa zonal wind, respectively.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4936295/v1/77cf60e7fb6895551d87abdb.png"},{"id":63137412,"identity":"60e565da-79c5-4513-9e3a-58034d49956f","added_by":"auto","created_at":"2024-08-23 14:42:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":359722,"visible":true,"origin":"","legend":"\u003cp\u003eClimatology anomalies of July and August daily rainfall (mm, shaded) and\u0026nbsp; 850 hPa wind (ms\u003csup\u003e-1\u003c/sup\u003e, vector) for (a) Break period and, (b) Active period during 1981-2020.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4936295/v1/483addc9f5d68ad5263ef945.png"},{"id":63137973,"identity":"27cd1c8c-f212-4235-a58d-953b62127b1c","added_by":"auto","created_at":"2024-08-23 14:50:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":90478,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency distribution of duration of each break spells and active spells for the periods (a) 1981-2000, (b) 2001-2020 \u0026amp; (c) 1981-2020.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4936295/v1/434062991c46b7de395e346d.png"},{"id":63137420,"identity":"15950277-43eb-4386-87dd-2429c4ee6424","added_by":"auto","created_at":"2024-08-23 14:42:30","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1325018,"visible":true,"origin":"","legend":"\u003cp\u003eLagged composite daily rainfall (mm) of the break spells during 1981–2020.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4936295/v1/d7421698b67273ddde38a7b3.png"},{"id":63137426,"identity":"610f2064-78ac-46ec-9cd3-80728b191ce4","added_by":"auto","created_at":"2024-08-23 14:42:32","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1377014,"visible":true,"origin":"","legend":"\u003cp\u003eLagged composite daily rainfall (mm) of the active spells during 1981–2020\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-4936295/v1/1b414087ea17ba449e025b35.png"},{"id":63137416,"identity":"80c463aa-eca5-4c0c-b209-a52b8c4ae61c","added_by":"auto","created_at":"2024-08-23 14:42:29","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2089624,"visible":true,"origin":"","legend":"\u003cp\u003eThe composite map of the SLP [hPa] \u003cstrong\u003e(a)\u003c/strong\u003eClimatology mean, \u0026nbsp;\u003cstrong\u003e(b)\u003c/strong\u003e Differences for active spells and break spells, with \u003cstrong\u003e(c)\u003c/strong\u003e Break spells and \u003cstrong\u003e(d)\u003c/strong\u003eActive spells. Black dotted explained 95% siginifcant by two tail test.\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-4936295/v1/fae371d2391a5cf9c5500a7a.png"},{"id":63137422,"identity":"0b51f5a8-da30-409e-9ffb-8cdd467c904d","added_by":"auto","created_at":"2024-08-23 14:42:31","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":603595,"visible":true,"origin":"","legend":"\u003cp\u003eThe composite map of the 850hPa winds [dircection=vector, strength=shaded, units: m s-1] \u003cstrong\u003e(a)\u003c/strong\u003e the Break spells and \u003cstrong\u003e(b)\u003c/strong\u003e the Active spells during 1981-2020. Red dotted explained 95% siginifcant by two tail test\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-4936295/v1/7d3bc8aafa9e29e3aebc2c9b.png"},{"id":63137424,"identity":"2458f7ab-dfa7-4b87-a9e9-a00f39dacda3","added_by":"auto","created_at":"2024-08-23 14:42:32","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":552713,"visible":true,"origin":"","legend":"\u003cp\u003eThe composite map of the 500hPa winds [dircection=vector, strength=shaded, units: m s-1] \u003cstrong\u003e(a)\u003c/strong\u003e the Break spells and \u003cstrong\u003e(b)\u003c/strong\u003e the Active spells. (High pressure area (H) and Low Pressure area (L) are denoted. Red dotted explained 95% siginifcant by two tail test.\u003c/p\u003e","description":"","filename":"image13.png","url":"https://assets-eu.researchsquare.com/files/rs-4936295/v1/b5ca8abafba3931974f25781.png"},{"id":63137978,"identity":"8c7fb1af-c859-4d80-939d-5a32700bb6ce","added_by":"auto","created_at":"2024-08-23 14:50:31","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":609384,"visible":true,"origin":"","legend":"\u003cp\u003eThe composite map of the 200hPa winds [dircection=vector, strength=shaded, units: m s-1] \u003cstrong\u003e(a)\u003c/strong\u003e the Break spells and \u003cstrong\u003e(b)\u003c/strong\u003e the Active spells during 1981-2020. White dotted explained 95% siginifcant by two tail test.\u003c/p\u003e","description":"","filename":"image14.png","url":"https://assets-eu.researchsquare.com/files/rs-4936295/v1/5cb7e54f193eafb33e8f5f78.png"},{"id":63138763,"identity":"2e3d4166-32b3-4716-8597-118245df5d51","added_by":"auto","created_at":"2024-08-23 14:58:29","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":1008941,"visible":true,"origin":"","legend":"\u003cp\u003eComposite spatial distribution of the vertical wind shear ( 200 hpa minus 850 hPa) (a) during break spells and (b) active spells, with latitude–vertical cross sections of 850–hPa to 200hPa component winds (include both the horizontal and vertical wind components) (m s\u003csup\u003e−1\u003c/sup\u003e; contour lines) (c)Climatology, (d) break phase and (e) active phase average over 90° E–105° E during 1981-2020. Black dash lines show depth of tongues; red-solid lines show the width of easterly wind core and red-dash lines show location of transition level of southwest monsoon wind and upper easterly wind. White dotted explained 95% siginifcant by two tail test.\u003c/p\u003e","description":"","filename":"image15.png","url":"https://assets-eu.researchsquare.com/files/rs-4936295/v1/2935763d3a10f84b36045e2a.png"},{"id":63137419,"identity":"21321f4e-369f-4a04-9bcc-78582c231055","added_by":"auto","created_at":"2024-08-23 14:42:29","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":1369975,"visible":true,"origin":"","legend":"\u003cp\u003eComposites climatology of OLR anomalies (Wm−2) and SST (°C) anomalies during (a,c) break spells and (b,d) active spells. Red dotted denoted 95% significant level. Period of analysis: 1981–2020.\u003c/p\u003e","description":"","filename":"image16.png","url":"https://assets-eu.researchsquare.com/files/rs-4936295/v1/efbaf209fdb7c291d5d7ba96.png"},{"id":63137423,"identity":"c31e8d3f-7e84-4c34-a8d7-b47ddeae0662","added_by":"auto","created_at":"2024-08-23 14:42:31","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":22209,"visible":true,"origin":"","legend":"\u003cp\u003eHistograms of the break and active days of various (a)El Niño and (b)La Niña years during 1981-2020\u003c/p\u003e","description":"","filename":"image17.png","url":"https://assets-eu.researchsquare.com/files/rs-4936295/v1/95c8e8bc769a397489df3a7d.png"},{"id":63140103,"identity":"25045151-6e16-4cf4-81bd-1906ee5013df","added_by":"auto","created_at":"2024-08-23 15:14:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11466175,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4936295/v1/f790ef95-0de0-4395-b81b-bc6524c08d5d.pdf"},{"id":63137969,"identity":"c3df8140-3462-49ee-a4e6-861a18d4cc52","added_by":"auto","created_at":"2024-08-23 14:50:29","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17031,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-4936295/v1/94305338503c1bc9f37d1a33.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Intra-seasonal Variability and Physical Characteristics of Break and Active Phases in the Mainland Indochina Southwest Monsoon","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe mainland Indochina is situated in the north of Southeast Asia (SEA), sandwiched between India, China continent and South China Sea. The Bay of Bengal (BoB) can be found on its west shore. But also its experiences two distinct monsoons: the Mainland Indochina northeast monsoon (MNEM) from November to April and the Mainland Indochina southwest monsoon (MSWM) from May to October. Rainfall, which is a crucial component of a monsoon climate, has a big impact on managing water resources and agricultural production (Aung et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Endo et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Oo, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In mainland Indochina, agriculture accounts for 60% of the GDP and is mostly dependent on MSWM rainfall, which is the main water source (Ghosh et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Satyanarayana et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). There is a typical monsoon climate in eastern Bay of Bengal region and the MSWM affects nearly 75% of the annual rainfall during the raining season (as global terms called summer). The rain patterns are non-uniform and subject to high variability over annual time scale (Oo, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, intra-seasonal and inter-annual variations in rainfall patterns are possible, posing major risks to agriculture, water resources, and the long-term sustainability of a region (Ren et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sein et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). According to Oo et al., (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Zaw et al., (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), extreme weather conditions are linked to the Indian and Pacific Oceans' larger-scale air circulations, particularly El Ni\u0026ntilde;o southern oscillation (ENSO). In the mainland Indochina, long-term climatic variance is significant for climate research because it influences seasonal to inter-annual climate variability.\u003c/p\u003e \u003cp\u003eThe variability dominant modes of the mean MSWM rainfall over study area with positive anomalies in the northern and southern regions and negative anomalies central region by using EOF analysis are already exhibited in previous studies (Sein \u0026amp; Zhi, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003cb\u003ea\u003c/b\u003e shows climatological southwest monsoon daily rainfall over Myanmar, the annual pentad daily rainfall shows in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cb\u003eb\u003c/b\u003e. Further, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003cb\u003ec\u003c/b\u003e explained seasonal latitudinal shifting of longitudinal average (90\u0026deg;E-105\u0026deg;E) MSWM daily rainfall (mm day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (day of the year) during 1981\u0026ndash;2020,which may well explained the clear pattern of MSWM.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring the onset stage of MSWM, the ITCZ moves quickly northward, reaching about 20\u0026deg;N as the Southern Hemisphere (SH) \u0026rsquo;s Hadley cell suddenly becomes stronger and deeper into the Northern Hemisphere (NH), while the NH\u0026rsquo;s Hadley cell almost disappears (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This change is accompanied by a sudden augmentation and strengthening of upper-level tropical easterlies wind, which accompany the westerly subtropical jet\u0026rsquo;s northward movement over the NH, and a strengthening of the SH\u0026rsquo;s subtropical jet (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003cb\u003ea\u003c/b\u003e). This seasonal transition in precipitation and winds is consistent with previously reported observations for the South Asian Monsoon (e.g., Webster\u0026amp;Yang, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; C Li, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Trenberth \u0026amp; Shea, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). It is crucial to remember that the seasonal transition in Hadley circulation is similar, as a weak or negligible summer cell occurs rather than the traditional equinoctial pattern of two Hadley cells alternating at the equinox during the summer season (Dima \u0026amp; Wallace, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Nguyen et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Shaw, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These parallels suggest that the MSWM mainly projects over the zonal mean Hadley cell (e.g., Gadgil, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Priv\u0026eacute; \u0026amp; Plumb, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Schneider \u0026amp; Bordoni, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA monsoon trough zone fluctuated in rainfall between heavy and light rainfall amount, and Blanford (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1886\u003c/span\u003e) firstly explained the nature of pressure distributions and circulations in these phases of contrasting rainfall conditions. Based on the synoptic features over monsoon core region of India and the first established criterion for break spells in the southwest monsoon season, Ramamurthy (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1969\u003c/span\u003e) used long-term data spanning 80 years (1888\u0026ndash;1967) to conduct a complete investigation of monsoon breaks. Break days were identified using similar criteria are used in the Ramamurthy (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1969\u003c/span\u003e) and P. V. J. S. Gadgil (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e)\u0026rsquo;s work also. Additionally, the two periods of break and active of India summer monsoon (ISM) rains have been the subject of extensive research (D. R. Sikka, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Goswami et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; M Rajeevan, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Pai et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; J. S. V Krishnamurthy, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Webster et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). It is common to identify the dry and wet seasons of the ISM in the area of the seasonal monsoon trough (R Krishnan, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; P. V. J. S. Gadgil, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). There are a lot studies regarding the ISM break, however the MSWM area still needs a proper definition to define break and active events.\u003c/p\u003e \u003cp\u003eThe MSWM's active phase is described as the time when periods of heavy rainfall occur across the entirety of Myanmar, the center region (C) being the exception as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The break phase occurs when there are periods of typically dry weather and insufficient daily rainfall. According to Wang et al., (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), floods for the active and droughts for the break could occur respectively, and lasting for extended periods, which could have an effect on society's economy. MSWM rainfall variability is not only important for agricultural crop production and at the inter-seasonal, but also detailed analysis are need for the long-term levels planning.\u003c/p\u003e \u003cp\u003eTo define the exact characteristic of monsoon break, this study's initial objective was to identify and record all break and active phases during a 40-year period (1981\u0026ndash;2020) by using the criteria of M Rajeevan, (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Based on the daily gridded rainfall data's standardized anomaly, the mean characteristics of each break spells and active spells are computed. To avoid the influence onset and withdrawal physical mechanism, the break and active events of only the peak monsoon intensity months (PMIM) (July and August) are considered. The standardized anomaly of 3 consecutive day rainfall values over the main monsoon core zone are denoted that either above +\u0026thinsp;1 (active) or below \u0026minus;\u0026thinsp;1 (break) for the three consecutive days, depending on the event type. One of previous study has also shown a downscaling of the south west monsoon's break and active phases (Narayana Rao et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Prathipati et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). It is, however, impossible to infer a reliable average feature from these studies, which make conclusions based on one or two cases or a few years of data, and only a few have stressed the need for a comprehensive climatological analysis of break and active spells across space and time.\u003c/p\u003e \u003cp\u003eIn this work, we have attempted to implement a thorough analysis using the 40 years of data from both stations observation data and gridded datasets, as well as the disparities between the composite patterns linked to the both spells in terms of the composite mean distributions of rainfall event frequencies (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The study also examined the differences values between the composite monsoon rainfall patterns of all events. In addition, the study examined the distribution of lower to upper atmospheric wind patterns and sea level pressure (SLP). The data and methodology utilized in this investigation are described in Section 2. The study's numerous findings are discussed in Section 3, 4 and 5 before its summary and conclusions are summarized in Section 6. Further, the author hope that the circumstances of this study output will be useful as the basis for forecasting and monitoring drought and floods in study region.\u003c/p\u003e"},{"header":"2 Data and Method","content":"\u003cp\u003eThe high-resolution ECMWF reanalysis version 5 (ERA5) for the period 1981\u0026ndash;2020 served as the primary data set for the current investigation (Hersbach et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). Moreover, meteorological data from 79 stations of Department of Meteorology and Hydrology, Myanmar (DMH) around Myanmar was gathered for validation, particularly daily mean sea level pressure (MSLP) and daily rainfall data (Zin et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Daily rainfall data from CPC gauged based data are also used to analysis break and active MSWM events (Takahashi \u0026amp; Yasunari, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). For the composite analysis, the daily means of relative humidity (RH), temperature, zonal and meridional winds (U,V) at different levels, and total precipitation, MSLP and outgoing long rang radiation (OLR) are considered from the ERA5 for the period 1981\u0026ndash;2020.\u003c/p\u003e \u003cp\u003eFor testing the differences in the composite rainfall/OLR anomalies and frequency of daily rainfall events between active spells and break spells conditions, two-sample t-test was used. Then, we estimated annual standardized values for each parameter for statistical analysis. Frequency is a measure of the number of occurrences of a particular score in a given set of data. The climatological is the examination of each month and season to determine the mean proportion of occurrences of meteorological factors in the study area. Thus, we can calculated that the frequency of the weather element is divided by the total number of observation times and then multiplied by 100% to get the frequency percentage (Carlson \u0026amp; Winquist, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrequency Percentage = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{F}\\text{r}\\text{e}\\text{q}\\text{u}\\text{e}\\text{n}\\text{c}\\text{i}\\text{e}\\text{s}\\:\\text{o}\\text{f}\\:\\:\\text{e}\\text{l}\\text{e}\\text{m}\\text{e}\\text{n}\\text{t}}{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{o}\\text{f}\\:\\text{O}\\text{b}\\text{s}\\text{e}\\text{r}\\text{v}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}\\:\\text{T}\\text{i}\\text{m}\\text{e}\\text{s}\\:}\\)\u003c/span\u003e\u003c/span\u003e * 100\u003c/p\u003e \u003cp\u003eThere are several statistical relationships for perceiving the relationships between two variables which are expressed in both linear and non-linear equations. The changing trend of break spells and active spells frequency over study area was evaluated by linear trend estimation test at a 95% confidence level. This approach has the advantage that it does not require any assumption on the data distribution. In additional, the correlation coefficient (CC) is the major statistic for explaining the connection between variables. This index shows the degree and direction of correlation between a variable and its environment. The following equation is used to determine the Pearson CC (r) value:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:r=\\frac{n\\left(\\sum\\:xy\\right)-\\left(\\sum\\:X\\right)\\left(\\sum\\:y\\right)}{\\sqrt{[n\\sum\\:{x}^{2}-{\\left(\\sum\\:x\\right)}^{2}\\left]\\:\\right[n\\sum\\:{y}^{2}-\\:{(\\sum\\:y)}^{2}]}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eOne can also calculate p-value and compare it with 0.05 significance level. If p-value is smaller than 0.05, the Pearson CC can be considered to be statistically significant and the null hypothesis rejected in bias of the alternate hypothesis. The other statistical calculation of mean, median and trend method are also used in this study.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Definition of Break spells and Active spells\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo define break spells and active spells, M Rajeevan, (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) presented a criterion based on the standardized rainfall anomaly (less than \u0026minus;\u0026thinsp;1 or greater than +\u0026thinsp;1) for at least 3 consecutive days over the monsoon core regions. As a point of comparison,, Pai et al., (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) identified the both break and active days by using the similar criteria (rainfall) as M Rajeevan, (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) for 114 years between 1901 and 2014. Due to the heat trough circulation characterizes long intense breaks similar while humid convection regimes characterize weak spells (M Rajeevan, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), the anomalous daily rainfall is a major criterion to find the break and active phases during MSWM seasons. Sub-seasonal fluctuations in monsoon rainfall occur across the Indian-Indochina subcontinent resulting from the position and severity of the monsoon trough as well as the internal dynamics of the MSWM circulation (Sein et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Ramamurthy, (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1969\u003c/span\u003e); P. J. S Gadgil, (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e); TN Rao, (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) have shown that these spells are not uniform over core monsoon zone and other rest of regions. Thus, Narayana Rao et al., (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) applied Rajeevan et al., (2010) technique to identify active/break spells for small homogeneous regions to study climatological characteristics and their vertical structure during active and break spells. Thus, we also applied the three consecutive day\u0026rsquo;s rainfall anomalies over the monsoon core regions to define break spells and active spells.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Result and Discussion","content":"\u003cp\u003eThe average rainfall across all of mainland Indochina is used as the basis for several studies on monsoon breaks. The current study employs the CPC normalized daily gridded rainfall anomaly data to identify break spells and active spells (Hersbach et al., \u003cspan class=\"CitationRef\"\u003e2020b\u003c/span\u003e). However, the analysis by V. Krishnamurthy \u0026amp; Shukla, (\u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e) showed that the dominant mode in the daily rainfall has anomalies by the regions. Thus, the intra-seasonal variations are not equal distributed throughout the regions and the average for the whole study area is not representative of different sub-regions. Similarly, all-Myanmar rainfall are not also coherent over the whole region (Lwin, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e). The monsoon season\u0026apos;s rainfall anomaly pattern\u0026apos;s (JJAS) most noticeable characteristic is the significantly lower rainfall total over east and central Myanmar\u0026apos;s plains. High amount of average daily rainfall occur over northern hill and over western and southern coastal regions of mainland Indochina (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003cstrong\u003ea\u003c/strong\u003e). This rainfall pattern is alike to the dominant intra-seasonal mode of V. Krishnamurthy \u0026amp; Shukla, (\u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e). In identifying break and active events, we adopt criteria that are generated from the rain fluctuations between break spells and active spells over the monsoon core region: west (W) and south (S). Here the northern parts (N) are ignored even though it has excess rainfall due because the rainfall variation of this region is influenced by not only monsoon wind but also western disturbances from middle Asia (Oo, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe core regions are generally defined as W (16◦N to 23.0◦N, and 91.0◦E to 96.0◦E) and S (11◦N to 19.0◦N, and 96.0◦E to 99.0◦E) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003cstrong\u003ea\u003c/strong\u003e). The average rainfall anomaly of these two regions is consider. The tropical convergence zone (TCZ) establishes itself over the monsoon core zone at the end of the MSWM\u0026apos;s onset phase. During PMIM, the TCZ fluctuates primarily in this region. The monsoon begins to leave the northern portion of this zone by early September (Oo, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The term \u0026quot;break\u0026quot; is typically used to describe dry periods (fewer rainfall days) that start after the monsoon\u0026apos;s core has formed. The onset and withdrawal stages of the MSWM are examples of seasonal transitions that should involve changes in some systems related to varying rainfall intensity. Hence, only the PMIM, have been taken into account in order to identify break spells and active spells. While considering this zone, care was taken not to include the east lee-ward side of mountain range, where substantial amount of rainfall is received during the monsoon breaks. This core monsoon zone is quite similar to the geographical region taken into consideration by (A Kulkarni, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; N Singh, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rajeevan et al., \u003cspan class=\"CitationRef\"\u003e2010b\u003c/span\u003e; Ramamurthy, \u003cspan class=\"CitationRef\"\u003e1969\u003c/span\u003e; P. J. S Gadgil, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003eb; TN Rao, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) for identifying both spells. Following by the methods of Prathipati et al., (\u003cspan class=\"CitationRef\"\u003e2021b\u003c/span\u003e) and Rajeevan et al., (2010), there is a total of 142 events of rainfall, 74 active and 67 break spells are identified over study region during 1981\u0026ndash;2020 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eA dynamic spatial correlation between the mean rainfall (1981\u0026ndash;2020) during July\u0026ndash;August and lower atmospheric zonal wind (850 hPa level) are also exhibited in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003cstrong\u003ea\u003c/strong\u003e and \u003cstrong\u003eb\u003c/strong\u003e. The southwest flow wind (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003cstrong\u003ea\u003c/strong\u003e) and non-homogeneous rainfall zone (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003cstrong\u003eb\u003c/strong\u003e) was significantly found in monsoon seasonal dynamic pattern. This show the variability of monsoon wind influence to the rainfall positively over monsoon core region and negatively over central dry zone of Myanmar, and southeastern Mainland Indochina region.\u003c/p\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Statistical Features of Break spells and Active Spells\u003c/h2\u003e\n \u003cp\u003eThis section evaluates the several statistical properties of both spell between 1981 and 2020. It also examined for the daily, intra-seasonal, inter-annual and inter-decadal scales and compared to examine climate shift if any in the all spells events.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eTable \u0026minus;\u0026thinsp;1 - A statistical properties of MSWM monsoon days.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAvIAAAD+CAYAAABYxuEeAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAFxEAABcRAcom8z8AAJn1SURBVHhe7d0FmGRbdfbxm+DB3QkQXIMGEiCBAMGCu3wECw4hSEIIIcGDBgnubkGCu0twd3cCwV2/91dTa+6ec6uqe2a6Z7p71v953qf7SFWd2rr22mvvOqxpmqZpmqZpmqZpmqZpmqZpmgPE70dHbrVarVar1Wq1WttGbPjDLhrdI/rnVqvVarVarVarteXFdmfDH3aW6OrRVVutVqvVarVardaWF9udDd80TdM0TdM0TdM0TdM0TdM0TdM0TdM0TdM0TdM0TdM0TdM0TdM0TdM0TdM0TdM0B5cjReeK/jI6oRNN0zRN0zRN06zmgtHdI/vC3nOuW0aniQ4UfxC9MvpddDknmqZpdji28NP2/mP0h05sEFeJvG9vD9g0TXMI8A8RA3qq90d/Hh0IGPIvinzuXznRNE2zgzl+9Jmo2ls/srO3/H3EAXKN2dEuThx9KvKeD3KiaZqm2dncLtLoPyc6eXT66PXzc8+PZj/DvMkw5F8Y+cw25Jum2en8dfSj6PvRr6P3RMeJ9oYXRNrMO86OdnGs6P4RA/8yTjRN0zQ7mzLkHz872sVtIufeFR3diaBT+NvoFNEVopvP/y8uHHkv4iES9z5ygug6keu3jS4SFaMhX53PqaNbRzeOjudE0zTNDuGRkfbuDtG75/9rQ6doGxn92s1bRGeIjhIJnzFr6nUviVw/TwQ/nX/T6JSRNUf+v1F0zKg4aaR9Fd7j/XDU6MpRteMXi5qmaZotjgZbZ/DkiPddx8ET/8vofvNzR44qhp23/hfz/y8VwbTwtyPnSrxFpnlxieid0Xjd/WLxcbSoDPm/iHQsz50fvyHaW09V0zTNVoUR/cHoi9GJogdG2rr7RiNmR7WLv4mq3XxfdO7oHcO50t0iPC9yzNly2uh78+Nqr8Gwd057rn3nODEr+9uo3s/r9iXkp2mapjmAlCH/8+g7UTX6r4hOFYFh/ayoOpRbRWeMGP085s69NNJp8Lw/an7uwdHvRdeNdBJ2pnH9ipHrX4p0IO4pQ56nvmYEXhedLGqaptkpmNHUlj5hdnTYYVeNtHcfio7hRDCj+R+R8y+Pzhb9ScRIP0dkNlQb7foDIu2xuHs8NXJeDD1HTDlFHhKB4V7nGPvaX44cxwx3bbTPM2hw7kpR0zRNs0UpQ55RbYr2xdEn5uf8b2pWp8KQd04nMVKdycMjHZIp33tFOqoPRzVty+tu8azrN4t+EH0t+qMI/xV5n8dEP44s2DKN3DRNs1NgNDO8tXVCFWF3ma9HP40u7kTgtf909KtoDEMceWbkff5udnQ4ZchX7Hw5W94WaY85aMTmc9ycKRKC8635OU4UbbS2vDz7nrdpmqbZopQhz4AuNO5fiJw3BYsy5HUKhdj1j0TOL9J7Ix4hU8EGBdPrn4tMH6MWbtX7vSwSs9k0TbNTEErDScGIPqsTc2pGsozmc0Y/i74bmclcRHnV7zI7OpypIa+NFcpIPO3WMLmuTeakMVCw4Na5RbJ4tmmaptmijDHyplyLMtzFyfMiPWN+XHHtMA1c0686Ewb7+aPzRTqM00V4euQe3nvXLaLSkX0lKkO+OjLbYX4j4tG3uKtpmmancMlIO0di5D8b2YZSaKNzb41sMGBfeR5yM5eM+kWUx1wIzcjUkNd+l9Fvce2z5//fPsKFIp5/hr5dw84baactnjVbYHagaZqm2aKUIf/o2dEuRk+7eHiUYT8a8nhi5Py/z44O5yRz4TWRe645O9rlieJpWmTI23HBZ/jfdG8NBpqmabY7Fff+yegtkXAXemPEA//DyC9cc6q47t4bRoX20haTsO7I9X+ZHR3O1JDHDSLnPhYZIHCWcLxAaM3HIwtdL+vEgPa3Pq9pmqbZgvDQaOA15KZ1GeQWmTpnZwSLUYXH2MnGOVuWjfCu1wJZxrjX66x4m2oXBWE7rttmzXVeKMdfjSpG/r8j5+xwY5u02iXncVGH2DRNs93hZRcLb0cw7dyUagPNjkJb6/ibkR93sonAR6Py0PtFWNeFQWpXa7vImj0dQ26sN9LeOk8W0Nb6Je27e51n5HPqeD8x+N7bwKJpmqbZoghfMX1bW0qSRah2L2DEQ4P/pMh9N3FigunYV0X1eotVHxadJgKPj07B9K3rpnl56Q0e7HQDnY/3N/UMHZ3n4JX/Uyeapmm2MXZ/4XF/c1Q7zIxwqmgDXTcryhN+p6gM8J9EnCS1H7yNCBj9FZZTbTND3Ptcf3a0Cx7+x0bOc7xMZ1a9p9/3MFCodtwaJjOyx42apmmaLYo4dwuwRi1quI8duVY/EDXF+Xr9ok7KYKCu61R8bu2IA3vFuzZ6322DZvtJ21w2TdNsZ7R5fltj2e9iVBs6totg1C9rV7Wl1a6Wh73a6ulM5tjWj+uhRuq15P+maZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZqmaZrtyC2il0Q3nx0ddtiRo3+NnLuUEwMnjv4zemF0difCuSL3PiQ6oRPNpnHBSFrT3zkx4fTRc6MnRX/oxMCFIq/79+jYTgwcI7peJF/fMNcLomtGfxAdivx5JL0eHh3fiXC1yLnbRb/vxBz/3zpy7apOhGNF6sqLovM6sR+cO/Le9I/R70Ujp4qeMdcZnBg4T+R1D43qexRHi+Tx86PKd8+rLHj+5uCgvKiL6vEpnAjaYvn4z9HRnRi4fuTa38yODjvsKNF9I+eU42bjuFYkXekqTky4SOTafaJpHar241bR2H5A36pNf2WkHr4+enJ08UifvJM5QcR+GMvraaJnRk+N9Gsj54vc++DoOE6Eyhf2zLR93Bu8Vh/pvbSnZ4ymVFt/7dnR4egr7xm5phxM0Y5rh6utfV10r8hn7M8zN81hT4l+F2k0cLzog5FzCuzIaaOvRq5dw4nwV5Hj70Vnc6LZNFR6aU1fjqZG2yUi134YaTRG5JdrX4xO58QcDc67ot9E34hcp29HP4nuGh2K3Ciq9Dq5E0Ej7JzB0pGcmON/nY5rj3AiGNR+JXLuJk7sB/LA+5B8qUF08aeRa7+MDNhGrhC59q3oLE7MOX/0lki+u1b5/r+RfFfWmoPDX0fy7EdR5dk/Rc4x8KYGIiPItefMjnYN0N4XOWfg12wM0vWlkXQlhlgZksVtItfeG00dWxxkrr0i4jwBg/66kbr360j/6v8vRd+NvhOVc2CnwhHx6UjaaHehX3L8s0hbNXKlyLWvR2XkG7g695rIQHZfUd/UO+9F/xCNjGXg/k4MsJ30pa7dwYk56uuDIt/lx5G8lcdfi3yW17CtmmafeVSk4PEegtfu7ZFzN3ViwCj5Y5FrV3cilPGoUE4NjGbj4LX4QPTNiBHPAPvbaERe/CJy/RxODFw5kk8fj8qQ59nQqDj/6IhXqDh1xHPEqDgU4ZWWLh+OTupEeEDkHE/p1JB/XOQaYx/y66ORczd2Yh/RCbwjYmBr/L3f2EmAIa9DcM+00zPQZiB8PiqjUD39bOS9DOTL6wv/m53b6cbDVuYykUGZgWB5BO8SyS/evmM6McD769rTZkeHHXbU6E2Rc17XbAxmSrS/X4jUtR9EfxaN3DKS7m+MtAEjBlWumfWqmU6e5J9G8vuO0Tjbcs7o76M/mR3tXLQ5Bj7SRrsLhjyHAueSWcWRy0Xu/Uz0R06Eu0fOvSzaH0P+TpG8+FSkL/2faMwT/5vB9Fn3cGLguNFrI9du70TQN+hbnftcdMmoUE8vG7m3+pim2Sf21ZA3TYgy5HkS2iO/eZhyrHxS8f3PCzw2WqZh1zLk5R8PCE+QaUvnXhyNhmmz2JA35ercKkOedxSjIV9epkUwqr32trOjI3KByHuYMTNt7P//jniGigtHaxnyOpGawVGGvM+ro/F9mq3BIkP+zpE8W2XIq88YDXmvazaG8rYLgRFy5//pQKnq6CpDXtiUNuNEEUPROR7lQ5VFhrwB0lqGPC9+eeTLkOctX2bIa+sMlh4TcVRN8TqzLN+PODJEJmg7tcEFQ/55kc9aZcgLv8RfRvpksyujEd80G0ob8tsDU3O/jcyEyAehTNL8zFGxXkP+ZJFG5xORhsrrwPurYRw1NRoOFQ6UIc8j4x4dwCL+LXL9htFJImEwwmvG0Kn1GvLyU2f2/siMjrAbyGOzNGO+T8M3mgNHG/JbDwac+HVeeDNb8kgdels05sd6DXkwEIVaqNOcK/CasR5a6yQ/dzIHypCv2U33mY2e4nO0rfJOm16edHHvxd4a8vVcXgMONN93zGP9ccfIN/tFG/JbHx2FaUThSzWVKFZWuv+/2dEu1mvIM0wZCKZ0NVwVclFx4SUGoI7pUORAGfLVmT97drQnjO4PReJkK2zN1LH3HPNlvYa8OMxTRgaB1lFUB2nBq/csud+UfnNwaEN+63HWSNtqkaLFpwxsYYnqymgU7o0hrw91LEa6Qm0s4HSupO7rY3cyB8qQh9lMbR9bZkrNdFtQDovHHb8zqpnL9RryZm8g3M2xOgr2Va1BJOVHWRnDWptmr1lkyNeodWrIm44q42QaI9+G/OZhSo73Z/Talqf2WVHtaiAvlhnytUBIjDwPQIVs8AbppCD/GHAW1tWAbdpYHSosMuRXxcg/NnJtUYz81JD/48h96p6O5VeRGHbHZG0CGOjyU32sz6tYaXG21bmsipG/dKSz8P4MeQOCn0emj6uDZCwKF2BoWIfh/X3X5uCwtzHy945cWxQj34b8xmDjB+lpMA8eVHXQuTL8sCpG3sJJ18qQ1786Vr9rBkxsuDhthh8ni+vi6Hcyiwz5VTHyNYu5KEZ+asirK9o27Spni75R3bJhgXMcLwZlPOWVn38RQb9oIMXRclEnwlox8hbbulaGvPbdsToKbfZ1InlcRv64bq1p9gmFWWEqQ97K6/LI13ZmhVGsQudaLYZrQ37zeVgkjRlX4iqFWGjcndPY14p3DZBGyqLIMs4LW6W5vzzyGg7TxO5ftJjKAMH9Yyd1KLHIkLdLgXOPj8bt4xjZzrnGo4ZVhry8YHgLlSL3UB3rjFCDtUdGdsCQ7xYfM8y9vvLYTjU1RT/t9BiF7ueR12H5Lu5zbpGnr77HoRyze7BZZMjr+OULI7B2PClqxw4Ll9GG/MbCaDcDqm6qy+qhdljIhTRmiJfxaKG4c2Kt9aUjtfvUf82ODjvsipFjnv3pDjcMe22P6+U026ksMuQ5J8wY25nGFtcjl4/cyyNfBvAyQ166mkVZ1tZyaJhR4dnXhnJ4cHbIYw6vsoXKaGeIl0f+bk4MMOTlu2tlyD8hcly7Ao6In3dNP9G71jT7he3yFCbxYGCgqAzOTbeftIreCJWXsLa5Gw35qfHY7D9mSGpLq2USP40zRYxzxr28GuHlda8tB3kVNHaVz+LvR8bpw0PVkLdHsO+vkdXRoKZeeXNGGE7iZ12rHWVWGfJmUHTU9vPXmeuweG4cE0PN3wqfWqYKrzHArl00/NbASE0PvyeyVR6jxPM7ZxA/4rlqO9o25A8e9oxnyAulq7A3Xjz5okxMY6afHo15xthoQ37jYNgZ/ErPRfq/qJwhtd3ru6Pp1pS1yLycZsLcatvF6c5Wrn0kcm2nG/IM5lr0ewMnAgOdrSEMcBrPXjMZ+sVK49GQH/fd194JW9KecmIwtBnsBgrOaYfdUwOwZTIbXuFPZTPV7Ezhe0wdnbXts9mDqstFbTPbhnyz39T+1xXbC42/czwCY4hGxWIaPZe3oQx53qNFcWfN/sE7x/tqSlBHXZIXZegxIhnfDMAyKE0j8hBAA1Md+xgyoZGxty3PhIawPH0MhYr1O1RDa2zt6ftbGFpT5OI2dSy8OOLKCw2yzlw+6SDAY1fhSdMfDhmxroHRZuegEVO5PsfUsg5jzPfKY9P3OiL5xcvnHKOuvHv+vipynldfhwXGhmd13vvp0OB6TQVPO6nmwGHGRp3kqa021WynsiAsqn68D8okJ4rwrNoqVj2u+r5sN6Rm/ZgNMYOl31Nfqi4Kl6g6/sAIwk/Nfsk/O6QUwul4e93rB7wK7atzjNbyRoMhW1sDj23NTsTvdDB0fdcygA1GhZE5x7nAoQWx5BW+8h9OzClDXnjMMrSTXqsO1ZojaPfs4OX1nByVvyQEkuGvvRSmCLPh6ps+WQhQwVmmLVcfa6bAc1ddFGs//jigQYvzNp2o+5tm3WhUFC4FS4FkMFgUVzAAGRYKGU+Elfk8eu7VuFQMGXiP3EcWcbiXTNGXIdnsGxozvy4nbceBVjGmfe08I/a99gjXQMiLaiQ1YuOiGp4L8YM8ua5bWOl+PyYjr2n6gxg7GR4XcYvSoOJTTZ+XASy9pIdOWmfAI+Rexr2O3gC4Ytmlc3nbhDq5j3QYo4dJvvmscbGrz6kdE8TeTzFY8Hmum2aGGZjyBvlcn/XJ+bG6Xj9qBc/Im894cJ1HyP3quNk27+3Ha5oDh5lQgy35wBMvX5SB8i4qgzyRZm/kj7xyr5kY9xp4aS8gLljb7rz3ch8Z+C0Kp2qWw4urXZSWNfM5wlh3bRx0GbjrU7UTHAHSXoiI+8yClWcXBtHl4bUOqvK16jID0kYFOxGzhb5rzVxan1Ozn2CnVBtmX3f3+utYuAwHVVE/lsgxJdTJvTQu2lcv2DVsmnHGukJctMOLnJE1cDCg0HYaKFf7LJ99jvqmbuoLpmsaDMLfHLlffa1nqzJhNmL6S+xNsyaMPUaeDl+hV/DKWCk0MLw5Cqj7NCw8ENMpIJWNZ5i3QqVzL/mJ/xpFN/uGaUMxdqZpa7HNiMrPg6rxF6JR2JKSt0KeyQtxfhrNsQMZMesi/tv7VF7bAYPnthZhHQpo6F8eSQOzUWLUzXSMMLgsHJbuVdYNtnhGR+SdWRF5p6Oqe3XsFrEWjC/GwlhXPIfpd681WJuis1O/5NfYaVj4ZcalPk8HcbOoPO5TTPXqAA1I3K/+mqFjOCx7TbM5KFfKkXyQf9ZaTGOsYeG7sDcGjXvFzDtXA0goU8LllB/hGe4j3mM7fjTrxxoFg2+G46LwB3VZH2mQNMZy89TKz8onu01J+zHso5B3F4u0F5VfjFptuEH7tA3aKQjd9V3ZIjzp018jh9++UBeUXfdKa7+UPe2XzGYo79LNfaVxRpmNY3ZVezvmg5lp9os6M9ajwnN6b8Z75QXvvoGdclGfZSA+3WSi0J6aiRn7DWXGbI/4/EWf2zRN0zRN0zRN0zRN0zRN0zRN0zRN0zRN0zRN0zRN0zRN0zSHNLauemKr1Wq1Wq1Wq9XaNpptP2vHA3tWt1qtVqvVarVare0hNnzTNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3THFx+PzryGtpsjhbdO3p+dHYnmk1nPfl69Ll+b3Z0RFw7RrTserPxHGmuZRw1ki/q9TLkvXw7EHX7UEfbtqoOQX7KD3m3FvKs69vORN5O662yUe3sKGVlWTvgPVa1Ec3+sSifRuSXer8WVe/Xc2+zNvJEeh5ldrQc+UNTvH5VXdvS7e7Noh/O9e3o89GXou/Pz30mOlW0Ho4VMcRPF60q6FO87kPR76JrOdFsGqePXhH9V3Q8JxZw4ejl0Wejz0VvjE4TFSeNHhl9MfpU9J7oqlEbhpuLPWM/Ft15drQnJ4nuHr0vkmf+XiMaUc9uEskv+faF6A7Rokat2T+0mfeLPhBJ57dGl4+mXDF6f/TpyH13iRZ1RMePHhJ5v/M40ewY/iD6x+iT0aWcGLhQ9N5I+VBn3fOJ+f8vjU4RjVwgemf0iGgtg6bZOxjct4vkxVWcmHCW6FHRR6IvR/rQP4umMA7/X6Quy8+vRQ+L9sZmag5H/3WdiJ0iPb8S3S06bjRyyujpkXaWnfui6AxRoV1Vd8a6VnpNdNpoy3Lp6DlzvTtiTP868iWde0x0wmg9MDS8/u3RMZ1YJ+7V0XntNZ1oNhyN0A0iBfgH0buiRfnK2HD9ddHfRXeMHhvVfqU6HQ2U97lrdKvotdGvoitHzcZz8ujB0Xejn0UPiEZ02Iy8d0QMwTtFBuDy5GJRcdPoo9F9Ivn2skidu03UbBw6lidEb47+PmKk6dg5Rv44Kgy0vhk9O7p1JA/VPbOTo/fHwFqbKv85W/40anYG54rUQ46zn0ZTA1Hdv1Gkvt4iuvlcjA0G/gkiHCfSVitPP45eEK1nhqdZH2eNRAyon7+MbhiNcIq9OGLwaU//Nfq/iFP01FHBs3v/6CeRH/SRl/eN/i1qR9i+we5geOsjpad80K/Jg5qZMuB9U2SQxW7RR34w4kCuwTBnmAHWWNc4ur2GA+1k0baAd0gC8MTuCxohr39bZNS5XkZDfupFbDYGnh2Nyj9E/xExDE4UjfDYfjjisV/mzTlfxINw3dnRLnQiX43+fXbUbDS8Cx+Prh1pjB4ajcir80fVqePPI8bBw2dHu6jZskJ+a8zUvZ7e3TgY8heMpG+hszEIu8fsaNeAmGf1v2dHh/Og6H+jyideJQ6WJ0U6F96hRV6+ZvvByGDE86xfP1Ierh6txR9G2lsDxIIDzAwpw4MDzoxrG/Ibh8H26yNtMGfIjaORY0d/Eo3GOIOeU9QsaGFQzog36Go2hjNFZ9717wy2pxlptkz1iYx37e/oSOGBd47DchmMezPcnCvbBsYZY5q3dTQKoNHRGb064l3y5UwH1dSEToa3yOt/HmloeAUYeWSUI2G9llEiPEBnhjbkNx+eHUY4GBP/E00Neb8UJg9HL+4UBiOvz1j4zxYx7o1gm42H106ohqlX04cGYmtxxkjHzuuzDPVOWMdLovYGbS7njdStmk1R974R/cvs6HCEqH0rqjAcnRLDXfv7lxEny0WiZvujPjPs9IPCMsyiraf/037rR72mMGOqHcaTI17JNuQ3DkY6O4Z33YBrNM6XoT81C1cznmbZ1H/e3XacbB7aymdF2soTOxH0mY7L5gQniTBT9WVZWJMB19ejcQCw5VllyDPUfxH5UsIuGOPuNfLRKV0tqjj370UMDlMdEk7h/U5kRGvqSQyT+4xyJCAPVhvyBw6hFUJrpob8PSN5eI7oCpG4M+FSY6yZxkh8vIEar8RfRK+KeIC2zdTTNkVHIlxjPYb8lSJT9becHR2OcCr5y4BQPw2uhdc1m4vwCN4fdQrqlHbwKbOjwxHjrI3929nRnqiLbcjvTDhI1mPIi/NlSC4boGufxQG3Ib85CLFZryFvzZJZUX0k2EJmVNV5a9V4960JvGjUbBy86OoSm6QGTMKXDKqm6z3NhgkNHg38gn1kHYMZrm3FMkOed6+Mb6NMMAgYg85Vp/NXkWNG/AgP7xjXyXBwH6+whBYa0Ib8gWOZIW9an5fQgg+GukUh8sTgywLXEcaka8QYXO86imbfYfytx5DXmT8jEqIxLlIGD4MZFYNyeWf9Q7O5aN+sKzGLWfWE18iMpo5efCxP/G0jg2IDsOnUPdqQ37ms15BXRsx4L5s15RhrQ37zWK8hz+kibPEtUYUZs6mEtHKCcpiZVdFG/ybaVqEbWxzOEn3buHGK+mJ9wysjcfAGURYmm/0U3rhoTSfni3VmZfNuG5YZ8kJqnFcwFdBC4XO+4nB5AR0zysWMjVw20kl5DxXBfYySNuQPPMsMeXG4DHkDM9O0jEALZBl94rQLC30MwhgiYs8sNLEQaGo0NhvLeg15dYgxaHHrFINyA2lhGhZXWkApH3uqd/NQn3jjzVqOMOr/MzJbadAs9lbMtDC1RbtitCG/c1mPIa9PfUOk7V0WCteG/OayXkP+XpE2eKyrZqwZ8dYQsofsgiI02Qz3j6Ku1/sPG0RIqbDu6W5s1qGwVcx4snE5KOXH0yKOlRGDL22xha6LvPVbmmWGvAU4zpsWGkcuVgU7r+FA7VozGvKMdJ5eiz4UVjG5EtN9bcgfHJYZ8o+PDLSmBVe+16I8RiCPrjUPxcUjBuFTo+48No/1GPK8BxZTPXB2tDbCa9S7mv5tNhaen+lAeIq21oyX/L1kxKi3WHlKG/I7l/UY8gbfvPE8istoQ35zWY8hb0tf+TS9R7w2w5HhPiLUkWe+dw/bP9gzBrnCva0JXAT7VeiNvGCjChPnmZ8i9NRAbLRztg3LDHmjR+cl0LgTAwPd+dqtxPSwY4ZfefgkmBESj5TrUHDdx3hvQ/7AYyZF+Iy9qUdsXSivpj/KZTrKTjZgRNr9ZtpJmJURjqOSNJuDurdo15pCvTLIEoO5Xg+7zua3kR2rmo1FZ2C9kDqzbDHVFPt/6+zHdrawAJYh39tP7jwshmbIT2dtRsyYyv/p3vFTeBhtHz31Mjb7j91RGPLCLhbBxmHr2N5wij5TP6o/ZfMU4uXNhBsANPsGA128u7bTLjbrwU5+wm0W1Tn2jNCncZe3bcP1Isb01JD3ZZzT4ZuuZyRYlMVwc39tmcVj4FihlJgKLkPea+29akDgtVYJu68M/tGQ733kN59lHnkFWz7ZSYPhIdZa3o6LJu2Bq6GyQMd1mDI0MOAtXmSANBvDKo/8uSPTgHagWTQVqI6Jr2UE1rS8vY+Fu4kTHPc6bvYP9YIRLyaeUTV22iOmb2tAzOiqkKhl26G1R37nspZHnifYYj0/MraK9shvLss88uq8DSKEbazahtn2z7z1tcBVva9YbYO5Zu9hcxjksjNrZ74p2mDtbdks+jvRIW+P9KsjQp6EO45bN28rypA3ShkXL/ryDDk/cOA6Q84CDaEyFmpVR8WgswLYPa5b4CWRrBh2jjRGFnj534IPcUxezwhxrg35zcH2ZAZQDL1auCyveG9rC1H5YNrPD4r4cSHXhcyoJLVgx72uucdA7IWRkSsDY/qrhM3GwIAT88eTY6Cl0Ve3DLh0BPLGjlDyVJ5YZc8jJ8bvcZFpRnVYwyTv/ZCMfPOjMjqeVVP1zd5jgCyNq449N6r8sB6hflHZzBijwO4KtkHjGFH/RgNM3hp4M8zMiHpP08fer3cb2t6ok7ZhlpfipuWtflAYo5Cswn0WrxvkLTP2/G6Bvc69VpvtXu2FGZ6eJd1//GCbfNK+yie/56A+16J0XvVybDqvvXY/DzwbqZwr+mH9p/zRhqv3tqWd7i7WrB+DI+luIKwOSHftJVtH2C8MnNib2mPOK3kljHiR4f/oSCj4tp35tC+tim/Uv2gVL08QY0AstL+LDDcFVcgNA9HPGUNDpCIwCI0+7W7jxyyId5AxIqzDr8hajNdsPKYE/dqkfPGXgcewt9huTHMeHfvBu494HqYLqxgqttZy3XvYtnL8QYZmYzFdK51JvlnLIO01YGa05I9pWfXHNfdV/omBry23DNQMlO1U4x51nee42VjEXloEvig/ODVqJoxRxrCvfK2940e0w9pjr7XloLzTjjoWT99sX/SL+kB5KU/lrTx2PP4wlDbZejRG/7JFroyOKmfKXJU7M6jTHceavef20aJ8shkEDJYMzNX56mdL2oLRnuLwrDrt/ktEzb6j3dR+Vr82pn2t/eI8MdNZ54U+TSMSCvn1z9F6w1Obpmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmma9XOU6HTRmec6erSIk0Z1z4mdaLY9J4nk53FmR2tz2uhc0RmjIznRHHDklzyQF3vLH0aniH5vdtRsNqeJ5FXp3NE5o7NEx4tWIY/c5/59yetme6L/VcfXW0+PHylXZ49O4ESzqcgfdfIckTxaL8eOvE4+nciJZkPRnkrfsa3196zRqaL12ivyRv1bq33eclww+nr0u7nuEU05YfS2qO75j6iNge3NcaN3RfLzlk6s4JTRQ6PPR9+Mfhs9PjpZ1BwYGHVPjqT/d6KfRw+Mjhmtxe9HN46+Gz09OnLUbC7ax/+M5Nc3Im3s1+Z/fxndKVrG2aKnRpXXP4vuFx0janY2/xBpk98QHc2JJXDA3SD6YKRM/SZ6b3TZqNkcLha9IvpW9KNIfb51tApt79Ujfe1XInX/Q9HVombjuHz0hUibWW3tV6MfR2+O1jPI/YPo9ZH6px5uK1R8hUtn4e8Xo6mBdr1IQ/GZyJd8XKSANtuXf43+N2KU/5MTK7hZ9NromtFFoodEysG9oy4Hmw9vwr9F/xVdIbp49NhIHtw8WgVvLuNdA6cjeWbUhvyBwczVRaM/m+tC0T9GOpdrRYuQN/eJnhfpnC4RPTGS1zeKmp3LBaKPR9+PGOirBm7KljZZu/CnEWPxE9GnolNHzcbCo/606DGR9pfd9MroF5E6vgx58fLo/pG+8yqRdtjg/vRRszEw1LWv0lhbq05cKvpc9IJo1aC4uGNkkPbr6N+d2E78VcRIf1/01kiHcY2o4Fl6UsTg8+VcZ0RMDThee1NNdFQnJjh38sh1IR0jRkLj6wwkHPM6NBuP6T2N/r2iH0RrGfLySx4VOpj/id4djeebzUEdVHdGA1x4m4G1RmoV/y8ym6Zxe3b0/KgN+YOHtpORtiycTbsqr8epYO2h2TD51+xc5O9zov+OPhKtMuSPFQl3HeEd/ml0mdlRs5GwTdgkIxeOfhj98+xoMfrHqWNUPv0qus7sqNks/jwyC23wtBYGVercfSNOr21pyDPOTeUx6Pz/lKgQa2R617Td7aKpIW9q//aREaZr9NzoTBF0SEasL47qusTlSaiwAAXaVJURr/PuMbh4RiSso9k4GHE8fLy08oh38G7R3sA78cbonVFP9x8cxP/9X3TP2dFyDLDlF9qQP7gImdFO3mV2tH7OF30vuvvsqNmJ8PB+MqqwKv/vbdsq5JURIi642XzYLSIZzIbsDTeMfhKNDtNm49HfCWlaK6yGo0zdeUlkBptX/gHRtqIMeV67P4lM+3w2qtGn0aPrDOxrz/9nyPvyRqm+sHOviu4aPXp+LA6MZ8k9Bgne30CBTB+6x8AA148Y7s59IGKcaMgcGxl1+MbGccnIwMzUE6+uWOu9NeR59Hny5b1y0BwYzhMJb1Jv3h69MLKQZz0w3g2w25A/OKgnD4oY8mdwYg3OG8nrv4sMmM28TD2Czc6Awf6WyMw31NH1GPIG6AYADMoHR2borHfqNnlz0G5ySgqLEyL30ehhkdmRvYHDUvy28KhmczBbws658+xoNdaJMt6FMqpznCbb1pA3cuG9U8gY1beIIL7LNNBfRFeM3MuQB4POl/5SVF4Ahbq870aeMG0xrtQWn+06zzCuGwndYfzbQQfiQd3zuqjDNzYGsyMMAnF+GntpvS+GvMEVTz5PYXPg0FlbxCPt1Q0LkNc7yG1D/uAiDOLbkbq31g4K6ubDI3nNcyevLWxuA21nwkEm3KoGeNro9RjyvPcfjmo2XF8snKDZHITD8dpaTCmOWpr/bbQ32LTATCqHZ9fnzcNGA9YA2jlsLQygRX/oF4URb2tDXryzEb7pHscW1p0/4nkVP287nqtGrumMcKXIMcOfse9/Brm/xJsE294ZuYrfq/v9fWQEhrxjRnsZJka+zonb39sRb7MY+WXGowq3GROG/KodNKbU4uj1jHSbjUUdlGeMQh4hIWp2sqm1JatoQ/7gwlMqnvbSs6O1sa1g5bUYXJ2LTQbWk9fN9kE+C1O8w+xoF+qpWeu1sIZM/DVxqtidQ5twuajZeNgmHJLqpRCMJ0QG2kLl1mOUG5hZIGuQNo2bbzYO9qaoEiFqa2EzAQ5kg2IYrKlDNvLYVpQh/56IIS8m3YjTtISFN67xBMIUnuMy5CvUhkfACPMRc9nVxIjGQECBtYjAfbb2cV2D49ioCWXIvykq7zvPgnPubUN+/7H/relb6WmQZIbF9mUMeQXeiu+1Fhfbfosxsmixc3PgUQ91JMIw1qIN+YOHNu0dkfZvXxfw8xqpe9YsNTsHAzzrw7TF2mQ7omijvxyZAWc0rhcGjB1vrH9aa9an2X8Y7zZ9EGKz1m/rqPdmWqxhECLZbB7qlN2EbPCwCnliO1HrP6vucWRzXusn7YCz7HeVthyjIc/jp3Ba7Oocz7nOo1bBl8FdoTWMP4ag/TuXNRwSw2t4GOxdDlvmOfeo2dFiQ17COteG/MYgjMaAyvS++DwSFyaPGYMKc+0mJC+nhroRqxhMncS+GiPNvqFOLvL4WKCjfvohjGKVkW7xD2O+ObDwwssne/kvQn2r/F2W19pKXvnyHDXbH/nMSSZUjuFe7bKZazPcQgNs/Qz3jnV7kSOFMSlswyxdO1o2lmXpyTkmLKoWVMqnRf2j34HQ37KHms3DBipvjV4TLTLC5WPZqgbJIlE4ra0NVfcMtIRNqYMGaAbH24LRkBcjD1vWVYjM+yPTf5ga8gxz4TDOGcHYB9cvnolvF5pj+knsvPfSKFloqVOTYF4zDa1pQ37zMCVvYeQfRX65jC4VGbmKtXZeR6HwW0TJe1B5Ia5PObDozn1mbpQVjVd7dzcf07m873ZHMGMm7c1Y6RheHVW4BW+txqcWkUOjZYAuRONFka3tzJKZQlxkMDYbi/rxrEhHsegXHeWn9UkWwsI92ldbplncKq95inQ2L4u6vu0s5C8nix3Eql1mhNhu1M5U9QuTfxPZMrgMQW2BdRTqvPdQVmwnrM/s3VA2nj+OhNKoixxeZJAlvcetCoUf28u/nJ/6U7tNGcjzFLNl9JsGXev5Mb9m7/A7K0J/a33mFBupCBVnxxhwaX9Hm0j9kld+8NKalW0TyuiLK4y8rbUvrS/ESHDevppFLUD1ozI1QrX7yfirr2Q0wyugU7IRP8/heF2D5K+Ftaj3Fb9dhZuR6ZzRbm2f12wsRpvS2OLjwuCsRqY1gOMNdJ9FzSrBxyIzLGLLxJg1m4vYyvLoCGOT9uoYz4MF54VORj7VQBvWNBiEicustSnqOqO+4zQ3n8qTZTGXBsmui5M2sNLxW9hq5kyIo7ZSXvP8ubfZ+YijVs/HgXZtyyycFXba0O8qG9pj7bXBnvt6sLfxcIIZkJu9NsjyQ0O2njTLOYbV2IpbPtUP9XFuMiydY9+YFZdfn47YRev5oaJmfTC6RRbIm4oumMJBKS/Un0UYZLkub7YVCuhNIgshaypCQ2BE6fzY2Ru5OCf2aGxkFGQjUSu4ied9bEz8z8PkF0L99T62cKpfRKv3tUinXmfawzlbAnUox+bAK8vTw9tQyFd5byBVKPQ3jexkdJtBtiZdz1Z6zcYglK3yQP0pb11h4MVLNO4jzdC/beQ1VT/9775lP0rUbBy2mJPmy35t0yCNd3Xasdg+rfLaNpQVltjsfP4yUibGcFXONXH0o9HI6NDvKiO3ipSZZvNgh+gX9XvEXpnaJmZXRDTUVrGcmQZfbB+vkVeltm02Foa8OqL+LEM7a8Zq2WyIKATvYQDWNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3ThGNFN44eHT0mukN0iqi4SvQf0SMj1+le0SWjZntxouhOkTz8z+hS0Xo4RnS7qPL/RtFRo+bAce7oQVHVvzNF6+HPowdHXnfn6ORRs/mcPpJf1a6WrhCtxXmj+0bu/7foDFGzc7l8VOXjNtEJovVwiehRkdf9a7TeNqHZN9g8ld5/H50kWg9/Gj0i8rp7R+eImo3j96ObR6ONSv8UHS9ai9+Lrh3V6+4ejTbwludk0auj30a/i34T/SJ6UXScCM+IXKvr5P+fRc+LupPZHjAE3xN9InpZ9O3o/6KbRgryMjRWL4m+GSkr745+ED09OnbUbC7y5m+iT0bvjN4Q/TL6bLSqQzhSpEH6TPS66F2Revv2aL0dULPv6PSltzr339ErotdE14yWIa/VR3n9luhNkff4dHSWqNlZcIZwkn0hem30wUj/q7wcM1oFx8o3og9E2vPvRdqEi0bNxqItvU/0uUj7q05rg/1//GgVN4m+Fn0okq/63S9Hl42ajeEo0Tui70TqgrZWfXpotJYhz7n5X9H/RvpXr/tqdJlo23DHSEfx1uh0Ee/8lSLeoCqgT4zc84TIddJJfTRy/s3RCaNma/Nn0e2j40YMBp5ZnYDO48TRMm4QfT/ivTfyPXLEq//r6C+iZnPRidwwumqk43esE/h59LBoGeop7548kmf0L5E6e8uo2VzUl49Efxypb6T+rEIeme3SBldey3uOlvtFzc5Cv8mze/5I2WCQ8KxrW1cN+DjgPh49LvIaZYs3njFvprXZWDg19Z0XidRJaS5yQb1kqC+DDcXof26kPsunU0dfj54ZNRvD0aJXRWYvx7bW37XQrv4wunSk/fU6DsptFXHwnEjHvqqTYMC7Z9pAnDbiKXLtVk402w4dweejVdNIvLqfinQehULPs2DKsDnwMAB05GbL9gZGpXx7yOyo2UzUEV64P5od7TvqJg/9C2dHzU5HuIwZz7+dHS3GTByvrnCCggHy3mjV4L7ZOAy+vhtxai1DeJ0Z0bvMjg7HTNtTd/3bbABlyP/j7Gj9mJk2G2a2ZVvD884QN5UgTn5RqEQZ8mI9p4gXc+0ps6NmO6DBN/IUh8uIN0BzvIwLRTw9D4/Eyp8qEmLz0mit6d9mY5F3Gq1bRN+KVnntFiE+m/fBtHyzufxlxCB7csTL+v+icTC8XtRTeW2dQ7Mz4Tnk6dWe6ks5yM4cLcN9QggM8M4VaROsf9GeGwg0m0Plk37QgMlgSv1chvueFbnvgpF8Mhv6peiKUbMx8J4bHL0/umdkcCVf1vLI6z/lzYWjP4isTdmW4cJnjXj2GOP0xUi4jWn5YpUh/w+Ra2+cHTVbHQsfxY+Jl7bWgUG+noLLKNG5KPT04qjj4w8sfxeJda9ZMDMle4vFQD+KOt568zlPJG7TmhL1jdFlncl1ovVM+RZCL0zhX3x21Ow0hDpyprw+Uj4M/tYTnyscUniGgbkZU/Hb2ulmc2DoWbwun8S8/zS6RrQWYrTNfMsnbQAjXuhcs3EYXOnbhDH9T/S+SLz8kyL1axkiSdgzBmUGAeqQGesHRvJ7W2G6/bGRwlkGvYWMFSO/ypC/deTa22ZHzVaHd12jQtY2iH1XmNeKB9NBKOgW9+g0GJRGsc2BQ+y0fHt8xPNmGt26h/ViKlh+33921Gw2Zk+oOHtkMZXp+PWG25j9MvX77GhbxWw264ahIbZX3WaY/zgSU23N2ipOE1m/VovZzapbE8ML3Gw8DDu7oMinp0XaUptAnDFahVk4s9kWInOcsbOE2rQjbGOxbqGQVxxdbNNVoU93jaw1e/n8/+tF8spCZnm0Nw6XLYMpOiPOMuYtrgPDYZkhb7rXtY7f3H5YwKMjsLDqck4swfajZmrMvpjSFffHq2+Vd8fIHxx41C2k5J1fT7iGnaV4g14ZdTjUwcPCcfWt2tZV2E3BdLEF6Sd1ojkkuFqkjIihXhbyeMqIZ1hcsEWujHczN/piW9OuCpVsNgZrYAy67PB3dCcWYNbETjWcZtY1GIxb12CGjRd42eua/cdAibfdOtBl9eEekQGw9Z4j7BtOl207KDYCMTrRINRIxgh0kSEvoUwdu2Y3jGb7wUtr9Hm32dERMcrl+eV5GLHjDU+h2Zrm4MCLZwvYtQZTPH6MQeLhbQ4ewmrUN3sWr0K9swbF7hZmTJtDCzPcdhNbtrXhbSOexKk3mHFoPcUfzo6azYatZEZk2W9zXD8yKJvG0Yvj5tE/2+yo2Qz0e2ZBOJlHb/2IBeVmSEQqjNi28mPRtpg1MRq8WMTDWgj2F2fEOOeJRYXW+HIFb+4DIuftZTu+R7M1OWc03T/cntXysDyEBnLCaMTSw0hW3Jip/RGeBuEdBnnN5sIrYMA1nearvf1rxyGxmLx544/CqM8viNTp/iGoA4eYTeuPpuEwZjetUagOXBv819HY0TtXU/HqbLNzMZs2zWMzMYxxnt7yJArFEo9d2zxbPC2Wftrv2pPe4E/YTbNxyJPpgFr4BmeWmZHyrEt3CyhrBs2ASyy9aIcRThi/4aKNaPYfDirbeo5YU8S2GdeSWbdkkXF52vWr8sFC8UK+irHXv1qcvOXRKIh7FvNsZwXyYzG+vBXxtZG+KT7nGHR131vn5xgSe7tzRnNwEDZl4Z0YaTto6Ay+EvmRmsprAzTTUTqD8gbZ2ciPlFg4YmcjcZhi5Y1k7avbbC46EemtTpolkwfqIM+umM2iGi6/TIfaAcM5vyrIC+yHpW4WWWzVsbSbBwPMOiOhDzpzA2Zxzz+Jxk7DziTyhzFQMfW2QnNOvPS1Innm9f5f5qFtticcJjx/tpEVcqFtrbVL4y+nCwFQJpQB8CDqey2i1iYI2bKDnN3FbCXd6yk2lvNFtpJVh60ps/OMuq0+Xzkq7AYmn2rrUIM0YakMQ6GpPPQcosJSLXDuNnhjENvOlmWrSGO/nmuQJTRxDD19XiR/LjA72uWpZ7BbGMvg1876QSj5U/dseXiN/LCML2uajsTR6njGn4g2DSSOyJet+ySaxQHTkWazdRFXyZg3IDMdyIg3FVseXWhYFGyqWGqF3Y4pDHyvVUE0YmZzms2HcWdhsZ2C7HggD/w6rw5lnDLkMTIoFysLA3WLq3Qk4unlOS+vmRT5pzw0m4eFyNJfvZHmZkWuG40LYHmSdPI6dzMu8tMAQN0c80ye242hPfQ7C94/ZULZ0K6q3wx58dcjBu/KwtjmahP03Rwq1soYEPiRIu/ZbCw8s2atOTq1p2QQNd1C8uqRunr52dEueIG1t5xj8omNxXbqxa4bB2eXAax8kcbywPrN6doiTpIPR2NImigFM6VmwbTTdmDclht5MOhNDdGiWCLepbpeWrZ4oNn6yD+e92UNvjKwKH/rdbYm3ZaruXcA0l4eLJrykyfOl6HomGfO4Eyn4XXkf+8zGpTN5iCNpTctazPlkTa4UP8W5RmN9zU7B/m6Ko+Vo0V13r3VJrQXfvMZ82lRfZZP+slp/+j8qra72Ri0m9J42UyHPFtWTzguu41tmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpVuDni28YPSD6Eyeapmmapmmaptk7zhC9IHrbXFeKphwpemD0zuit0W2i/eHI0Tui30W3cyJcJvLe942O5kRzQJHHF4uUhbdH8vom0VGjZmvzB9HfRG+M5NtLInnZHDw4K/41UpdK2tc3R2+Jrh0t49KRe+s19XptZHNocYroXpH8f0/0iOj0UbO1OHF0t4hd8+7o8dFZombrcPzonyN9pPr06uh80Y5A58CgLr0rOmY0conoJ1Hd8+To96N9hSH/2sh73dSJwDvv+PPRyZxoDiiXjz4XPT8ymFIOfhndPtqfvG42F3XpPtFHo0dFj4y+En0n2jGN1DblmtG/R/eb697RMyPtnHq1jJtHP47kp7y9f+T154yaQ4cTRC+L3hspR8rO9yIDweNEzdbg2NHzovdHHJ5PjbS/jhn4zcHn5JFBFvvyiZH69Pror6MdAUP+V9Gno09FP48uGo3oTHQ+T5r/fWy0v8ad0ZD3utns6LDD/ji6c+R52nA88Jw3GvPdYO5/Io2RkWyzNTFj8leRmbXiItEPo4fMjpqtxD9FX47+aHa0mFtEPPHHmh01hyonihgaYzm4S8TBcpXZUbMVOF4knwy8CnVYPpkpbQ4+D4/+NzrH7GgHwghgUJuWN23nf96f4lQRb5/Oh6G9yJA/T8RL/6bIKOdvI1PLI9eIGO+vii4XvSIaDflzRQYKt4p4GWEUZSrE+3q+J0TnjgrT0y+NvN9VI/c43jGjrIMMb5Bpwvb+bC/OFH09Gutxc/BhmH0s0n6ugkfe9G/PTDZTbhCZrdHnNVuXK0Y/ilaF0DUHBu3oZ6J7zo52KGXIm64TXvGDSCxehdcwwF0X82WB6tSQd/1bkcbFtMX/Re55XFSx7u7h6Xde+Ib7jVZ/E1VozdUi13VgR49OGpkKYZB4XwMJ180cMPphqtq5n0ZmE4QUOPYsl42avaPS/Q8jAyzpzrPQbH0MtjRYjHj18+PR+aNm68DBYabkT2dHy2HIa8c4RV4Y3T0aHRjNoYN+lqdXrLz6bB0ZZ9Xo/W0OPhyXZq45H9knnJavmx83B5crRGxDa4/OHGl/LxCZSdkxlCHPgD5lpPCJh68RPwPedcb4lef/l0eJt/6Tkbg9oRlgDGps3CfhGPPvmx9XXCjj3Wt+O/8fRrDu4VU3lahS3DYaF4z8V+SeO82Odk1TOzbakjk8+c+Yn/uPqNk7rhdJyxqMPSdqtj4WKv9nZOBlcCzvGIPN1uEY0YciM5EWJ6/izyKzk2I5/f1gxBHCkdIcWuhj9YlfitRrbXP1tc3WwWzby6MvRvLpZ9E0RLk5OFw3ElYjwkAf+dVIHnGUrApx3FaUIS8e2qINxrbjR0e8ABoQOk1UhvxjIlwyYoxb2PHcyIKPZ0WfjdzHq2uF/S8iXnjewuINUd2DMuSdH70NphIZlN6fN949fx/BKnHHFvgV3s85C4OavcNiOl5DgytGhNkZ4RmMkGbrwpC3tkTekZksdfDqUbM1qLVIN54d7R2MObOkZiVP4kRzyHDc6PqRei0+/muRHTesKWu2DiIYhNHIpztEX4g4MC8cNQcX4doGVpzS7Ex1R14x7oWE74id+cqQFwvNkLcYQKgKT7spXdeeHpk6utb8uAx5HlzHDHUe9u/OZRqDZ5fhb5pJB+b9ePxhuvCVkdcuMuR5rIQJ1D3e3/tWeI6KgjLkxc4XOkrnnjY7avYVsxu1k5CQq2b7IDxKJ8KYNxhvDi7aTs6ID0f7uouFrSy1sW0YHNrw8upP7YxiAN9sTcyaCKOzFXBvp31wuWX07ahCsgu7gnEOm03Z9pQhz+Nj9G908pr5OV9+XHltisL5MuSFzvDIi8dd5rVVoL3Hej3ypjsUfPvZO/beNf1h5bFzU4/8aMjzJjvXhvz+o2zIX16GZnuhkTINf7bZUXMw4RzR/tnWdV+xXZoZsl73cGhjUGjBtH5SGGuzdRGu3JtFHHw4Ir8ZcSyP2EhFtMmO2FRgNOQrpEUMunPEq3e6CGXIV4z8qSOLTBl7Rj3FaSMGumlghfgDkdeJeQevuU7JuWmMPAOfIW8XGsd2uYHQHrGizq3yyLchv2/Yzmy6T/XDol9HFoY0WxMeXvVyXLhzwsgAWCfSYVEHHz/owztnodUUzpN7RBUGZVbU4qwxPy0+/0QkpKK3pDx0sMB5anxYP6F/M7BrtgZnjdTfcSc/eSeCQDjHdAe/5sAiNFGok8iSwiJkUSPCwXdEH6nT0DAIfan4ywtFYoqcF2teBZFn3jkJUoWWAV8/FsXo9z5GOfYfr72tLbwzHWghnuvfiMT6eU39Suy4aw1D/uyRhQnOSfDatYbuGkEH6VhcfiEeyjm/UNqsH4uD5Z/FeA+NrLoXziRGvrYDbbYewtX8WigvnXppbYsQDutW1O3m4GJGxMyINT6LOnTXtVfWNbjOsBdS+JHIzKfFrtpBU8AdVnNocalI3ydMzmJ2zin9puNyrjUHH4MrjhOORnX2KZHwYufYMc3BR2iikHF2jTWVHCPa1B2zr7xff+T1toe8TgS8Pg+OnFdIi4tHzk1/7VOIDSOCx0iHJATGKHXsuP5fZPW9PeH/MjLdYRVxxV8bPHhvXvajOBGcs/2a9xQqcKPoRZEddHCdyGvKQw+Lypyz532zfizWsdWklfd2LpKGFhp3HObWRh2zoLx+qY7s3NRG39bgYpFQRX8XYUaFgTb+0qtYTp48eWmG0sB6x+yu0Kwbba+YePHw2mRlwbols9PN1oEtZNZaZEDVWfbU+CN9zcFFPylawzbr6pKokm5Tm6ZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmuaQ5w+iO0SXnB01TbOZnDz6p+hcs6OmabYLJ47uGp13dtQ0G8DvRceKjjvXMo4e1T3+bw7nGJE0lJaLcE3lXZW+U24b3Ts6yuxoT44TrcoDn+Pzjj07WswxI/ccf3a0HJ9/osi9jPVl/Hn03eias6NdHDXyuhNER3JiCZ7zJPO/q/D5nntZOkO6+EzPfDQn5vxZ9OTo1LOjA8vxIs+kHKwH9z4hutTs6HAqz7zfKuSp+5TLZawnzascKW9T/l/0kGhM4wOFdFxVFiud1ip3I/eNbrXr3yPg+yvLy6j0XvVM3mNZWhbyyz0njBbV+5GbRd+JRmPA53v9WuWjyuOq54WysaoModq26TNfK3p4tNZnbEfWan+VB+3Per+78vrIaGw7R3zekXf9ewR+P5L20/Zuyt60Qe6R9967+OvoMdFaZWt/qfK0Vr808m/R3+369wist+7Kg2VUW+neMU1G6rnX08drO78XXWh2tIuq+2t9b+/vWVaVLd/FPdO2xrNXOSi5T/nxvlV/rxI9KlrVVu0PPsdnr6d91td7tvXUJX27Pv5PZ0d7ov74Psvyr+wcWlbXPItn9uzrsYE9t7wY7ZVbRveL1tsvrZs/jj4SMcLoNtEUhfTF0Q8jncd9omYXF4j+J5I+04IvI28XvT/6YiR9pd1ajeFpos9Ed4vGQqCA6cC/Hv29ExMULq/5avTl6CuRgjMtvNeOPhW5R34+MFrUkDHOXxd9I/pa9LHoHNEi7h8pR8qK59QYvDH6QvSj6BnR2aIRjZcBy+fn8rwa5EUdkmf5cPT0aFEl0lhfI/pg5Hv9b/T6SCOF00XS5R9nRwcGaSFdvhl5Jt/xBtFaMCil21lnR7s6kltH742Uo+9H/x7VdyscPyCSV1+K5NeVohFpro5/LlqW5v6/c+Sa9/F+t4/Gxuey0Q+iS8+ODhwMis9G/xFNy7Vyd7XI95Z+ysDzo7NEq9DwK+NXnx0djvrMSJB/yvMUjfp/Rq5Lq09Enm9EubxTNKalmasxLdVxM1nvidzzrehVkbZlEe7X3vz37GhX3f3b6N2R8qGdNsg6WTTCSOAcUA/cpw1QZ8Y2ptBGKLP3nB0dEW1blSPvxTDRidb3+otIOZ2Wv+2M73ajSB7+gxMTtDEPiz4ZKU/S94bRovQdUWa16ZwNI4ws5Uv6Tq/hlNGzI+XcM8n/6X3aDm2Q96826PrRMs4TqTuefaw3F4z0X6teu79oG/V7nvPbkb5yrYHk+SLfbfpc2t5/jtRNZXmK8iuv1DV189ORfBjRDnom16SJe58WyedCX64+fyBSd6WROrPKCH5O9Jpd/84M1BtHbIjqKw2A5e2I5/3XyLO6TzpdNxrbQG3N30TKn3yWLnePygj2TNpD6atMkffy17krRtAeqs8G4xuJ9vkKEZvC52qnnhetmlW8eeRZ/itaNVCFWQ7f/Q9nR4dz+kh7yQ77IycmnDt6Q6TOyuOXRWeMRtTFp0TqmvaTnXGJaBlXjthWb4r0E4WyqF0cB3Ebgg7519Hv5pIQUwNBJ/bLyJdwD2/hspHNoYKG4o6RzNfoMFpVthGNiwzX6PO2PDOSfv8SrUo/mc0YYdAXChYjVkOhAtwjGlGJeRXlEePDe2gQfhGpDIVnUsAeF2kIGLaOHx2NI9HLRBqVl0YaGl4EHcIio0hD8dboEbOjXRWD0emYcaRh870ZJ2MD5zv8NDJCVcY8k/uka8H40Bj5Xp6T8bKocWdUKKOMCd/Ld5YORtiF9PMMazUIG4E00QnrYDUw14meGP0sWuZ5A2PhJREPXeF+gxihFP73HaWTjq4MJ5+nsdPIGORphH1XeTga29LcM8jLRWmuDOgwlGlGn3LEKPx5xJgvlHV5bBB4IGCUeg5lQMP+pGhah3QSvq/vpNwZoDD63xeNjekU3/fN0ViuGNGvjXzejyNlakQHrkPWYUrv60U6SXWzwsukpbJbRom0fFAkLXX+BSPJ5+jU3HOLSLuhg512StAxMS5qBkEHbBCtXWGYV54+NCovm8GvwbSOyiDNfeq2slAdOLQ5PK//FxmoSfMp3lPb8pNI/iuTvo8yVJ+nPL4tUgd2AgyAp0bSRR7fKxrRpqizr45uEmkzPxT9NjKoWcXjo+dGo8HPSDCwk1/KxtRoOEWkz/EZyoEBg/LKeDtnBHnAs+pctUHqzbI2yP3PirTJv4rOHxX6F+/PkN2Mvl96+Z7Ki+dkhKvnyvCqz9O3vj0yYCnMUr0y0lequwZfI/ogecnwVmbVbd/bvZePCueVcQY/I9+AXHsu3evzvDcD8S6RNleboO5J70XPfaro41G1t38Vadv1kdosZcjrHxtJc/gr35Q9/bX7XhjJozEftc/aFs+rbZd23ks7rmxpjy4eSV+vo6tGDFTtpmtQlhm2+qu1BqF7w9kjAxa2hu+gvWJ7MnYXOTc5spRv/bo0WjWoq/I5Opn1jdpTgx4DOn/PFI1wLrK15KkyyFGqXzMwK0cII/7lkXzjeGRDvSJSXrXdU9TNt0TyQn980qjwXj5P+d5QFCSJKcF8Af/rEEeqcPGCVSGbFlINOI3nJaQOpBr3EYVl0fntgkLPSFCRVZxFhryRu0wtNJSMChobnhHpx+BUUce0ZHhpsBjY/qqcIwxsDTDDYURn7fN8tsKuMDo34jVGieUxl2fKgpHzaNwvQ2Pgs6vDMvU0HdFqFHxGhQIwRhgqDIIR6fjRSIGHBonRUp2Q0fK0QjM+GPo1kFiGxljFWubp3Ej+JNLQTsM1GDcayWWDCXmgwdEAFYw5HUCh3rwrYryVgVoDLx6ZQgOis2IkQDpJ82k6SXPvpfwqr97HoHCEkemescFVBjWw04H/ZsDjrFzw8PKuqCPTNkg7payP5w3oNLhl3ExRDzW644BE5/WCuXjYfS5DaeRyESNNh1TIU44QXlJIb0afujuis1cPKy0N0LQlYzoyKLz2L2dHe1KextPOjnZ9DhWe38CEV7U6kfKQj+XRIJlH3eC4yiPDiGHi83WM44CyuGjEGJyW7Sm+l1kK8fzbHYa7OsfxpbxM81SdVHfHtkk6SXMG1TJ4XtUrM7eFtppxY0Cl3dP+TcPszLTqqy88O9qFumvQaLYO2jltkIHhyDsibfu0DeJ9NoBkaGnPR0MejBjlZfRIbwScYsq/NmaEg8f3WeRFhZkos6763xEDVmVaGdY+aTtG1KnfRKMXX5vBBtK2lPGqjVGPRhjo+pBKA/3YOPOlPfE+DNZFs9wMeO1RGZTa9elg3WCQx7rafHlsUDPOwkszbY179dH6AQai7z7CjvBeq/LMd56WBwNz5W4jQ1E51c6869/daF/0N4vCYTiMGcy+41qGPINa2dQuF9pHbTf7hlNHPZt+vr6Q7TC2UZ7F4OGms6NdThp277j+T1utHBgoTzEo00Zo56eGvLLF5tGWyMMNgyHvIRkXGiv/q8iFL246RyGRuaMhr/HSWCkIMkPH89ZIYwcNC6OT8aBBgs6L55CBsaiT2i7InDJWNZwq/NSQn8JAZkQrlMvu9b4atenUrXzwGoVZQzyOPKHR1bma0hkxgleRNRwqqnSfGmmMQIZBGSXCWHiCKh9XoWAa0et4VlU0o28evjKifZbKcrHZ0eHodNzHEIaOrhohHZuKPf0cxhpPiUZ1FdJQOV0WT7mR8IIbkUvLEaEvjJvR8BrR6aj8U8/BiHrHyBxnzxgW8nBsNCDNNMgGcYzSVWnO2JWGvFDjQALSTNkx9V4YGPmO0/fbDDTK2hPonHRYU0Ne2mprxnLAA64xXzbYUB415AaMI9JCmil7FSIxonFXlniZRsyWaKSVUXkoLafefJ4zbWqlpbSdend0+Ka7p0YMI08bq5NbhnRhhJgtKCND/ePRm5Y7zhn5Wml7hshAHKb/px517/3giLEifVah/VA+tC/bHXlp4OM7S/8yllehbGiXFg2GCgNT5a/aO6jfwg20rYwKBsrUkBdCqb6XwwOejdHDiPVaziZ9gj56xKDVQHIsC/plTgaDM7OvBqlTQ/4ikfcbB68bAQPPzPbUCaUv8x2n4WqFmV/XpyEgwj/1teX9nhrynl+9nC421VbyxFZbwc6ZDkTNnghhWmSkQ72XBwbDU0PNMzGYp8b2FJ+hfaj8uXPEtlI3Rwx0lC9GvFANbfs4uwZp47XsvEXIU23PdNaiys70/TYasxza0WkZVe7lnXph9kRft8q+MCOinowhSQZVFQqsTMvLqSEvSsLMVw3eoA/VZ5STUVirgdQ44NIOviiSn2NZ0E6w76Q3R5eB6LRPltYGymNfut+UIa9xKgNOAlZIgukG11V+HVIZ8pAovIfvjHQcDHQNt4eXARJHo+81RigyQqVyLAHWWtixXTD9vB5DXkEwzcmzvMzTrXGRB1PDoZAv0ntqyOs0eF+mAwAeWo2k2En5Ib/k04gCpUHgcQEjQsXhCZDnwlZMFy8anftOBgqjR2kRPCcaySpXRrsMi6khpNFWyBcNImqEPq3QvP0GEowv7+t5Pfc0P3RWKq2B6lhxNwMNpM6wRvUFrwpDfdqJFAxyhncZU4vQsTDydAg6ffhOjKtp58GA09lJdx43jfayNOe1ZfhpwIWbjDDslcuxQ9CReo4xdGuz8X2FHi0y5A2seeSVNZ0175kGeVEYQSHERDlc5BGCjmCRIc8A045NjYzKP3nEOJau0zhznSvjrcq4hl75ZSC4puPgPVrk8Vau5Ocq41he69TGuFJtlDo9bXMZCb5/rccolCNt9NSQV/c4fXSu6ptnJelRZbHQP2gbxjCi7Q6jicG7HkNeGyS0cdW6GGmjT1jmdebsWmTIa295laftiH6YN5hRzxDXBunDR7Tv40ASHD4+R74r68r21JBnWKoLU4N7fxHqYjA5NXAZp/quqSFeMDIZWMtCl5S/RYa8uuP7qfsj2jzltcJHDaYYZZxvnBbqsTZ2DL+ZIh/VTzZShT0WBuq+z9TZNqLPYnAK5TBoh75e3kz7BP2cGR+f6TtxxowzNGAgLwoNhDLC8SBPp44ODgwOxc1eU2aGnZO4HAnQjggLrRkaf9cy5A3CzCYt83ILmVlkyHtd9Y+FfkUd5yyD+qu8TB1W8lh5GAd60pOtCzbzIkNeX8PeKuf2hlCGPC+S0b2HN7Krxkfcp8ZAQVFx3OsLMIQUOsb/GPes0LunjELGn8qg0THFyEtkhDgdgW1n1mvISxNpuWx0DAaITn4a3lQsM+SNCuUdI9FIUOOtICloDLAyVDRWRsAMP0amRtmzM96qwWP8MfgYvfJLRfPcDPGpp5iBpyNi1C1Dg6xDG+P6GbQa4WnIg0Zubwx5lc6oWsVQkTU+DCllUGM4Gi5ep4Ew5TVtZDcaZYEhJP18b/nB+6kx0WlZZD7F4E4YjPwd69QUjZLvNxqROkH1bPo6+a3zYKAzDMWNLktzxoJnEC/qNWZuPDejhYdJmRhn0Xi+dZRCKA4UOp9lhjw8k8GF9KG1YhFrqnyaJsUyQ156GjTIT99fXWL0ym+DB+2eDslz6ih4z6QlQ19aMp5H44yBqH7Vcy/z4qqb6uOqgZ6QAe8xDmB4+XyPCsUqDPz3xpAvz6165nt6T+XZ57l3rFe8Y8rkNJ58O7NeQ17e84wvMsAKfag0U4ZGY2BkmSEvjMfsk7LEq2kwzYhQb4XKyT8yqJ22QYwir602mxGlLFcITrUvU0Ne32OQsCpUaF9Qj9UPhqhyry553rdG2qUxXHBEH+L7LnOKLDPk5Yc2Vj9hZoOTSoiFMq08j8YeY167V/VyLYeVQZKQp0ULIdW1GuQvw+DC54ztDc+wAcbUIPQs+lAODHVdvz5d7GyQ4/wiQ14Zkr7alCk+S5kwy7lZGEj6/GmYmoGSfKuBJofEKkPeTAe7QCjgNFysWGbIq1fKndlH9hn5POc4K2AAK9+0ZTz/3ostot9QHyo/ze76jLLv1JNFhjxHmrLGAbJhlCHPG2QqokbjjCONu0JcHYfpZ9cY8tWJ+pJi1HSevphCw2gfvXSmEhRuryWFY1EnvF1ZjyGvQVDpNBirjEiNlnRc5mVYZsiD8cA77fUKPi+fsCeNUxkqRvmMOw258/JdXvPQlIfC9zGYMB3F467z0qEwAsf4ah0Ro9iomoG1CJ2J5/U5Y/pohL3fdBccnj0DvUW7oSwy5KWlhU0qPuNfpZJGOi0D0tGrWYYyj+KykftGooEVLy1t5YeGxvNrEBbFLFbole+zrNFSJzUO0mKc1eGJM5CZrr3QIfp8g3RpwWhbK8117DwVVY54GISzmM2xS0QhrTX203UOm8kqQ175571hgPJ2WPSpwWTML6tz6pHvuMwjusyQh45GfjFk3SNvlS3hhGW8MfgNmse01FEwzirMzGDTswqZ8znCgTy3ejh2TOoPhwtjbBm+h0GtOj16yBnyOplFHnkD/ekAfZkhX4u1fE/eP99TRyUdazBYqIee12fvFNZryIthlx7TkK0R7SfjmzGwLPRrmSEPxqLyxMhTttRR7az6oS2BfNVGT9sgDp4K3dBGOFfog/TT05k7TgL5rk1bNqO8r2i3lKGqS9LE57ArloV3MEDVk2W7Ui0z5KFeC43w/tKFg0d557wqw0z7bTAmbRjKFvrKC7Moi+wXbaN2VL2dXlefDEwqmmER8kO/PM6kQfvqc8cwKjA69QWcF57PZ0898hytjNJFs5Ly3WsWOeEM2Nk0+pXNsNWUTwNOfeFY9tkn7Ak2SqFNl4/LUC69F6fzsnK5zJCHvqKcgJwT6ot7x37NAEndKbtJHdPOGwDoq/UvQoz1mwVHKjtqamvo+7Wh03WO+8XUkPchOnudgYdyrQL6eV8dV2E0Ejbar9eLDeOd5b0dDXmLBBj47mPQM7J2EmsZ8hoE6SR9pobWFIOefTXkC5Wk4raM2k3TT7ezYhCpNDp7lV0BrVXrOikj0OlIUgOj4yijyOBAQeWFWIT3V+B9n1pIWxiNilOcbj1VoQ6Ltmda5pFnTGmER1QejcTYcJYhr/LVtOWBQH7U52mgdBaLOu61DHlx4jodAzQN7YhwLYb2tHwpJ/LS+/E87U2a13MzOHSGGrdaYIky5JX/A8UyQ17Hx5BhAFRcODSWqwwqsw7SdF8M+ULaVv5qKzX40xjaMS15HL2nQbJjr3GsnSjUW7NkYzvKoJnurDEiPxho2oepF5hBzkiaeuQNcpyftg/LDHmvV6/GDgs6SDMP485GyjhDflxztd1ZjyFvRoQTwWzPKvbXkIf3qPKnPnDcqBtTg2Zsg+zmNfb3+mztEqOfUczA04fz4pZRizLkGXernFH7g3rsOT0/x4JByqKtN+E599WQL8a6qx3Tv6m7zqnHZjRq8CutObIYxtMfPvQMBgWM0GndBzuLDbRsfYH6Kk/MsIxpDgN7Mw/T/li/q29lE2gbzHpPIx08p7Z9GoqnvOkXDPgXUYa8fsX33kjkMeNcezGdmdbOcvBxLklTjit2E/uJ42ORjbW/hjyUZ3muPMhvm32oJyNjXYOZa30ODKqFORkE+wzPzqPvu7g21u8y5JXfDaMMecaFzPOFJLJzOhIdf3WCKrbz1YjrkBwbzRY8ws7VNJ2OV8iHQqbBck1nM+7Csd1R0RhXiyqwUbSO1Wh8OqJehAJslL0sjk4e8dytZ7qake7e2rVkGQwJDV4ZgQZaGg7xuCPKhU6sGnFThRrSRd5lhd40qQHhuJCrYIxUZzFiTYX3HA3Ggtfy5dHoaYT0Z4yNZUplM8oevYHVOGu8NsPLsBaMIyP+qRFUSFedsLo4bbAYEDoZhhGP+RTeKXV19K5IJ42ggYv8YAzsbZrDc2koGdAjFl9J9+m6jM2kjBXesRFpooOdxnTqFCu0ZRHCElaFhulIfMdxh4tleAaez1UNtOdnHOjwoVNjrEx3P9BhqJOMpoLRxws0HcRBZyavDazK0zrie1q/NNZFz8LjqFxNvUYMP3VlnIGDjpInk0E3eg0rNniMh/ccOqzRu7bdkc7a8mXlSZup815vSILpdwbVdCBVcOiom4tCNaaYITLruywUBdp4eVIDNGsy9Mnae4YoeQ+z6gyTMf/1Xwwc7e2BQHnXhiuLixAjbjapZramKJO+KwfGWjCkGYs1QDOoVl+mgwCDHeliJqvQxmiz2QCM6kUYzLJ/pgNpsBvUQw63caF+wenFATqN0dYf6w/UQ7MBBo/SZKR+V2Y6C6s9441fNtvhO0n7adjLRqD8yLfpoEMfpVwqj8qhtpT0a9b/cRouiitnY0o/6b+srLBptKfLHDYjduEz6FoV/s1QF61S4THS2XPXM3t+6SvfDKTGdpEjTVmb7ia1X4yGfI0aeIGcI4ZHjQSnhrzQC8c8N0YxCoVGwLmqAHZ30CgYufDg8i64zsg7GMbURsLAUYhMdWvcNQbjiFDDp8OWhuU1cD8Da9kot7zcixofr1PBVF4VzHuVUS0tNSIaavcxBE3t8OCVpx3yidyjATEC1mCNDYARpcaKB9w9GgqeBJ7NihH0eh39aGgUnounU0GvQaD7na88FwYhXZQ75Uu6+e4Kf63BKCqdeQcMGn23MZ15GxioKouKzAhSsQxEx+2oGFpmHuTXsvTfKDyf/GAg+e6ML88vTaaxpyMGaCr56H1RL9UfXtsKkfKeVN9DoyPtfEZ9pgZJuhhww0BHmuuMea0Xpbn88XlVjhguvPrSUlsxwshgPK/yVm8kntd3M2vBGyKvqxyoewaNZmx8N89u4MYTo1FetuDQgFmbNe0kpYX34DFiaKmPY/l1zWf6DOf9rzPh3a7dJSotpWGlJS+PtKw1IF4rNp4nTifu2PeSd9JWTCa0JTxKyvUUZUt6KDcV+uTzvFeVDwvtdCjCFap81GB6GvcrTbURBhzaeu9T7Qy0FToo63hc834GVgZEo8Fg0KAdEWayE5Bm6qXBFGNkTBfpzIjXvmgTtUHVblUZXUQZo9PQpip/6px8VV68V5U/ZUSZ89d5zhRtKZUx6XMXtUHKQcWV+z483voI3n/imdZHMzSqvYG85WQajdiNQnnzfJ7T86q3nH/+LkO7r3xNveOVdp5XnRFSOKada9JO3+Z/xjWjWPtRM8dmy7y3MAvXpaX7ecG1lxxu0B8bQHNOVr33Wd636p42gJE+XfQO31VIBsOvwmK81nvU67VBbAJ9QLXLnDLsqnJceEZOO22VZ/e8jFZ1kiO1vjv05wbiBv3jYHzEZyonG7nDm+/E4eR9a1e0+q71fNJQPaqySG+NpI86sGzAa8Bp0KaNHfG+3l9Z5pBRJsa8kafqiHRQl7SfyoHy4HVwTXlRp53zDGYrfB6bApwWntuAu55bBIMyZKH0OEDTbipDq8Lu9hoL5lRao/4yHlReD+D8OCIX9uGcTkNCME4Zlc6RkRNvhP+NVFRMXhrHNfLT6df96/EybFUUCB23DFUwfR8jbtM7lWm8Ns5LW+ESvJpGjSrRstAZaWawM90bV7rpWHn3vafP1KEw/HQYDAqedx2JgRUvn88tI65wv4LqHo2c0TovhMI9YgEfQ0LD4DP9X4Y9FHiftWiqUAH1jAwojZzv67t73tHDwVhRPpQZXmNGrsHBuM0WY+DlEa+hwq/x8jw6y6rUnokhakCicWIYea/pQkcjYZ3DGK6wWWgcGJXCXXx/DZG0nG6VNsXAVz0atyLk0ZSe8nQsRzSuJdCoSyP3ucZAlS5j47ZWmitHZgUYpMoII4IW7aDCOOYFGp91s/A9Pa9G3WcyhpUnhqY6oz3irZHO6oZnFwLC8ODt1kgvQj2WZlPviPIvnRnY0l56KINllBqIehZ1SFqrU/4fp7ClJQN3mpbTRU6MNXVVHfNeBlryp4wIiHGVr4sW9pkR8Yw8QWP50DaNYTgGuj5DGimT/uf1HL2EyofyIG3VNWnjf8YdowMGhMq2667pIH1/jp0RHRhP2rK2bjuhXspDxpK0Vrd8d4Nc7a+0qRln7ZN2Tz4oI+pTdfhTGGTq+5hP2uKa5WUseE9p7LjWMZlBUr61dZ7DYEC5Gb3T2iDlQdka26BF8dIj2mifORrx8Kzyc9lGDPuKuqsP0hdJYw4Lz1p92zIMfuTDGM4Fs0++r/SoeiEfasBqoO+6z5FHPlf6jpsHMAD1ncq1tFUneHS1nWZb6rlq0CNdx7qnr64ZbbaPNmBRuKhn8noG9/h6bc1o6Gl/fVfP473ZZ2aWGZiFNtJ30e9qKz2rsjgNpazF8NN0G+EAk9dj/7K/eE+fyyE4tQvE+5dxPUW6+C6r0KZqz8YQXmkjvEV6slV8tvoibSpcS1nWJqvXBsH6Dp9nMFSYlVWW1Df5YvbK/5W/y9DvaCOr3SwM3qXBNNx4vzCFJI5UB1VGmhGIqQjnx7hNjYdzvFiV6DwBRlm8v4xNni3HOhyGFmNWJunUoEP1Wc5v5wae11pIAvGO+z4aXx43nTxUAp5p1+reUnnOFqHzV8ArP6BSe53P8J4+07HGpkaORvT1WfJgUUHR+PHquce9033ORzRAPs+9jKSxUeWVYViM8ciFRVKMT89Zry+VJ6PwzO5zTTmZFnqegXptpbP/fYfRa+3ZDDRdUxan3mMonzq16SKuzUD94OXSQXkm8bLrmdaTZ9JVWhQGvMvK0TRsSfpWmk/3gi90KKvS3HsyUFwXJjLt0AthARrhCsnaTMw41HeWDpUWPGQM+UK7U99NeVGGl3UQ8N01zgza0WvFmPEeVd903o6rc/WeOqZKa8+xyFvEsNJmukedWBbCo15qO9yn/BrwVb2G52OMLPLuSpsqH9P6No0vZjDWPTr0adqoN/Va71ftjI5ybI/Milmw7pqysii8wXmd4zhQ2K4w8nzXKg+VLsoJw5vM2vjOVedL2qplYZXKjFmyMVRSvitPXqss+Dx/HZfzSx+tjFRe6lN5tUfkrfur7K63DaoyOy3PHAUMGF7ojYaxZAc1z+n7rmUkgU3B8GfQjvWi+oHKq/r+NQiCcl5ppy9bFI4Gu71Vfnqvab9i4D6Wh5L3rvhv4SLi38f6XOj/lJlFdXfaN/usKgf6skWDHJ+pLLlHGaooixEDMp+5bHAJecHoXU848HphM+lPpNf0u3IILmunRYKYGZg6G0c4kwz+pEthIGsWxPsrA/W5Y96oM2Z96jkM2KezFPKNg6OeWZ85DqCWoa3lEBr7V30Mh6jBxPRzmh2GQs1TumyRz8GG8W6qcKN/wnkz8Zxiq2mVl+dgo3KLQVbRt/JzaiR5oBmn2xnlgqHFY1MD8K2GTocXqMJstgMMfTG2OtBmOQxQ8dNmEseB0laD0cy7u2x9z8GifpdjUWz5VoBjxozARu+9v5kohwYeB3I3sv1F+TRrUnu/b1X0MRx1B+JHKZuDDCPJlCqP5+gl3CoIqWIQT7e62soIoRBnuB3idXkgTOdOve1bCd5AU73LfkhpO6FsMJQPRMjVvsCDJt5ynO7d6tR6qe0cPnmgEOPNcbPRISsbiZhf4RzTGdWDjZks4SZb1TASoSC2fT0zIVsFsw7yelxfth3QtxvUHYgZ931FH6OuL5vlbnYYwpfExS2aSm/2Hjs68Lxuh2l+U3GmsbfyQEmogVi/rTjQ3BdMr64VO9ysH2EAjKtuv9ZGGlm/sJHxyBuNMFghKBb8bTWE0giJaDYGgzZhOdst9EPfro+vH5LaiuhjxnWCTdM0TdM0TdM0TdM0TdM0TdM0TdM0TdM0TdM0TdM0TdM0TdM0TdM0TdM0TdM0TdM0TdM0TdM0TdM0TdM0TbOdsbeun373i13b5ddDm6ZpmqZpmuaAcNLIr0cu03mjo0YHg8tGft3SL4hu5Z+u3s6cLpLPfkl2PZwh+vPootFpnGgOKAa354v8WuC5o/X+GJMfmLpIJN/8Km9zYDhrpL6U/KiOvLtQpO1dD3650s+8H2l21OxU/JiNMqKe7s2vgJ488ro/i07lRLOpHCfSjpK6uV601X6oSBtwrvlxszkcL/KL4+qFNOcMXovTR/LG69ZrD20Z7hL9boV+G502OhhcJ/IM74gYIs3GcuLoI5E0vp0TKzhK5OeP/RS2n7L/UfS56PpRz5YcGAyq/Uz/V6JvR/LtcZFGaxV+3v8NkUHxDyN5d8+of3Fzc1EvHhmpL1+P1B0/H+5Y3v1DtBY3iP43elt0XCeaHcnVo3dH6ujPo29GfhV3FX5187bRJyJtgjL1sah/nXjzuHz01kg+/Tj6XvRP0Vp9IKfXkyOv+XL0y+hh0Xb4lfHthjzSXv4k0uaqF6t+SVX/ee/o81HVI3lsQL1tOGN0rbkeEfkSjISbzM9dKTpYP8msQfI8b4rakN94Hhgp6L+KNEar8JPNKsZ9It6EC0cvmJ/jGW42F97Yf4meFPEEMeofG6kft46WYSbr0dGDovNHPMFPj7zur6Nmc+EhVV/OORfP+t9HP4iuHC2Dt15eGwAw1F4XtSG/Mzlh9IzoX6M/jhgQL4+0rdrZZfAGvzS6faRsGbAbKH4xOljOt50MT/wTo/tH2l9587zoN9Elo2WIaHh2xK5iT1UboA3mUGk2DtEF343ki5lP9eIqkTZ4Ge57VXTd6OwRBzKbiPPr+NG244qRwvXZaBxhmgISgkHOayT8z0tbnCwyZU/TKeOTRM57H14E/2uEFk0VG7m6bqR6hagN+c3BdBMDgQHPqFjLkP/nyP2j91cF4JHQgTSbi3o3bVROHamrL5odLYbXfer10aj9X3S/2VFzoHlq9J5oVbjgxSPtnnAJhsObozbkdyb60WndZhjy3q6aKT16NC0Td4p+EfFKNhvLorb0gtH3I/3jMhiX+kkDrpGXRWZQ2E7NxsCAF2WwN85ntuW0LX5MZEDAsN92GJEwnE0xjAX2FNH7IyP9B0Smhb4T1aj/b6LPRF5LCqdRUPHvkfMPjxgd/hey85/R+DmmkWsq5MOR0a//3xi1Ib9xlIfg8ZG4MB3G3aJVKBs8ROO0rbLw6ajjMg8OBtPq3TNnR+vn2tHPIjNuzYGFx1WozFqhbDqWiutUz94StSF/6HDHSBicAd3eIBzH61Z5iJuN48bRTyNO0GXcNHLPOWZHh3OLiBPNTGmz/4gu0R+uFZK2HoREmg3dmzUQW4ZVhvx7o19HphweHN0wsqOMQuqcmCKFWgclBkycH48teP68L5lCFBtacaJeAyNbBiUDw3QTo79eY4qjDfmNw0wHY8KiSXHy8motQ573R+gVD7AwDSNWg7tLR83BQWiGOikMbhVmvi4T6TiEUxl8qcPrWQDUbBxmVTgvtI9/6MQ6Ud/akN/ZmKlWn61DEjutD75rtLfrWF4dcaRZANtsPGZPzHbIJ22o/lB8tf5xGQZljMLpAmaOFM6xvR2sNYsRGSDGXd6wMa1JeEq0tw4rTpRPRcLbDlZY+X6xypA3FewaL3mhY/lAxENvsY4vLY7svpF7eXwhrtoxz2GF0zwkco7BDo2XY4v5Cga9c+2R3zg0RP8dPXR2tMsjvx5DHlZy/0/EeJQvKkovmDw4qEemZhnla03NMtifFanXpnjlnVCqRaFtzeZh5so0vLZurcVxI23I73y0ra+M1FGL0dXRm0d7s7OJtTPa8rvPjprNgH1jbZh84k2XT+LdV7Wl8sPiWDu+jdQst51Smv3nqpE8MZB9V8SW/GhkhooTa71tLie1dQ/bdsZ6lSH/vsg12/kUtlYTR+T8IolXQhnyQmwK8WLO8VDh+ZFjMX4FD4VzHSO/cVjIIS9rHQPPjcZ/TPdFMPhfHzEebc8knIo34jWRNRDNgcVuU2aweNrXQgPGmFeH1Gv10VQv73wb8wcOU74GUmMbuh7akN/5qKM8geqogbmF7Ga612qXCzOrH4yspVi19qLZP8a21OCLV17/+W/RskEXQ14Egj50RF+sPdCfNvvP9SKDYLZJzZCoCwbI4ubVkbWwOFa0wuiw3nbsrSEv5kvCCYfhWbfgQ6Hl7dMA/VWE8tCXFxi1alu4BoxyHY/xTeLsnWtDfmPQAPGovz0SXmOgZMcTDZFBl/wyxTuFsWd0qwyM+WAnGx5GjVhz4JBnPAardqtZC1PwX4hOOTtqNhthiMIT7Y6wt4OnNuQPPZSRD0VmwtfKdwtlbdH88cgC+ObAIqzYRhDLHFp3iDhObOIxcrPITjaMx2b/sSOQtZuXmx0dDpty0UBqinh4zkk7hJl52basx5Af47mMSE1j8ByIcV/GKkO+PPK1lZ5wjUIFcK5DazYGiyPtVSyO7EuRxcti9xiFpp8Y+dUYjYvtLI5VJuTRiDAdU1fCNpoDgy0jNf7LPHW8Qhqh2lFK6NOiH3TTWOl8tt0PX2xT5BunhzZ2Ci8fQ3/RIBp2reHMOFg/zNdsLurqNERRmdC28rBXO+w+Rv3o+VVmnhZ9MpqGbjQbizyqdnXknZH1YrWjW+VTDdg5zfSxtR6w4LzUH9t+tNl/7DDDrpmGCbM/bctaP2DJW6+9HRGhwFmy7Y14+HEfhvN6DXmNjYUezjMKxBSJ0zNlJAyj9qleZcg/ana0617HRlR21fBDF0axKkAb8huDhkhjY4rJ9C3ZC9d2ZRYkM+I1PjoOITO8tv6XzzyJRrV+bZfXx+j2zpHwDp6FZnORB9LetN9zIp50YVEaJ3/LELCA2UDtH2dHu8LfzLZcI5JndpqyQN2uUXZDaTYfxpYZLe3qon2J5SFvqv3+y0iTn+qjumYArcM3A6rOTo2+Znujz3xuxOCzCFo9tVhPf8iZVYiZt6vbJWZHuwwOmw4IzxBWoF23DsN7rPUjcc3eY+94balY7NqO26yofPIbAAX7R1/JQwx1Vv21o8oFIvaUHfq8TohkszFoF18RSXuOZXXhapE1nOzUaluFlMqLM8+OdrWzFrZybKpbjr1Wu7wt7U4LVhUuce/jKNGXsqjONSuDRxgROhoGnesl20wyIlALWzU6hRX5zpUHnrdJDH0tpJQZvPUMe1OM0xFUszEw7KT3PWZHu9BBMDqMbmtAZ9qJce9eIRnyR97cK1rmSWw2DvXDwEr6+0Vdq+orj4Rs1PSs0Df31EwXw1HYm0VAfi2SZ8IAWb1rT9CBQZspT5btNa1uuS5/y0jXEYnrVNeEvrnOiLOIa1vubdwshcHAqy5EVf20MNLszXRnKWGryoFBOYQ2OmaoGAhqF7QJBvIG6T3g21hq7QJbx0w2WaxqAD6GP90mki8cJoU4eFtqO8+Wkr/snW3v/d1iGCiJLJDO6oK8kj9jX8cR5npt++lXXx0bEKtHXkd2F+NQ3nb4sjzjYqXHaVxTEVZWi6leNBVvpOMXI8W0k3vHbXv8kpnXjrFgpgGdm/4qqNd6j+qsGCbUjdLmIITGHrjjfqm8vxoeP2QxYnRq6y0eB/vJ2xO7OTCoYxoedcPMlzwo8TpUvdQx8NxPf9mRp95MF/nlyHF6vtlcGGryqMLWphgIM8pGA52DRBiO/OVg4QWUd/72AGznoT6aHa06uihUladd/G9525UDx8rW2B54vffqOr45MBYrnxYtVOVxN7sytZW0A+qvet0LXDcPYTJVJ9iO03qgnfU7C7Ug1qwnG0g7O9YjWvWLsE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3TNE3THER+e9hh54mu8rvDDjvK/NQe5Noxo2tFZ5qfappmH0k9+4vUpUvPD49Arp8s168TnXR+qjkESH6feJ7vp5yfOgK59pc0P2wOAqmfx00eXDs63fzUEci1i0aXmR82B5Ck+x/N8+c481N7kPz7/Vy7UnT++amDzjGiG0TPi14YPSm6bDQ1yHQaT47c85zohtGxo+Ygk8J09eg50fOju0UnmF9aSK4rpE+MnpsC+bxSjq89v8U9Vxqvza/7jGdEN8/x77svf0+f42NEt45ekePj5+8Z8/fo8+vHdU90opz/n+iy0cmjpR1Ns5uzR4+M1M3HRReI1sNFI/XY6x4SnSEaOWokr11/QfRv0amiZh9Q3qOHpoyrI0+KpP9ucvyn0TNdn+gv5reoJ6fJPY+LpnVS2zwjx7+X4zPl75Hy9+HRo6JjR+rbkd2T/08anSI6d/S+6Gy5dtr8PeHsTZpNI+n8x0nnR+WvfHt0dJ75pYXkPoOxZ7u/lOPnRtrYO+Z41oYWOXfq6LHRdeendpN7ta+nis4UvT/H54uUqRPPrzM8lB1/lbOHRMrOGXLuSLM3OUTI9z1LvvfD81d6PyG60PzSQuZpdqdomlf/Hh2hr821i+T8E+f36DP/en7JNf3jH0anjN4TXSw6Vc6fbH7LrD3JsTr+wMjzHWd+blbHdzr5rpeInjpPvwfm72nnl1aSezkL/y5iB5HX7k7XIuc5FJ8zf3920Lnnl1xTh04YXSZ61/yYvTQz6POao+d/9ejI+fuK6JbRCdf7jJvFH0Wvj/Ich/0y+r/o29F3ohtHYOgz4H8eua/u+V70wOj3ouYgkMxQ2R8QfTN6TfSCnPt+/r4sOsn8tiOQa38e/SJ6Y/S0qIyMK89vcc/lnI+ePf+rc3l+7vld/vqcWb7n//tGL83x/fNX50Uvic46v37h6M3RfSKV5l6RZ72t681SrhB9Kfpo9JToC9E3orU8NDePvhm9K3papK5+JDpXBAP0R0WfiRjx6v9Po7dGx4+avSDlnjH2mehT+f8p+fvp6DvR1ea3qAM3m9eb/47UN3XqWdGfzm9xzwWjn0ZviWZ1MtIZXWt+i3t0VK7Rf0QPjdRdnzsz2PL3mpH3+LfIoEL9fF101dmbNJuC9I2+Fn1onh9fjr4aXXx+yxHIfQy+Kg/VxioXv8i1N0V/ML/vqNE1cl4ZU44ePnuDgZz760i+3zN6cnTv6PXRzOjPX8Ygo1KZMAh8cGTQwFBc6fjZSeS7Xjr6QvSJpKV8+lz0v9FuY3tK7mO0vTWSn/Ko6ua9cu2489tm5BxH2jcig2h5+6HoP+aXXb94JJ/0g/JCfqmfN3U978dQdN7nGGyp50+fH5989iY7mHzH20fsmbdF0vl70fuSLmeZ37KQXOdQfHv09eil0Qujj0UXnt/ivTkcpeM3cv+b8tf7fyq6yfwW93BIsovkD3vFYIq98ufz64x6+SVvHhNpX9lb98l7cpAdcI4ZvTjK5x/2joj3j3HGy87IulzE63qfyD3fii4Zucf560SM/UNqNL+VSOExRfqT6I7zU84Zbf4m+rv5qSOQaxqTD0e7R6LrIfcbAKhY15ufmpHj26eA/I7y/7ujPbztOT5d9NHhnpvNLzWLMfpXJxnX6imESDDs3xgdy4kFnD76YsTwL/44+kn0hIhHx3truGYDrTm3i34d3Wh21KyLlGWd7qujD+b/483PHU9Zj3TkMwMpf28WvTOaGduLyLULRR+J/mR+aim55xFDXTKonhl8RY4ZiN+Z3/P9SLvdbBJJ6xNE8lsHf7T5OZ48Bt0rk/57eNZXkfvPGX0r2t1+5/V/O38vRp1y9ND5pT3I+fNHX5Pv+WtQyBmwm5w3o/M41+f3MGpmz3sokO98rIgB9y7/O5f/eV8/G7095/YwyoucP2qu877+y/zUQnJd//ij6J55zW4HZ/7fw5Oe62eNPpvz8uDXEVtqD3Luwa7P73lx/i5r83cM+Y5mShjij56fkg5/Ev0sMoOy0NbMeeXaoMlAerdN4/5oFjmAXOOt/2nO7XZYeq375oczcs9Vop/lvLQ3ID/v/NKMHBsUv8b1+T33ml86KFwxynPMPOx/5sQCjIK+HuVZD7ulE83WIZnC+/35aLeBkP9Nl74j0oEsLPi5xpDnKTB4Wze53wiVsbF7mj7/m1p6ZWQU/KqIYeO5ZrF/+XuOiMfpvyLX3OfZrjF7g2YRl4h+Fk3T6G7R/0aM80XcIvpxNA3BMaP2qWhZvPTFou9HXcf3gpRjXvQfR3sMTHN825RvM2OzdtX16B05NzP2F5HrDPkP557zzU8dgVznUeJp5wFiWLw8YjzyDM7eO/8b3PP880jxJLlX3VvqGW72j6St2cufR3t4dXPMq8cAHwfNK8m9PHxfjHbHTuf/K0SXSx6bgdWGPmJ+aTe5ZvD2oqjyXTv74uiv5tePlf/vNz8/lp175NpCA3anke8qjIXj6/rzUzNyfJfo/6ILzk/tQdKnDHnt71JynYeWw2qpZzbXhDwJ6RjzSR290vy6z7p7JH/GfLpfrp1o9iY7lHzHO0Q/iM45PyU9hDWZSWJ3LJw5ynmhjQa/t5mfOgK5ZjZTaO9T56cWkutXi9gz6o78kQc89zNjPn+FRJnFUtfcJ+9eG90yz7pwfeBm89gon33YK6Jlo3KGgXt4+U7jRLN1SOERwsJDuwc5Z2rOtNLChRo5z3PAAPF606xGqmecX15Irp89UlnuOj81I4XjyjmngRQ+8JTofPn/NtEstCfHprxulL9G2z7vr6LrR5eavUGzCNPhP4ymMyY8bLzr1rAsgmfgy9HU83uHSCjcOHDT2fDk6hyeHn0sOnPUrJOUYQYWw2AaE1/165rz45tGDD1T5A+LbhydYnbznBwbFOjEdAzqpBjpPaaTc3yUnL9BJP5d6MS9c44BZ6AgBNL78MpapGVg4L0uEN08Whmv3ew7SdubRGYq9zDYc2ztkvKxrkFU7uPF5x1+WPL1CCGrOfcHuSZc5giGfM4pE9fNPdpfxp91GcrFbIYnf48W3TDiWBEucPfcKyzsJvm77hmD7Uy+p/AkHvM9HB051ieZwZgZ01PyuqNEPPnC5h4ZGWxxhu22m/K/RcZm5oSYSmtx8NY07LGOMNf1g/8vMvMiJFUe3CjiTHHdYO16kc0j/sX7Req4OlyzszuSfL8HRWYq9nB45Nw/Rd+Opmu9ZuS8tBEiJU2tEZL2e4Qh5Vh9+HLe+xref36P+raH7Ztr8uOK0eUjxrx8EG4z++xctxZFvRJi89RInbLo9Zq5dsCjU0w3vCzKZ88WxS2jwmreFx2U+J9mMckUI1UjwVfl/z0a/Zx7fCQGcGHMc66Z2tPYG3ES7zzD41Z5zTIvPgNEbNnCWLVcMx31gFxfuCAn18wUaASXzf40h3PriOFdce3F5SMe92XxnA+LPhtNDfnbRzzu9X4GeOr9eyPvx/u/bHDQLCFl2YCUYbBHmc6xga3zsxC01IlL5n8GWNU3MfU8tbtDH/K/jsFAt+75QMQIvENefwRPT87fNrrT/PAI5BovFQ/h0l0xmo0haXzH6NvTtjHnePe0q+tyWuS+u+Y9ds/kTMk1XvWFhnyRa7UgduksQK79faRNOKTId2aAGSzvsdNIjsXNq2u717WMJN31tWYz9LfqpjVf3odXfeawyl/19xMRD7v7vjrPy/dHMyN9JNcYhBar7xG2MZJrt8h9/zg/3PHk+1qE/bF85z0MeWmQ89YdLXQ05fxdoy9FHJjaVmsZ5CeHyGzwk78Mc2E7nCnC4L4SGbzxrP/h7I0Gck5Iz39GSzcJyDVl4urzw4NCG/LbiBQW00JWURv5MZgZCgxjDcark0F7ZchPyX12NxAWo2AfIdwm54xyjXifOz/VbBy8QxYi8gYx7Bjht4oY8lOP/FqGvA7+c9F0ofPUkNe4OXe/yEIsC2JfG50jOuRJOedd45VR3yxivEDqyBEGuDnPs8ZQW2nIT8l53lOLrHj4lk0X29lJJ/PLfPbScJvm4JM8unPE0Jh65BnyDIo1Dfnkse0I7WLy5vmpI5B71jTkm+Uk3XhufxhNPfIrDflF5N7bJD/ER999fmzGWow2I/GfI7N1Zs+EhHww6m1g1yBpJGTl40nXPeyWHK805HPdLIh22JoPs2OMdjsKWX8wW2uSv2YpxdoLhxGBYPe820U8/U/Le2zLWSmG3zOjPP9hz492LwiYwOPjnk9EOzo+ayuTgmY69OORkabG4g3RGSLxWwywPcg5FcKi09l0+3rIvXa7+FW0ewvKIueEBvwgBaEXzW0sjEMxe9ahfDUSFsOYF3PN8N4dKziHIW9Ny2wF/QJ08Ha3mdZVjdnXIrtULcL7/Sh6UXTILH5bRsq6mGd1jXhx7CRyhDC1nNNRG/zu3n0GORZaI+bWOqSF5JoFcTx2Sxe35poOSZ2sHcSaLUjyx9aE3432MDRyzJBngKzc3hC5x64zBn9HWPhYpLy0Ib8fJP0sGv5R/u4xMM45hrz6uvR3GabkXs4169PYT47PHDEKhTHuJsf29Dd4WPd7H6okjdgtn0z+THcC4nG3buTU81N7kPNCcnji9/Ce59jagjfM/7cJyPfy3heZXZyTc3b+sqB1226/bMom32u2vd2y3RQYFb+KGBW7t/FpDizJJAtgLLIQ0zXbczYSt2fRzCej3bFz+V98num/pYtdF5H7FXRGwx5exLyHnTl4/q30PyRiKQ8gBtTqnkakJC+vEiXJj9D4W8xja8lluw1ZjGUryWn4k5Abi12XbkkaXhfZpvKQH7CnnItFntW1uU6Sc0dwduT8paLf5toei5JzjqferjF7dBojuW7bOdPzS9vVXOPVUydnW9M1W5Pkj5jm30Z7DLBzzMD/esrBwnDEItftumGwaIZm6VR+7mtDfj9IuhksyafpomSeekb4en+nw2vsXGKWmvPD8UkioTW25N5Njq19sX5Cm96sIGlk3Y82cY9wwBwLubHL3rLZS4tkhdbsEYqaY5tzvHv+P+eKbS33GCjn+FaRtX/LnFxbHtOADPS0D7OGYfQ4iSGTKBLufyL3vCayvV3hFzpNKR/wAP9mFyl8Yh119Lv3iM7/fxaZQprtQDLvJK6av1f2//zYgrg9PIw5tn0Tr/t0gZ3FH7yO7RU8cJwtst2rfeALhuT7I/WwZloY9PK5DHR7zIt3H7dJM6XL+Ld3vIGDrUH3WJwZ3GM3HJ3SIfGjI/tC6oAfWtLwyx91ww8t8RS9ZHbDnBzbh9gOCcLf7DRzwdy7O13n5123TWVtUXn+3LPHlHLO2f3pJzm/bJeiZguQPLIgjkfXj7bNSJ5xvthwwA5Cs1munLMQldE49RxapMfY22MjgSl5vbabk8aPvDV7SdLNj2WZ0faDlrvJsS0935L0nW3jmv/NgFsvNvsxofwv/n0aNnWFXBda8w+O87+F6C+IzJyPi2B5i62Hma53aiYkjYQN/zK68/yUc9Z8GGQ9aH5KWltzZDZ01g/m2GJgsx67133kf68z0Hr8/NhAi9PzGbMbQl5nYbHoBjuKbevfUGGc+ZGZfI/DPhC9PBKq8ZWoEsVCjQ9G7vl85B473fhBmcdEy8Jymk0mGeJn2BkEQgAsvDAFpSHx4xW1CIfnQONlivcEeY3OwA+GaLx4BW29JW7MtO4e2zflXp5JO18wVvqXPw8cjD470JgNe3bkdx1eGombH3dWeECkXlZsp45IQ2XHG4a7ad53RsJtalGVUA471Lwq+qdIjOd7Ij8Ct/uXRpsjkjpgQKzzlu4S3iI48dF+t0E8u5hLPxSjU5ltcZe/9qm2ZeBrc79Yz3+J/GKgH6EZF7tamCoczpaFPLkWzYn7vHNet8camGZrkfxhxNkJxo/sWUynHMhzxv1s+0fk/9n+//nrN1pm5NiPDVmkp31euB1wzjP0/VCOHxv6Sl7z3khZunr+9tq1dZK0YrjZ2vE3+d9stgXj9mj3Wwu7Z9VyTtibfJp5b/NX2KlBGc+wvDXA1qfaMGLcilkoFUPUDLYF0O5XJu4yv6VZQdLcjJOtcg1qLVTlqLRuxEzVrG7kHnn4xnn+zNZ05X+DZu2lOuSHzthBNvBguO/2tOd/Hn+OT/aP/BGarK3eXUe3M+LFLHgVn8tzp5O/ZzSG21jVe9/I9Lx7GAa2r1y6Mr45MKQQ8wrap93iOR4+O8Pszrv8L9Smfhmutqg7V2Qq11SgxThiyY4Qz5tzwgqsumfwNQcWHTRvjzr3oUis3zRU428iRvi4C4OZlgdHdq/5cMTLPs6yeF+ee4a8uv7xyML3qZe+mZB6YEtHi9l2h5+l/jHEGAQ6DZ2HXyTcHRKV64x9W9UZLKufBtp2u9gjNj7HdpKynZn3USd59a+W17cRvw1IPglBtMiR0aEcMOb2iI3PMW88x8tu72xeZys8v/S51Muea3Zb8Z7KhTLEqPT3yXn9IbEH/EaR9GL0MeL0l9L0zTm3hwMj566Xc+/N31nYW/5yhnF4Ce/wGotXbd853V3FDxDZjtl9H/Ee0d9EPcu5TpJuflztERFvut/VMODave1kjjkiOS3ZLLu3mMz/HCZ2mRErr26wiUSN7CavVUc5XqxtUJden3PtvGq2Dgp4tFezI+5f9Zr5e7YhcfDZ1/C1tV4n73tGbYNIXVmZ3mvVN6znnmZrs1Y5GMm9a7ax8zKxx69Uzs91OdkPpOn833Wz3tfsy3s3h5P0O8Ivrq6H+evWrBedP03TNE3TNE3TNE3TNE3TNE3TrA/TDhbFtVqtVqvVarVare2hWeiQFbq2m/SLjg9ttVqtVqvVarVaW1Zsdrb7bJcdO9DY/cIe8H6RsNVqtVqtVqvVam1NsdnZ7mz4pmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapmmapjm4rPdn99d7382j20Vb9Wdvjx4dede/G4a9PI+x69/dHDO6a/Ss6J+io0RHjdabjtsd33Ot7yrdaNm9rq3Kq9keqiuQ5v8QXX52tCfrzYdV9ynj632+a0S3j1a93xmiB0Z/PDvafDzLbaJbzf/frsiHv4tuNjvauWhj1irz2p27R1efHe09l4juFh1tdrQ2p4ruE51tdnQ49jj+18gOC8u4cnTHXf/uwY2iv931b9M0TbMW54reHDG+F3Hh6J3R5WZHa/PS6E0RI2qrcazov6J3RMd3YoNgKH49MogBo0hn+pPojdGDogtGH4weFR1IjhOdOtrfwctJ51oLn/WM6GPRJ6LHRCeKRuyD6vqH5vpI9KLoFFFxqei10Vsjhua0PP199Lho1fe6dPS/0Vh2Txc9O6rnsxfrCaKRY0cPiD4efTR6cXTWqJC/l41eH70tYkBOn8Mg7j+jMpCvH303usDsaDEXi34YXW12tPkwCqXxq+b/b1eUDeVEPu1U1Kv/iZ4TVV6dJDrZrn93oyx/OnrY7Gj9XDQ6Z3TD6AWRQeUVohNHq9DO+bzaBu3k0V9Hnle58r9t0rR/xSWjM0bq8FMin6UN1VbBoOxb0ZlmR03TNM1KNJ6Mmg9EPMZTHh59Mzr97GhteKD/O9qKhjxj618i34lnfqM4b6Tzm23OH3SmH47uPzvahQ7uiREP6IHE4OIL0WlmR/sOQ9t3XIXO++0Rw/ye0X2jz0Wvjo4bgWH7yuh90Z3mYvQyIHgT8UfRe6NbRozwL0Wjh5HB+5mIobEKxszLozKyleH3RAZU/xbJH2lj8GmQh+NFDP0vRgZgyovBxicjBgfOEnn+m0RXijzf+CwMlc9GBhKFQdCnIrMzy+C99F6MnwMBg/CF0fPn/29XtDXy+emzo52JcmlgqK4U2tqX7fp3NxwU747uNztaP4zn10XSUD1XLgwapp72Ef0Fpw3Pe8Ex5Jm8j/fw14C3ZkvU/3tE2gDP/7xIf/GkqAYD6tmXo3KMNE3TNGtw5+jb0XQalHHDINUgr5etbMgfKHjJGIgH2mhfxC0iswWnnB3tO/J0LY8nw5fXmbetuEz0m+g6s6NdHbn3YUgvg0faLFEZ1zr9MhakrRkVnsBVnDBitAutKR4Z8dCfdna0C9P7nu8qs6PDDrtB9Oto9OIzLH4Q3Wt2dNhh14reENVgkFe7Pkc6M6TuMjs6HN+b9/8tUQ1YprQhv2+UIf/U2dGhw3Mjg+SRMuTvPTvaOwxMfzeXwe44Q7YI5dWAVTjOiJkCr6/3umk0RX2p6+qSWbDCwNusngHBodyPNE3TrBuekK9EvKgjwhu+E113drSL80RCGnheeU3FMo5hBWXIM1w0wkJJ7hCNmFLlLR3jynm1nxbxCjGMxs8UA+vYZ74m8hk8P8vg/eT95gkS2iIOs2L2dXA0Tk97Rp2JMAlhRIytemaxzQwwn/fo6F0RT9U4QyFNeLHOHZme9j6/iHh1PbNnZ7zxTo3fi+eeV9iUuff1OfW+0pSR+ZKI4SpN/JpXwaD1HXl9xZR6bobr9SLfTYwrr5xn8CzvjzzXn0dTeNY81ysi78ND7buAUc7Y+370f5HrwpOm4SiQLoyIEYYtT7TY74Ih/+DI545lp/AsPocxDu9bnkjGuLReaw1GhdWM39f3kAYjjHqzBmWkM8C9bgwjkpbSvwYywmTka80yeB4eTagbyqfyP0U+/SxS1hexyJAXliQGWfk0uDGgkW7qDi+mn2oeZ9Lc71nNZkCsslmPqgvqXX23RYa8Z2NAqWdCI64arcJgxmyKe+WZmZuzR4WQJM/J2FPPlPMrRpCH0tJAzfM9MzpHtAxlqdoI3+W2kecmz+yaNotRry4sivU+c8Tg96zaL6Ff2gAIu1K2hU0VZofUC4O3QrsmJOTWkTLq+/1lxGD1/dTna0aVplPUHW3B38yOduF9lKNpiKPBsbIpHx8beY24dHXCoPl7ke+izfUeBr+eQbsi73jMa3ZrUV0rzBRJV/XLd5OW2oFV7SzPuvCzsa4oP8qq2QPGuPLmGawPgXohbE1+aU+lnQEJz7z0LziXDJ6lf9M0TbMGOhwGI0Nh9BaWh7XiMDXqOgX3Mvp12gyTcWFSGfLQiQpBYNyMPCHS+ZTHldFlKlUHK4RCQy9GsqZjdfbCe/49YgS4fvFoEYwrnQSPEC+wzpb3tj5Lp6yj953L8yNMgyfWfb+KGKMVFqFj1VmamRBy8ZCIseX5K85eKAUjlxGhkxUDLl10aDzP4qIZC4zqf46gY2b8GEDp7Bi2jG3x4GAc6SSlFUNOun8tqo5VGAtvmPd8a+Qzfe+fRtJQhynsQ57+OGKw+Oxpx8iYMrASDqPzlcY+l/HNcJKejNSvRmJhGQi8/J5/ypMj+Tj1OHs+nXXB0LR+wIBJfnj/cWBkXYZnYBD6fMa3GFoGvpj19XTu0vEb0Tg7IP+k2Wj4KgMMH4YLlJdfRtOQAt9NufDaP4k8H089w9lASxlgqCsny8KYLhR5JuV5EVNDXjiW7+79lQfGkOc3gGNMChH7ecQ4LRhvyrAZBvWPoS7N5BlDU71Q7soDWoY8GN3WDajfnkF9UAcXDUog7RiAvjMDUL1URpQlz47zR2aEhO/Jd+VcCJpyp6wpLwZp6iqD3rOO6xGKGqAw0BnJ0sJnG0h4Pm2Rtkq9VI4ZiMp9DdCgHhq0eV6fLU89m/dVzhjCnochW0gHXmNpUuVG/stHg3xtozr4+Uh51j5IX+XbQHwRnpmxr32oASkD2OdoL2uQrJx7X44IAzfpakCsLVO+fa6ZP3XS7J/3la/SgANG3de2yF/tkTKwDO3KKSJlWPuoHTM4qbj1RWg7lc/Ray4NxcOLrWfACzk7X8TBUTh2n0Gz/qI+a3TsKCP6gPWuzWqapjnk4elhjJbXl5Gqs2GEV0fOuJl6dHWCvCl1z9SQ17kxOEZ0+rw/ruscdRz1moLHimHLWOC5YQisB/HLPDmLOi3PyOtVhosOmXE8dhY67LoOcZoMI51pwVDWkVsghr+IGGBl/DM6GFw8hgWjmJHC0wQGgsHJuKOKDlpnCu/puxQ6dcZ0eVrdpxNn2JTRpCN0LL0KxhvjZPSajeiEDUDGcBMdse83xqWvJ0aeYcP7z4gy4GLIyHvlSn6Wh5KxwJBilDC6GB0MyJoFMEhwnaGjfPFEyiux6kJ1GPiuG4AsMzQYVAZojISCAc1IV6Y9H6OXl9hAzYAJnkGYGWPM9+GdNRhjUJUXnsHk/XmRy/vICGcg8jzbdcbzGaDUABIMVOViOkNVTA154Qfy2KCtuHbEQPVevNfK0BjCxVPLWDeYEqLkurQr/G+wV5/BkFd/4X4Dg8qHtVB/GVxjGlu/4DNrzQADzuep72P4BGNOeb7x7GgX4sAZbzy9UzwT43jRIEgbwXvMKDdwKDgGtD/yS9ljeEp/RmTh+X8UldNA26StqTplMKM9MXCr7yn/6n3UQ99D2ag65nvKA7Mly5C3BvE1MJcOFjp7r/oOyp96qLyr29pjRnthBkPbOSINtZsGVOOAt9rptWaylA9tnrxdhXLNUNduL5p5UIcNNsxWLoPxrt9ZNFD0HOqCAWzTNE2zDngXdaLlMeZhZGCVsTrCKGXQM6p00Izf6iD2xpD3Gh2hBpsxxAjQsWrgGYPO6zwZyIwtHdEib90IA6YWK+qcR8/x1JDnsWZkjt5XnlmerIJHnhE3GlM8q96/psanhrz0KU9aUYa8MAcwHHm9p97rKYx5xqGQGd7Eer10sQBz/AzpKY14Nsu7pTNlMNRCslUwDCtEgPE/Tv3LU8b8KhhUno8hxwCRJtJSWvEMLuqwYXZFnPq4o48BnvwR0mA2Q7ky66Dc8QKaLeJJNxiYGhI+R/nhQTeoKrwn76/QGc/HSGaMeE7e5ILHV94o/9LO58g7Mx5lmCvbvN7uZcAJyWC8yyvP5/0MqsymFMoQY380xkbKkK/BnfT22dKn6gVPtDAn6SK/GWgVJ+27SmuzL5BeBjPeT9n0ejNPvn/F8I/1oZ6P3D96Wlch/Rns3t9AU9kx4ABPM+N0aoArYwZMDDVtjcGkwQWDmYE6zVPGsdAm9Uq5ZJwXnpNBq96PMMINENU9+aa+jPkB7yOfzHJAOksfdVo50rZ5X/eUh92AQJq7rl5J43EwpZx5nTpTXvwpBiYGHv9vdrSrPZDfyk45AJQnAwJtmHZiasib5TLgHOGA8axeO6JM8NQvConbF3jc1a9Kt41GOy9dp2tNmqZpmiXoDHnzdCSMQFPSOgQenkInoUPQuTDExaky2nQoqwx5BsVIGfI6wvKg84I5x8PJ0+l1vIXlBTP1/taI8aWjH72AUxhWBgamonX8jAUdqs8bDRf3+Y5iyV1nMDLYRo82Q55xMXqsDW4YeLWrwtSQ92xrGfI6bR3VaJCM6OilhfyQHsIreGLLYPAZnqtis+E7SDOvK4OzDPnx+afYHs77M7p9FoPBZ9UCVazHkC8YlD5PiIk0ZoxODagR5YS3fRyAjPxj5Jl4SR8fKafgsZQG0x2V5DMDY2rIF4wQz8dwNZAUtsToG+F5Z6S5jxGpjCs3i4xbO9vIJ+XHQLAWXTJelcEKtSlDeRw0jJQhXzNEyrvBNWO96oU6xyBj+EJ4iVkl5csMg/tr8aF6om6N9cpf+VF5W/WhDGeecgYzA1jdMEMnPRfhvM+SNwZE3t9rfGYtHvZ+6sI0zMSAyuyNeu519WzKIWfCIi+vMi//pam6Y7AgP7Q9DPnpYlczKTzn0l8++7ypYehzpC/DG9o47+/5bH+obKjf6pU1LMqzeiwEC+K6DfpqpgzKsEEfY39RCBqUed9Xe6o8ylNppN1SzzyH/C/D3czTeg155X6sb/LJQEHYTq0H2F8OlCGv7jdN0zTrRMgDDzUP3rQzAC8Pb+AYn64hZ+QtM+RNOVfYQiFOtjzyOkYd/bJ97KeY/ufJEt+6Ft7bfYzS2r97NOQZP4wanlfP7H8d4xhX7bl48saFWGXI13T8vhjynouRMYYbFLxv0l/nXgMW38WUeRkiZcgLLynKkJe25elncHjWep8pOnYdJkOoQg58f+89euSFLvA67y28tAy7MjwXYdAhvRhTUw+mtGX8MgjBYKy4Z4av72aGZIThYgZAvkn3VQid8XyLZp4KxrwBay3YG+HB9Xy1wFN5r1ktz847Xc/OMBTyMObZyNQjr6xK91WUx1soGSOQoW9mBOoZ4009XAQjVnkZDflCSASjWhoyDhehTHle71EDfrM6XlMeed/d4HsM04Ky5blrS8+9wefykmuLanGs8it8a4Qhr2wz5JVzoSvTsB31xCCyZjGgvVIWecYNMLRT1gQ5Z/BokFMzgwaRDPky7FGG/LKBaWHhsvLCK6+uM9bNVDgnNMnn1KBM+k4NebNv1piMlCE/bj9ZhrxBFgN8I/Cs5WxZNOjaX4QXKTdjG9Q0TdOsQRlwPEk6wOpECp0T47PgtWT88iBNDXmdhwbe7hdeU54pDTSjjcHMG83o4GFlDDFWCwZu/cKlRY5jSI3nmHZgBWOJJ9TngwGvAy9DcjTkdRK+JyODp5vnfurB3WhDvmLkPaPnGkMOGBwMGwa1eOBxJoNnVChCxVfvjSFvcFaLzSpdCullkd3oqRQ/7jXjszGUGDVlJC5CGRg7dd/HTIsyUPnPQOR5rPICi6U9g9mDEa9nvJRXznvLOzHzkA/ef1G4FSOM8T0OyqbPxwhjWCtPNTPi+vhsBhmM6fLsjng9j23lAe8wbzbPLYSMyPMyVtUnIWIVTjFlGiPPy2uQYTA0ol7U2gD5waPsOfwWxBh/zxj0+mmcMeNXOVcWlBcGof+FLRmYFOqEECTe7EWI75ZvoxEr1EjZKcN9mSEvnE0dGnczgrju6QwLDJTGGSLPqv5IM6wy5NVd6SRvhKeNs1PyYhp7r15X2FWFBRpcqW+85OpXlef9MeQNIn2O99NuQv1QZgzIDNwrVn+RIa8tY/TX4FceHihDHp7Z5y+apdpflBcDtVpr4ftrDw10mqZpmiVo8BnVOmfeuKm3WMyyjl3Hz7Oss7EbAqO6jB9GvPPVuPPMWezG4DIVrDPiAdch1vszVLyv+F6fz/Olc6y9kBluDGMdtYEDA4DBsAgdoc7atLX34r3XgVdsqEFKTUfr4L2fDlpYiWdiFOnIy4vJwGUQjgvHGGRjmItBAqOiPKkMPjtolGcWwip4IIVhQAcv9MF9nk+n7PsL12FMmg2RTjpuP6ykc5cv5dH3Gb7b+ANDOnTPryOv0BoDJ+8rXIBBOt3tx/fkjfQdeSsrrtpnjQYgQ0U+en+zCYtCVnS6DErlw3fijWZclrca0opRzFPsfRgCYpKFM42eY2XDDADDffTSi/FmfOvQvcbC1UWGhPUbnnc0TIV7+FzlQjn1fIybcdcXxilPI0PMfbyi7pvunsGQlp7ycPx8e3EruxYvCgPhJa+6YeBo4fT4K5cj0192tSOKZ1EOGKo+i/fY547pb7Apvxjt5aGGMib9GNYGYUJP5I9yXsay/GRwwjoM39XA3Dnli0G4bPDGmLXDi9AeZZ3RK289S23XaNGiwcu47WqhXLumDnofz6WcGgBNsZORQY5BofLl2eSPcqLdkk7T0C+DBIPfWjTO6NeOOOe10sSzW08xlj2DPyFKYx2Q38qdc6NXv+4dB+3SRXuibJfBvwh5KEzRe9b2qr6LAYRzjO8aeDLQORTG9UbKmHbI99A2ej/PqfyZkSqqXff8o7NkfzEztmqgtz/cOTLAql93VUelifo6nbVrmqZpBhhAGv1lU5q8tTpSRjmPoY5OJ1YdDuOWsTl6NRndPH8MCUY7jyMDdPRW8RgLBWDQMuDs6lDGCm+MWHDXvEcZzMvghRfy436dTXm1dGjep0IkhBLp1HWOnptBJMaZ4cMgg/SwsHL0ZDEMxPnXjAXvoveoHSgMGuxYMT6nc2Kjy8MEnTxvs+/rWaVbxbAyLBxLa9/ZIMq95a31fgyV0cBkcPHIStvRMGHAen/hCKNRXej8GSfylPHguQ04xh2KeKy9r2c1uFhkoJh1MACpPPS8U4NfOWFcS2dGJgOE0SdvRnTgPNvjTAiUK/nku3ie8kxPMdBh0IyDKec8ez0fY2G6+I9hyMNeeSLPFoV/yHPPZ4A2wqj3es9nEFgDKijfBrLLntl3Vo6EyxSeTznwLOQzR2MdYvmlo+86XXOhjpnxUKe9Xv4yaCu9lZeqD84pZ/LFvQZvaxlNnk8dU3YMqniZhYyUp1z+MSrHnXNGlE1lwefx8E5na0a8J4Pfvcpr1Un3S+vpjA6vrt2Fqv6D4W0A6D3kkVCZKcqo9/M84yyMdPSacVcf9VW+1mwcpJmypQ1bK/20QfJm3J7Rc/scM32F+iadK2QJyprPUVY9g3vk//Q++co5wTGyrOztC9oeA6pxsLwRSH+DK4PMasf0AdrU9sg3TdM0u9FZik0dYQzzEK/aOq7Z+jCgGH28vau8ogcKAxre5vqF2qbZ7ijTZtwqlGyjEPpkVnXcvrdpmqZpjoAFgqaGhe4IK+DRE8IgfGHcj7rZnpi9MLsyjTE/GAiXEfNrh6Cm2SkIjxJ+Ns567C9mPtTb6cxT0zRN0+yBKXkL6MTWijtmxAttWM+e683WR5iB8KPpQsuDgRA0IQ/LwkaaZjti7ZA1NhVWuBHwxNeagaZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmqZpmuaAcdhh/x8ctFpZZDHTAAAAAABJRU5ErkJggg==\" width=\"690\" height=\"254\"\u003e\u003c/p\u003e\n \u003cp\u003eThe study\u0026apos;s identification of break spells and active spells for the years 1981 to 2020 is presented in the appendix, respectively. As seen in the appendix tables, for 40 years (1990, 1991, 1992, 1994, 1997, 2006, 2017 and 2018) there were no significant break events. Similarly, for 5 years (1983, 1985, 1988, 1989, and 2012) there were no significant days of active spells. During the total period, there were 67 break spells (total 291days) of duration varying from 3 to 12 days (37 spells (168 days) in July, 30 spells (123 days) in August). Similarly, there were 75 active spells (total 297 days) of duration varying from 3 to 12 days (44 spells (171 days) in July, 31 spells (126 days) in August) \u003cstrong\u003eTable \u0026minus;\u0026thinsp;1\u003c/strong\u003e. The average break spells duration was 6.7 days (3.7 days in July, 3.0 days in August) and the active spells was long 8.6 days (4.3 days in July, 3.1 days in August) during same study period. The lengthiest break spell (duration of 12 days) was occurred in 1996 and 2016. The lengthiest break spell for seasonal (total two-months) (duration of 18 days) was occurred in 1998 and 2016. In the same period, the lengthiest active spell (duration of 12 days) was occurred in 1994. And for seasonal, 24 days duration of active spell was occurred in 1994.\u003c/p\u003e\n \u003cp\u003eTo investigate the spatial spread of rainfall anomalies with low level monsoon wind (850hPa) related to the both spell over mainland Indochina, composite daily rainfall maps of the 291 break days and 297 active days were examined (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003cstrong\u003ea \u0026amp; b\u003c/strong\u003e). As seen in the Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003cstrong\u003ea \u0026amp; b\u003c/strong\u003e, in terms of both break and active spells was discovered that the composite daily rainfall anomaly patterns were completely similar each other. During the break days, the rainfall along the west and south coast and over most areas of monsoon core zone were less with weak southwest monsoon wind, which the far northern along the Himalayas foothill were exceed are found clearly. The reverse pattern are exhibited during active phases. This pattern confirm with the previous monsoon rainfall studies (Oo, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sein et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003cstrong\u003ea-c\u003c/strong\u003e show the frequency histograms of the duration of both spell during the periods 1981\u0026ndash;2020, 1981\u0026ndash;2000 and 2001\u0026ndash;2020. During both 20 years the halves (1981\u0026ndash;2000 and 2001\u0026ndash;2020) almost equal number of break spells (32 and 35) occurred. However, there was rise of about 7% for the active spells (total 40 spells) during 2000\u0026ndash;2020 contrast to that during first half (1981\u0026ndash;2000) (total 30 spells). Furthermore, it can be noted that the two periods, the short (3\u0026ndash;6 days) and intermediate (6\u0026ndash;9 days) spell durations made up the increase in the frequency of active spells (35 to 40) during the second half. In the long duration (\u0026ge;\u0026thinsp;9 days) side, the number of spells in use substantially dropped to 0 between the first and second halves. As opposed to that, the frequency of break spells of intermediate and long lengths occurred about equally in both halves of the study period. However, in the short spells (3\u0026ndash;6 days) side, the frequency of break spells showed significant decrease to 6 spells from first to second half.\u003c/p\u003e\n \u003cp\u003eThe bar plot of both break spells and active spells durations for the entire period of 1981\u0026ndash;2020 is displayed in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003cstrong\u003ec\u003c/strong\u003e. This graph shows that short-duration both spells were more common than long-duration spells, with roughly 69 percent of break spells and 76 percent of active spells spanning the duration of 3 to 6 days. From 2000 to 2020, there were no spells active with a duration of less than 13 days (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003cstrong\u003ec\u003c/strong\u003e). While only 3 percent of the break times were longer than 13 days. This finding suggest that the exceeding of monsoon rainfall which similar with previous projection studies (Oo et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.1 Lagged Rainfall and Monsoon wind\u003c/h2\u003e\n \u003cp\u003eThe purpose of this section is to discuss the evolution of composite breaks and active phases during MSWM. A lagged composite simulation of the daily rainfall anomaly for lags ranging from \u0026minus;\u0026thinsp;12 to +\u0026thinsp;12 days (17-June-1985 to 8-July-1985) is used to explain the modifications to both spells respectively. The halfway point of the break/active time is referred to as Lag.0. There are some interesting characteristics in the modifications to the break spells. Nine days before the break spell (at Lag \u0026minus;\u0026thinsp;9 and \u0026minus;\u0026thinsp;6), a large part of the study area has positive rainfall anomalies whereas there is a belt just to the south of about 22\u0026deg;N with positive anomalies and the southern coast also has positive anomalies of rainfall Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. In the next ten days (Lag.0), this north-south band of positive anomalies is sudden turn to negative anomalies. Around a week from Lag-3 to Lag\u0026thinsp;+\u0026thinsp;3, at lag.0 the pattern found significantly the break phase with negative anomalies over the main monsoon core regions and positive anomalies over the northern mountain range regions as well as some eastern part where are associated with the monsoon trough migrating. By lag\u0026thinsp;+\u0026thinsp;6 days, the region of positive anomalies reinforces over monsoon core west and southern coastal regions of eastern BoB region and when lag\u0026thinsp;+\u0026thinsp;9, negative anomalies occur over most of the northern region.\u003c/p\u003e\n \u003cp\u003eIn contrast, nine days before the active (at Lag \u0026minus;\u0026thinsp;9 days), negative rainfall anomalies appear over the western and southern coastal part of the monsoon zone (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). The positive monsoon wind which increases and slowly expand northeastwards. The whole monsoon zone has negative rainfall anomalies at Lag \u0026minus;\u0026thinsp;3 while the distant north, northeastern, and southeastern regions have positive anomalies. The same pattern (albeit with more intense negative anomalies) characterizes the break at Lag 0. From Lag\u0026thinsp;+\u0026thinsp;3 days, negative anomalies over the peninsula spread northward and westward and subsequently cover the monsoon zone and the west coast by Lag\u0026thinsp;+\u0026thinsp;6 and +\u0026thinsp;9. At Lag\u0026thinsp;+\u0026thinsp;12, negative anomalies are restricted to the Northern Mountain ranges, while large positive anomalies are observed along the west and south coast. This characteristic may also be noticed in the development of V. Krishnamurthy \u0026amp; Shukla, (\u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u0026apos;s breaks and active spells explanation.\u003c/p\u003e\n \u003cp\u003eThe rainfall anomaly composite over the MSWM region for breaks discussed in this study is comparable to that of Lwin, (\u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e); Ramamurthy, (\u003cspan class=\"CitationRef\"\u003e1969\u003c/span\u003e). While the present study used higher resolution (0.25\u0026deg; \u0026times; 0.25\u0026deg;) data, while Ramamurthy, (\u003cspan class=\"CitationRef\"\u003e1969\u003c/span\u003e) used rainfall data of meteorological subdivisions. The composite of rainfall for break spells is almost a reverse pattern of the active composite. The rainfall anomalies are shown to be uniform over the central monsoon zone and along the western and southern coast during breaks and active spells. However, the positive and negative anomalies are significantly seen over western Myanmar and, southern peninsula, vice versa for both phases.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.2 Decadal Variation\u003c/h2\u003e\n \u003cp\u003eDecades wise, variation in break days observed during 1981\u0026ndash;1990 and 1991\u0026ndash;2000 showed maximum break days (81 days) and minimum break days (66 days). \u003cstrong\u003eTable \u0026minus;\u0026thinsp;2.a \u0026amp; b\u003c/strong\u003e show the decadal distribution for the number of occurrence of break \u0026amp; active days respectively during the period 1981\u0026ndash;2020. The progression of events throughout the course of the two months (July-August) is shown by dividing July and August months into 3 equal periods by 10 days (hereafter called as D10) each (11days for last period of each month).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eTable \u0026minus;\u0026thinsp;2 Decade wise 10 day period statistics for (a) Break and (b) Active days (1981\u0026ndash;2020).\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"766\" height=\"310\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eIn the \u003cstrong\u003eTable \u0026minus;\u0026thinsp;2.a\u003c/strong\u003e, the decadal distribution of break days shows that during each of the months, first D10 is most prone for actives. However, when both the months taken together, the first D10 of July followed by last D10 of August recorded highest occurrence of break days (21% \u0026amp; 24% respectively of the season). The decadal variation explained that the peak occurrence of break days during the two-first decades (1981\u0026ndash;1990 \u0026amp; 1991\u0026ndash;2000) at the first D10 of July. However, during the last 2 decades (2001\u0026ndash;2010 \u0026amp; 2011\u0026ndash;2020), the supreme number of break day found on the last D10 of the August. It\u0026apos;s also noteworthy that there was no even one break day during the first D10 of August throughout the last ten years (2011\u0026ndash;2020). Decades wise, variation in break days observed during 1981\u0026ndash;1990 and 1991\u0026ndash;2000 showed maximum break days (81 days) and minimum break days (66 days).\u003c/p\u003e\n \u003cp\u003eIn case of active events (\u003cstrong\u003eTable \u0026minus;\u0026thinsp;2.b\u003c/strong\u003e), 55% of the total 297 active days was in July with an average of 14 days per season and 45% was in August with an average of 11 days per season. The decadal variation shows that during 4 decades (1991\u0026ndash;2000, 1941-50, 1961-70 \u0026amp; 2011\u0026ndash;2020), highest number of active days was observed in the last D10 of July and the first D10 of August. The monthly variance of active days by decade reveals that throughout the course of four decades, July had a higher number of monsoon active days than August. In terms of decadal variation, 1991\u0026ndash;2000 had the maximum peak number of active days (27 days), whereas 2001\u0026ndash;2020 and 2011\u0026ndash;2020 had the minimum number (3 days).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 The different of atmospheric parameter\u003c/h2\u003e\n \u003cp\u003eComposite atmospheric parameter maps of the 291 break days and 297 active days were studied to find at the anomalous geographical pattern of rainfall over Myanmar associated with breaks and active events. In this section, we have mainly discussed the physical parameter of three atmospheric variables that the break, active and different values between these two spells by using ERA5 along with the characteristics of the Intra-seasonal variability (break, active spells). We also looked at the various rainfall categories based on threshold frequencies over Myanmar in order to better understand the variability of rainfall.\u003c/p\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1 Sea Level Pressure (SLP)\u003c/h2\u003e\n \u003cp\u003eIn this part, we\u0026apos;ve examined the composite average SLP patterns of break spells and active spells as well as their variations over mainland Indochina and the surrounding area (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e). In the break conditions, weakening of heat lows and the monsoon trough, as well as the disappearance of the offshore trough, are seen (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003cstrong\u003ec\u003c/strong\u003e). These changes have a direct impact overall monsoon circulation, particularly the low-level jet\u003c/p\u003e\n \u003cp\u003eThe heat low that stretches from Pakistan to the coast of Myanmar and passes across north India during active periods (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003cstrong\u003ed\u003c/strong\u003e). The pattern indicates that the intensity of the low-pressure area is less intense during these spells (below 1000 hPa). Both the break period and active period of the monsoon can be seen the trough is lie over north and middle India (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003cstrong\u003ea\u003c/strong\u003e). The western coastal area of Myanmar has significant rainfall occurrences because of these troughs. The monsoon circulation produces more rain than usual across mainland Indochina, especially over the core monsoon area, and low-pressure troughs, depressions of BoB and the meridional pressure gradient changes between the north Indochina and South Ocean modulate it. In addition, there is a strengthening of low-pressure areas over northern Myanmar, which intensifies the flow of southwestern winds from the BoB to the mainland Indochina region. This heat low, which stretches from Pakistan to Myanmar, weakens and becomes discontinuous over middle inland of India during breaks. It then moves into the Himalayas foothills region, bringing more rain to northern foothills region and dry central Myanmar during breaks than it does during active spells.\u003c/p\u003e\n \u003cp\u003eThe pressure contrasts between the break and active spells show that the monsoon trough, which has a reach in the northwest India to northern central Myanmar across the head of the BoB, is also called the heat low (BoB). The pressure differences clearly show an increase in the strength of the seasonal monsoon trough over the upper northern area of BoB is greater intensity in the break phase compared to active (-3 to -5.5 hPa) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003cstrong\u003eb\u003c/strong\u003e). Further, the pressure differences are positive over far southern India and the southeastern Indochina peninsula. It clearly indicates that the analysis exhibits a good skill in reproducing the observed variations of pressure distributions during break spells and active spells.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Dynamic Anomalies\u003c/h2\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1 Lower tropospheric (850hPa) winds\u003c/h2\u003e\n \u003cp\u003eThe two low-level jets are crucial in regulating rainfall variability because they carry moisture with two dominating cores from the southern Bay of Bengal (BoB) to the mainland Indochina and northern Myanmar. We have investigated the variations of mean wind direction patterns for the two spells at 850 hPa (approximately at 1.5 km from surface to ground) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e). The significant two dominating Monsoon Low Level Jets (MLLJ), Somali Jet (SMLJ) with a peak (15\u0026ndash;30 m/s) and Bay of Bengal Jet (BOBJ) with a peak (12\u0026ndash;25 m/s) during both spells are exhibited in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003cstrong\u003ea\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003eThe weak (strong) magnitude of MLLJ during break (active) over each sea area provides a clear indication about the transportation of moisture from the BoB to mainland Indochina by directly enhancing (declining) precipitation over Myanmar. The spatial pattern and the strength of southwesterly MSWM wind over the Arabian Sea (ARBS) and BoB are in good agreement with climatology MSWM season average during 1981\u0026ndash;2020 (Chakraborty \u0026amp; Agrawal, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Viswanadhapalli et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn active spells, spatial distribution of low-level 850 hPa wind, from SMLJ with peak (15\u0026ndash;30 m/s) extended and enforce into BOBJ core (over the domain of 5\u0026deg;N-20\u0026deg;N, 85\u0026deg;E-100\u0026deg;E) elongated in a southwest direction with core speed around 10 to 25 m/s with slight latitudinal/northward extent up to 20\u0026deg;N during active phase (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003cstrong\u003eb)\u003c/strong\u003e. However, similar pattern with weak intensity are found during break phases especially on BoB. Additionally, in active phases, the wind flow shows moderately strong south westerlies over both seas till to South China Sea. Moreover, the south-westerlies at lower level are replaced by westerly wind flow resulting in the Ekman transport (increase in wind speed and height) influence (Prathipati et al., \u003cspan class=\"CitationRef\"\u003e2021a\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2 Mid tropospheric (500hPa) winds\u003c/h2\u003e\n \u003cp\u003eThe discrepancies between the two spells\u0026apos; 500 hPa mean wind patterns are exhibited at (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e). The mid tropospheric circulation present a broad anomalous anti-cyclonic circulation over the Middle East regions and veering along the adjoining two sea areas in the both break spells and active spells (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e). But during break phases, the African easterly jet\u0026apos;s accompanying wind speeds suddenly increase (AEJ) between 30\u0026deg;-50\u0026deg;E, resulting in no cyclonic activity, i.e., weak convective activity over Myanmar, and driving all northesast winds towards East Africa. During break spells, weak in both west and easterly winds over mainland Indochina, and there is no strengthened wind associated with levar-channeled flow over BoB (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003cstrong\u003ea\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003eApart from the winds associated with MSWM, one of the interesting features noticed in active spells about the cyclonic circulation over northeastern Indian and veering along the adjoining sea areas is signifying found (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003cstrong\u003eb\u003c/strong\u003e). When monsoon troughs or lows are found at the middle tropospheric level, there is an active monsoon circulation interwoven with cyclonic circulation above the north head of BoB. Moreover, the wind pattern differential over that area is in agreement with the above analysis. Thus, it may be one of the synoptic features influencing the unprecedented rainfall over the seasonal monsoon trough area during active spells. A cyclonic veering with the maximum wind above the eastern parts of that trough (shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e) plays a significant role during the monsoon active phase, according to changes in mid-tropospheric wind between the both spells.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.3 Upper tropospheric (200hPa) winds\u003c/h2\u003e\n \u003cp\u003eMoreover, we examine the differences between break spells and active spells by the composite mean of upper atmospheric (200hPa layer) wind pattern (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e). The Tropical Easterly Jet (TEJ), which has a maximum strength of more than 25 m/s and is located between 15\u0026deg;N and 5\u0026deg;S is the dominant wind flow at 200 hPa upper troposphere. It plays major role in both spells especially over East Indian Ocean (EIO) region (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e). Apart from this, the anti-cyclonic circulation over northern Indian continent also call the Asian monsoon anticyclone (AMA) by Amemiya \u0026amp; Sato, (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) takes some important role that the longitudinal movement\u0026apos;s variability of the anticyclone center and its longitudinal dimension is significantly varied in both spells. In break spell the significant jet flow is only extend form 40\u0026deg;E to 98\u0026deg;E and lay between 25\u0026deg;N to 30\u0026deg;N (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e.\u003cstrong\u003ea\u003c/strong\u003e). Although, the core dimension of AMA is notably shift easterly to northeasterly during active phase of MSWM (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e.\u003cstrong\u003eb\u003c/strong\u003e). While the AMA core position also changes over northern India continent, the core of TEJ centered also change over mainland Indochina, especially upper Myanmar (20\u0026deg;-25\u0026deg;N, 90\u0026deg;-110\u0026deg;E) at 200hPa wind. This prominent feature is well indicating the shifting of easterly jet towards the south from its mean position in active phases and the intrusion of subtropical westerlies into lower latitudes during the break phases.\u003c/p\u003e\n \u003cp\u003eAnalyzing vertical windshear (the differential of 200 hPa to 850 hPa) provides valuable insights into monsoon behavior, including onset, intensity, and breaks (Chen et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shun \u0026amp; Chan, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). The significant convective shear cells are found over Arabian sea and BOB during both spells. However, there was weak shear cell are divided into two cells over far north and south edge of BOB during break phases (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e.\u003cstrong\u003ea)\u003c/strong\u003e. The latitude\u0026ndash;vertical cross sections of 850\u0026ndash;hPa to 200hPa two component winds (include both the horizontal and vertical wind components) average over 90\u0026deg; E\u0026ndash;105\u0026deg; E during 1981\u0026ndash;2020 for both break and active phases with their 40 years average are displayed (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e.\u003cstrong\u003ec,d\u003c/strong\u003e and \u003cstrong\u003ee)\u003c/strong\u003e. Black dash lines show depth of tongues, red-solid lines show the width of easterly wind core and red-dash lines show location of peak level of MSWM wind (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e.\u003cstrong\u003ec,d\u003c/strong\u003e and \u003cstrong\u003ee)\u003c/strong\u003e. During break, the core of easterly wind gradient is significantly weak than active and separation into two cores other than the climatology and active phase as mention above. Moreover, the depth and vertical top level of monsoon wind over monsoon core region of Myanmar (10\u0026deg;-20\u0026deg;N) also vary with each phase. These features are well simulated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e. Therefore, the weaker and deviated pattern from mean TEJ during the break are noted and substantially greater TEJ/MLLJ over upper/lower troposphere are discovered in the active spells.\u003c/p\u003e\n \u003cp\u003eBase on the above analysis, the inter-annual statistical relation value of lower, mid and upper tropospheric wind, rainfall with break spells and active spells are performed by Pearson correlation coefficient analysis and results are explained in \u003cstrong\u003eTable \u0026minus;\u0026thinsp;3\u003c/strong\u003e. During break days, the strong or moderate negative correlation results are found that the break spells with 850hPa Core (average 850hPa wind over 90\u0026deg;-105\u0026deg;E and 5\u0026deg;-20\u0026deg;N), 500hPa Core (average 500hPa wind over 90\u0026deg;-105\u0026deg;E and 5\u0026deg;-20\u0026deg;N), Rainfall(all) (rainfall average over 90\u0026deg;-105\u0026deg;E and 10\u0026deg;-30\u0026deg;N), Rainfall Core (rainfall average over 91\u0026deg;-99\u0026deg;E and 11\u0026deg;-23\u0026deg;N) and BOB Wind (850hPa wind average over 90\u0026deg;-100\u0026deg;E and 10\u0026deg;-15\u0026deg;N). However positive for 200hPa Core (average 200hPa wind over 115\u0026deg;-135\u0026deg;E and 0\u0026deg;-10\u0026deg;N). For active spells, apart from 200hPa Core, other parameter shows a positive correlation. Both results significantly level of 95\u0026ndash;99% by the two-tailed test of significances test. This explained the role of both spells with circulation of upper and lower atmospheric wind, over the rainfall variation of study region. It also found that the anomalous tropical easterly jet wind is a key player of intraseasonal monsoon intensity variation.\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" height=\"270\" width=\"690\"\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Thermodynamic Anomalies\u003c/h2\u003e\n \u003cp\u003eTo analyze the variations in the pattern of MSWM rainfall between both break and active phases (resulting in the convective patterns), composite maps of the OLR anomalies are shown for break periods and active periods in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e.\u003cstrong\u003ea \u0026amp; b\u003c/strong\u003e. The average of both break and active period composites OLR anomalies patterns are shown significantly negative and positive values during break and active period respectively. The break spell is characterized by negative OLR anomalies from far north Myanmar and southern China to the west Pacific and the eastern part of the equatorial Indian Ocean, as well as significant positive anomalies over the main monsoon core zone, the equatorial western Pacific, and the Pacific central. Thus, over 70\u0026ndash;130\u0026deg;E, the quadrupole pattern described by (Annamalai \u0026amp; Slingo, \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e) is seen.\u003c/p\u003e\n \u003cp\u003eNegative OLR anomalies are present in the active composite over the central and western equatorial Pacific, additionally the primary monsoon core zone. Positive anomalies are present over the eastern portion of the equatorial region of Indian Ocean. The highest variation in OLR anomalies between two events was seen over across the BoB to South China Sea (negatively) and the equatorial eastern Indian Ocean (positively). The weak positive values also result over far northern Myanmar and southern China. The study area is a critical area, which is clearly seen as physically linked to both break and active cycle of the MSWM with the ITCZ pattern and it has been a well-study area to investigate the ITCZ variation.\u003c/p\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.1 Relationship with Tropical SSTs Over Pacific \u0026amp; Indian Oceans\u003c/h2\u003e\n \u003cp\u003eSea surface temperature (SST) is important tools for monitoring and analyzing climate variability and change (Zhang et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). And previous studies already prove that SST are the best climate predictor for monsoon system (Ding \u0026amp; Wang, \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; G. Huang et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Webster et al., \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e). The spatial composite SST anomalies explained similar pattern by positive/negative patterns of equatorial Pacific regions (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e.\u003cstrong\u003ec\u003c/strong\u003e and \u003cstrong\u003ed\u003c/strong\u003e). Warming (cooling) SST anomalies was observed over equatorial Pacific during active (break) phases of MSWM.\u003c/p\u003e\n \u003cp\u003eTo further examine the association of SST on the both break and active events, we examined the frequency distribution of spells over study area associated with both phases of El Ni\u0026ntilde;o Southern Oscillation (ENSO) Index. During the period 1981\u0026ndash;2020 there were 9 El Ni\u0026ntilde;o years and 11 La Ni\u0026ntilde;a years by the NOAA\u0026apos;s primary index named the Oceanic Ni\u0026ntilde;o Index (ONI) (B. Huang et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). The histograms of the both spells of various time spans during these SST anomaly years show homogeneous distribution with spells for long time span (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003e). Long-lasting break spells do occur more frequently in La Ni\u0026ntilde;a phases than to El Ni\u0026ntilde;o. Trough the El Ni\u0026ntilde;o (La Ni\u0026ntilde;a) phases, there were total 54 (120) break days and 96 (54) active days with an average 6 (11) break days and 11 (5) active days per season respectively. This demonstrates unequivocally that La Ni\u0026ntilde;a supports more break days than active days, as the mean frequency of occurrence of break days during La Ni\u0026ntilde;a is higher than El Ni\u0026ntilde;o time as mention above. Similarly, the reverse pattern is found for active days that more active days in El Ni\u0026ntilde;o than La Ni\u0026ntilde;a years otherwise El Ni\u0026ntilde;o favors days that are more active.\u003c/p\u003e\n \u003cp\u003eFor future approach, we also find the best dominant index to predict the spell events that excess or less spell year for both event. \u003cstrong\u003eTable \u0026minus;\u0026thinsp;4\u003c/strong\u003e show the significant CC values of well know climate indices with break and active days during 1981\u0026ndash;2020, such as Indian Monsoon Index (IMI), South Asian Monsoon Index (SAMI), Western North Pacific Monsoon Index (WNPMI), El Ni\u0026ntilde;o Modoki Index (EMI), Tropical Pacific SST (Nino3-4), Southern Oscillation Index (SOI), Dipole Mode Index (DMI), Tropical Northern Atlantic Index (TNA), North Atlantic Oscillation (NAO), North Pacific pattern (NP) by previous studies. All values are analysis by Pearson technique and considering only 95\u0026ndash;99% significant level by two-tail test. As the results most are highly correlated with not only global climate system but also weather of small regions. Thus, we performed the test and result show that moderate to slight strong negative correlation values are found DMI, Nino-3.4, EMI and WNPMI with positive correlation with SOI TNA and NP during break days. While SOI show slight strong negative correlation during active days, Nino3-4, EMI and DMI show moderate correlation values.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eTable \u0026minus;\u0026thinsp;4- Correlation coefficient values for break and active days with climate indices\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"775\" height=\"202\"\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4 Summary and Conclusions","content":"\u003cp\u003eOur study assesses the statistical variability of the break and active spells by the data capturing of the synoptic features of MSWM. The semi-permanent features of ITCZ and weak gradient trough along the offshore of the western coastal of Myanmar during Break spells, and northward migration of ITCZ as well as induction of the meridional pressure gradient in active spells are well simulated in study.\u003c/p\u003e \u003cp\u003eThe temporal and spatial distribution of rainfall intensity is also investigated for both break and active days, using the average daily-normalized rainfall over the monsoon core region, which is consistent with intra-seasonal variance. The strong correlation values between the summer (JJAS) rainfall over this core zone and the monsoon rain suggests that this area is crucial for both intra-seasonal and inter-annual monsoon variation.\u003c/p\u003e \u003cp\u003eAs a result, 68% of the break events with 76% of the active events are the short duration event (about 3\u0026ndash;6 days). Only a small portion, 3% of break events and 1% of active spells are lasted around a week or more. There are typically 7 days in both spell of July and August. The number of break and active days correlates strongly with monsoon rain. No discernible trends in the days of break events or active events throughout the MSWM season can be seen in the break and active days time series analysis. However, there is slight increasing trend for active events.\u003c/p\u003e \u003cp\u003eNormal monsoon trough that inter tropical convergence zone extending from Indian region to Myanmar Monsoon core region during the active monsoon conditions indicate the variation of the anomalous convective rainfall and low-level circulation over Asia-Pacific region. Life period of these synoptic scale systems is also of 3\u0026ndash;6 days. While the break circumstances are brought on weak gradient of the monsoon trough laying from middle India to the far north-western BoB, and it result that the large-scale subsidence over the central dry zone region by a strong rising motion over convective areas nearby.\u003c/p\u003e \u003cp\u003eBy immediately increasing (declining) rainfall over Myanmar, the strong (weak) amplitude of MLLJ while active (break) across each sea gives a clear indicator regarding the transit of moisture distribution from the BoB to Indochina mainland. The weakened (intensified) mid-tropospheric wind circulation linked to MLLJ is accurately approximated during break (active) phases. A notable and important aspect of active periods is the occurrence of anomaly cyclonic circulation over northeastern India and bending along the adjacent sea areas, as well as anomaly anti-cyclonic circulation over the northwestern Arabian Peninsula regions and bending along the adjoining two sea areas. The shifting of easterly jet towards the south from its mean position in active spells and the intrusion of subtropical westerlies into lower latitudes in the break period. During break spells, TEJ/MLLJ will be weaker and dislocated over upper/lower troposphere, but during active spells, they will be substantially stronger.\u003c/p\u003e \u003cp\u003eWe explained that during break/active spells, spatial patterns significantly deviate from those of inter-annual variability, particularly those linked to the majority of meteorological variables. A moderate to slight negative correlation was found with well-known climate indicies, such as: DMI, Nino-3.4, EMI, and WNPMI during break days, while a positive correlation was observed with SOI TNA and NP during break days. On intra-seasonal periods, the signal over the eastern portion of the equatorial Pacific Ocean is comparable to that on inter-annual time scales. The study clearly showed that La Ni\u0026ntilde;a encourages more break days than active days because the occurrence of break days is increasing during La Ni\u0026ntilde;a than El Ni\u0026ntilde;o phases. The study is the first to demonstrate the distinction in meridional vertical circulation between strong break events with heat trough circulation and moist convective active spells over the studied region. To develop effective forecast methodologies, it is necessary to understand the mechanisms that govern the transitions in time and space from a heat low circulation to a convective moist regime. Future research should extend these findings to encompass the entire MSWM system.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eSource Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReanalysis rainfall, component winds, OLR, and Mean Seal Level Pressure netcdf4 data for this study were downloaded from the ECMWF data portal. And this is a fifth-generation ECMWF reanalysis dataset with a geographical resolution of 0.25 0.25 for global climate parameters over the previous decades are used to support the findings of this study are included within the article. Data is now freely available from 1950 to the present by registration at ECMWF. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe actual monthly rainfall observation data from 79 observation stations used to support the findings of this study was provided under permission by Myanmar\u0026apos;s Department of Meteorology and Hydrology (DMH) and hence cannot be freely distributed. Requests for access to these data should be made to the Director-General of DMH, Myanmar. https://www.moezala.gov.mm/ \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOpen Grads (OpenGrADS - Home), Climate data operator (https://code.mpimet.mpg.de/ ) and IBM SPSS are mainly used for this study. Among of these first two are opensource application for everyone.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConflicts of Interest\u003c/h2\u003e\n\u003cp\u003eI declared that there is no potential conflict of interest with any of the following statements.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eFor any component of the submitted work, the author received no cash or services from a third party (government, commercial, private foundation, etc). (including but not limited to grants, data monitoring board, study design, manuscript preparation, statistical analysis, etc.).\u003c/li\u003e\n \u003cli\u003eThe author is not affiliated with any entity that has a direct or indirect financial interest in the manuscript\u0026apos;s subject matter.\u003c/li\u003e\n \u003cli\u003eThe author was involved in the following aspects of the project: (a) idea and design, or data analysis and interpretation; (b) authoring the article or critically reviewing it for essential intellectual content; and (c) approval of the final version.\u003c/li\u003e\n \u003cli\u003eThis work has not been submitted to, and is not currently being reviewed by, any other journal or publishing venue.\u003c/li\u003e\n \u003cli\u003eThe author has no patents that are broadly relevant to the work, whether proposed, pending, or issued.\u003c/li\u003e\n \u003cli\u003eThe author received no payment or services from a third party for any aspect of the submitted work (government, commercial, private foundation, etc). (including but not limited to grants, data monitoring board, study design, manuscript preparation, statistical analysis, etc.).\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003eFunding Statement\u003c/h2\u003e\n\u003cp\u003eThis research and publishing are being carried out using self-funding.\u003c/p\u003e\n\u003ch2\u003eORCID\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eKyaw Than Oo https://orcid.org/0000-0003-1727-3462\u003c/p\u003e\n\u003ch2\u003eAuthor Statement\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eKyaw Than Oo\u003c/strong\u003e: Conceptualization, Methodology, Data curation, Writing- Original draft preparation. Visualization, Investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKazora Jonah\u003c/strong\u003e: Reviewing.\u003c/p\u003e\n\u003ch2\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/h2\u003e\n\u003cp\u003eDuring the preparation of this work the author(s) used plagiarism checker and grammerly in order to check the plagiarism and grammer mistakes. After using this tools, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.\u003c/p\u003e\n\u003ch2\u003eAcknowledgment\u003c/h2\u003e\n\u003cp\u003eThe researcher expresses special thanks to all Professors who approve and support this research and Nanjing University of Information Science for support to come out this research. I would also like to extend my gratitude to Professor Haishan Chen from Nanjing University of Information Science and Technology, for supervisor this paper and his others, support during this research. \u0026nbsp;The author acknowledges heartfelt thanks to the scientists of the ECMFW for supporting ERA5 datasets and the Department of Meteorology and Hydrology for support the data of Myanmar. Additionally, the author would like to thank three reviewers for their constructive and insightful reviews and comments which have significantly helped to improve the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKulkarni A, S. S. R. K (2009) Spatial variability of intra-seasonal oscillations during extreme Indian monsoons. Int J Climatol 29:1945\u0026ndash;1955\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmemiya A, Sato K (2020) Characterizing quasi-biweekly variability of the Asian monsoon anticyclone using potential vorticity and large-scale geopotential height field. Atmos Chem Phys 20(22):13857\u0026ndash;13876. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/ACP-20-13857-2020\u003c/span\u003e\u003cspan address=\"10.5194/ACP-20-13857-2020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnnamalai H, Slingo JM (2001) Active/break cycles: Diagnosis of the intraseasonal variability of the Asian Summer Monsoon. Clim Dyn 18(1\u0026ndash;2):85\u0026ndash;102. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s003820100161\u003c/span\u003e\u003cspan address=\"10.1007/s003820100161\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAung LL, Zin EE, Theingi P, Elvera N, Aung PP, Han TT, Oo Y, Skaland RG (2017) Myanmar Climate Report. Norwgian Meterological Inst 9:105\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlanford HF (1886) Rainfall of India. Mem Ind Met Dept 2:217\u0026ndash;448\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi C, M. Y (1996) The onset and interannual variability of the Asian Summer Monsoon in relation to land-sea thermal contrast. J Clim 9:358\u0026ndash;375. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/1520-0442(1996)009\u0026lt;0358:toaivo\u0026gt;2.0.co\u003c/span\u003e\u003cspan address=\"10.1175/1520-0442(1996)009%3C0358:toaivo%3E2.0.co\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarlson K, Winquist J (2014) Introduction to statistics and frequency distribution. Introduction Statistics: Act Learn Approach, 1\u0026ndash;32\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChakraborty A, Agrawal S (2017) Role of west Asian surface pressure in summer monsoon onset over central India. Environ Res Lett 12(7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1748-9326/AA76CA\u003c/span\u003e\u003cspan address=\"10.1088/1748-9326/AA76CA\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen X, Wang Y, Zhao K (2015) Synoptic flow patterns and large-scale characteristics associated with rapidly intensifying tropical cyclones in the South China Sea. Mon Weather Rev 143(1):64\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/MWR-D-13-00338.1\u003c/span\u003e\u003cspan address=\"10.1175/MWR-D-13-00338.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSikka DR, G. S (1980) On the maximum cloud zone and the ITCZ over India longitude during the southwest monsoon. Mon Wea Rev 108:1840\u0026ndash;1853. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/1520-0493(1980)108\u0026lt;1840:otmcza\u0026gt;2.0.co\u003c/span\u003e\u003cspan address=\"10.1175/1520-0493(1980)108%3C1840:otmcza%3E2.0.co\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDima IM, Wallace JM (2003) On the seasonality of the Hadley Cell. J Atmos Sci 60(12):1522\u0026ndash;1527. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/1520-0469(2003)060\u0026lt;1522:OTSOTH\u0026gt;2.0.CO;2\u003c/span\u003e\u003cspan address=\"10.1175/1520-0469(2003)060%3C1522:OTSOTH%3E2.0.CO;2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing Q, Wang B (2005) Circumglobal teleconnection in the Northern Hemisphere summer. J Clim 18(17):3483\u0026ndash;3505. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/JCLI3473.1\u003c/span\u003e\u003cspan address=\"10.1175/JCLI3473.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEndo N, Matsumoto J, Lwin T (2009) Trends in precipitation extremes over Southeast Asia. Sci Online Lett Atmos 5(1):168\u0026ndash;171. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2151/sola.2009-043\u003c/span\u003e\u003cspan address=\"10.2151/sola.2009-043\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGadgil S (2003) The Indian Monsoon and its variability. Annu Rev Earth Planet Sci 31(1):429\u0026ndash;467. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev.earth.31.100901.141251\u003c/span\u003e\u003cspan address=\"10.1146/annurev.earth.31.100901.141251\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhosh S, Vittal H, Sharma T, Karmakar S, Kasiviswanathan KS, Dhanesh Y, Sudheer KP, Gunthe SS (2016) Indian Summer Monsoon Rainfall: Implications of Contrasting Trends in the Spatial Variability of Means and Extremes. PLoS ONE 11(7):e0158670. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/JOURNAL.PONE.0158670\u003c/span\u003e\u003cspan address=\"10.1371/JOURNAL.PONE.0158670\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoswami BN, Ajayamohan RS, Xavier PK, Sengupta D (2003) Clustering of synoptic activity by Indian summer monsoon intraseasonal oscillations. Geophys Res Lett 30(8). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2002GL016734\u003c/span\u003e\u003cspan address=\"10.1029/2002GL016734\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHersbach H, Bell B, Berrisford P, Hirahara S, Hor\u0026aacute;nyi A, Mu\u0026ntilde;oz-Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A, Soci C, Abdalla S, Abellan X, Balsamo G, Bechtold P, Biavati G, Bidlot J, Bonavita M, Th\u0026eacute;paut JN (2020a) The ERA5 global reanalysis. Q J R Meteorol Soc 146(730):1999\u0026ndash;2049. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/QJ.3803\u003c/span\u003e\u003cspan address=\"10.1002/QJ.3803\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHersbach H, Bell B, Berrisford P, Hirahara S, Hor\u0026aacute;nyi A, Mu\u0026ntilde;oz-Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A, Soci C, Abdalla S, Abellan X, Balsamo G, Bechtold P, Biavati G, Bidlot J, Bonavita M, Th\u0026eacute;paut JN (2020b) The ERA5 global reanalysis. Q J R Meteorol Soc 146(730):1999\u0026ndash;2049. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/QJ.3803\u003c/span\u003e\u003cspan address=\"10.1002/QJ.3803\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang B, Thorne PW, Banzon VF, Boyer T, Chepurin G, Lawrimore JH, Menne MJ, Smith TM, Vose RS, Zhang HM (2017) Extended reconstructed Sea surface temperature, Version 5 (ERSSTv5): Upgrades, validations, and intercomparisons. J Clim 30(20):8179\u0026ndash;8205. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/JCLI-D-16-0836.1\u003c/span\u003e\u003cspan address=\"10.1175/JCLI-D-16-0836.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang G, Hu K, Xie SP (2010) Strengthening of tropical Indian Ocean teleconnection to the Northwest Pacific since the mid-1970s: an atmospheric GCM study. J Clim 23(19):5294\u0026ndash;5304. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/2010jcli3577.1\u003c/span\u003e\u003cspan address=\"10.1175/2010jcli3577.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrishnamurthy V, Shukla J (2000) Intraseasonal and interannual variability of rainfall over India. J Clim 13(24):4366\u0026ndash;4377. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/1520-0442(2000)013\u0026lt;0001:IAIVOR\u0026gt;2.0.CO;2\u003c/span\u003e\u003cspan address=\"10.1175/1520-0442(2000)013%3C0001:IAIVOR%3E2.0.CO;2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrishnamurthy V, Shukla J (2008) Seasonal persistence and propagation of intraseasonal patterns over the Indian summer monsoon region. Clim Dyn 30(4):353\u0026ndash;369. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00382-007-0300-7\u003c/span\u003e\u003cspan address=\"10.1007/s00382-007-0300-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLwin T (2000) \u003cem\u003eThe Prevailing Synoptic Situations in Myanmar.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajeevan M, S. G. J. B (2010) Active and break spells of the Indian summer monsoon. J Earth Syst Sci 119(3):229\u0026ndash;247\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh N, A. R (2010) The wet and dry spells across India during 1951\u0026ndash;2007. J Hydrometeorol 11:26\u0026ndash;45\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNarayana Rao T, Saikranthi K, Radhakrishna B, Vijaya B, Rao S (2016) Differences in the Climatological Characteristics of Precipitation between Active and Break Spells of the Indian Summer Monsoon. J Clim 29(21):7797\u0026ndash;7814. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/jcli-d-16-0028.1\u003c/span\u003e\u003cspan address=\"10.1175/jcli-d-16-0028.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen H, Evans A, Lucas C, Smith I, Timbal B (2013) The hadley circulation in reanalyses: Climatology, variability, and Change. J Clim 26(10):3357\u0026ndash;3376. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/JCLI-D-12-00224.1\u003c/span\u003e\u003cspan address=\"10.1175/JCLI-D-12-00224.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOo KT (2022) Interannual Variability of Winter Rainfall in Upper Myanmar. J Sustain Environ Manage 1(3):344\u0026ndash;358. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.3126/josem.v1i3.48001\u003c/span\u003e\u003cspan address=\"10.3126/josem.v1i3.48001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOo KT (2023) \u003cem\u003eClimatology Definition of the Myanmar Southwest Monsoon (MSwM): Change Point Index (CPI)\u003c/em\u003e. \u003cem\u003e2023\u003c/em\u003e(Fig. 2)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOo KT, Haishan C, Jonah K (2023) Climate Change Impact on the Trigger of Natural Disasters over South-Eastern Himalayas Foothill Region of Myanmar: Extreme Rainfall Analysis. \u003cem\u003eInternational Journal of Geophysics\u003c/em\u003e, \u003cem\u003e2023\u003c/em\u003e, 2186857. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2023/2186857\u003c/span\u003e\u003cspan address=\"10.1155/2023/2186857\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOo KT, Oo KT, Oo KT, Dipole IO (2019) \u003cem\u003eHow El-Ni\u0026ntilde;o / La-Nina \u0026amp; India Ocean Dipole (IDO) Influence on Myanmar Rainfall\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePai DS, Sridhar L, Ramesh Kumar MR (2016) Active and break events of Indian summer monsoon during 1901\u0026ndash;2014. Clim Dyn 46(11\u0026ndash;12):3921\u0026ndash;3939. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/S00382-015-2813-9/METRICS\u003c/span\u003e\u003cspan address=\"10.1007/S00382-015-2813-9/METRICS\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrathipati VK, Viswanadhapalli Y, Chennu VN, Dasari HP (2021a) Study of Active and Break Spell Phenomena of Indian Summer Monsoon Using WRF Downscaled Data. Pure appl Geophys 178(10):4195\u0026ndash;4219. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00024-021-02837-5\u003c/span\u003e\u003cspan address=\"10.1007/s00024-021-02837-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrathipati VK, Viswanadhapalli Y, Chennu VN, Dasari HP (2021b) Study of Active and Break Spell Phenomena of Indian Summer Monsoon Using WRF Downscaled Data. Pure appl Geophys 178(10):4195\u0026ndash;4219. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/S00024-021-02837-5/METRICS\u003c/span\u003e\u003cspan address=\"10.1007/S00024-021-02837-5/METRICS\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePriv\u0026eacute; NC, Plumb AR (2007) Monsoon dynamics with interactive forcing. Part I: Axisymmetric studies. J Atmos Sci 64(5):1417\u0026ndash;1430. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/JAS3916.1\u003c/span\u003e\u003cspan address=\"10.1175/JAS3916.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrishnan R, C. Z. M. S (2000) Dynamics of breaks in the Indian summer monsoon. J Atmos Sci 57(9):1354\u0026ndash;1372. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/1520-0469(2000)057\u0026lt;1354:dobiti\u0026gt;2.0.co\u003c/span\u003e\u003cspan address=\"10.1175/1520-0469(2000)057%3C1354:dobiti%3E2.0.co\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajeevan M, Gadgil S, Bhate J (2010a) Active and break spells of the Indian summer monsoon. J Earth Syst Sci 119(3):229\u0026ndash;247. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12040-010-0019-4\u003c/span\u003e\u003cspan address=\"10.1007/s12040-010-0019-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajeevan M, Gadgil S, Bhate J (2010b) Active and break spells of the indian summer monsoon. J Earth Syst Sci 119(3):229\u0026ndash;247. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/S12040-010-0019-4/METRICS\u003c/span\u003e\u003cspan address=\"10.1007/S12040-010-0019-4/METRICS\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamamurthy K (1969) Monsoon of India: Some aspects of the \u0026lsquo;break\u0026rsquo; in the Indian southwest monsoon during July and August. Forecasting Manual 1\u0026ndash;57 IV 18.3, India Met. Dept.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen YY, Ren GY, Sun XB, Shrestha AB, You QL, Zhan YJ, Rajbhandari R, Zhang PF, Wen KM (2017) Observed changes in surface air temperature and precipitation in the Hindu Kush Himalayan region over the last 100-plus years. Adv Clim Change Res 8(3):148\u0026ndash;156. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/J.ACCRE.2017.08.001\u003c/span\u003e\u003cspan address=\"10.1016/J.ACCRE.2017.08.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGadgil S (2003) P. V. J. On breaks of the Indian monsoon. \u003cem\u003eProc. Indian Acad. Sci. (Earth Planet. Sci.)\u003c/em\u003e, \u003cem\u003e112\u003c/em\u003e, 529\u0026ndash;558\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGadgil S, P. J (2003a) On breaks of the Indian monsoon. J Earth Syst Sci 112:529\u0026ndash;558\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGadgil S, P. J (2003b) On breaks of the Indian monsoon. Proc Indian Acad Sci (Earth Planet Sci) 112:529\u0026ndash;558\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSatyanarayana GC, Dodla VBR, Srinivas D (2020) Decreasing southwest monsoon rainfall over Myanmar in the prevailing global warming era. Meteorol Appl 27(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/MET.1816\u003c/span\u003e\u003cspan address=\"10.1002/MET.1816\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchneider T, Bordoni S (2008) Eddy-mediated regime transitions in the seasonal cycle of a hadley circulation and implications for monsoon dynamics. J Atmos Sci 65(3):915\u0026ndash;933. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/2007JAS2415.1\u003c/span\u003e\u003cspan address=\"10.1175/2007JAS2415.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSein ZMM, Ogwang B, Ongoma V, Ogou FK, Batebana K (2015) Inter-annual variability of May-October rainfall over Myanmar in relation to IOD and ENSO. J Environ Agricultural Sci 4:28\u0026ndash;36\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSein ZMM, Ullah I, Saleem F, Zhi X, Syed S, Azam K (2021) Interdecadal variability in myanmar rainfall in the monsoon season (May\u0026ndash;october) using eigen methods. Water (Switzerland) 13(5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w13050729\u003c/span\u003e\u003cspan address=\"10.3390/w13050729\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSein ZMM, Zhi X (2016) Interannual variability of summer monsoon rainfall over Myanmar. Arab J Geosci 9(6). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/S12517-016-2502-Y\u003c/span\u003e\u003cspan address=\"10.1007/S12517-016-2502-Y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSein ZMM, Zhi X, Ullah I, Azam K, Ngoma H, Saleem F, Xing Y, Iyakaremye V, Syed S, Hina S, Nkunzimana A (2022) Recent variability of sub-seasonal monsoon precipitation and its potential drivers in Myanmar using in-situ observation during 1981\u0026ndash;2020. Int J Climatol 42(6):3341\u0026ndash;3359. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/JOC.7419\u003c/span\u003e\u003cspan address=\"10.1002/JOC.7419\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaw TA (2014) On the role of planetary-scale waves in the abrupt seasonal transition of the Northern Hemisphere general circulation. J Atmos Sci 71(5):1724\u0026ndash;1746. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/JAS-D-13-0137.1\u003c/span\u003e\u003cspan address=\"10.1175/JAS-D-13-0137.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShun CM, Chan PW (2008) Applications of an infrared Doppler lidar in detection of wind shear. J Atmos Ocean Technol 25(5):637\u0026ndash;655. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/2007JTECHA1057.1\u003c/span\u003e\u003cspan address=\"10.1175/2007JTECHA1057.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakahashi HG, Yasunari T (2006) A climatological monsoon break in rainfall over Indochina - A singularity in the seasonal march of the Asian summer monsoon. J Clim 19(8):1545\u0026ndash;1556. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/JCLI3724.1\u003c/span\u003e\u003cspan address=\"10.1175/JCLI3724.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRao TN, K. U. T. S. D. R (2009) Differences in draft core statistics from the wet spell to dry spell over Gandaki, India (1358N, 7928E). Mon Weather Rev 2009:4293\u0026ndash;4306\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrenberth KE, Shea DJ (2005) Relationships between precipitation and surface temperature. Geophys Res Lett 32(14):1\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2005GL022760\u003c/span\u003e\u003cspan address=\"10.1029/2005GL022760\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrishnamurthy V, J. S (2008) Seasonal persistence and propagation of intraseasonal patterns over the Indian summer monsoon region. Clim Dyn 30:353\u0026ndash;369\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eViswanadhapalli Y, Srinivas CV, Basha G, Dasari HP, Langodan S, Venkat Ratnam M, Hoteit I (2019) A diagnostic study of extreme precipitation over Kerala during August 2018. Atmospheric Sci Lett 20(12):12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/asl.941\u003c/span\u003e\u003cspan address=\"10.1002/asl.941\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang B, Ding Q, Joseph PV (2009) Objective definition of the Indian summer monsoon onset. J Clim 22(12):3303\u0026ndash;3316. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/2008JCLI2675.1\u003c/span\u003e\u003cspan address=\"10.1175/2008JCLI2675.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWebster PJ, Maga\u0026ntilde;a VO, Palmer TN, Shukla J, Tomas RA, Yanai M, Yasunari T (1998) Monsoons: processes, predictability, and the prospects for prediction. J Geophys Research: Oceans 103(C7):14451\u0026ndash;14510. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/97jc02719\u003c/span\u003e\u003cspan address=\"10.1029/97jc02719\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWebster PJ, Yang S (1992) Monsoon and Enso: Selectively Interactive Systems. Q J R Meteorol Soc 118(507):877\u0026ndash;926. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/qj.49711850705\u003c/span\u003e\u003cspan address=\"10.1002/qj.49711850705\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaw Z, Fan ZX, Br\u0026auml;uning A, Liu W, Gaire NP, Than KZ, Panthi S (2021) Monsoon precipitation variations in Myanmar since AD 1770: linkage to tropical ocean-atmospheric circulations. Clim Dyn 56(9\u0026ndash;10):3337\u0026ndash;3352. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00382-021-05645-8\u003c/span\u003e\u003cspan address=\"10.1007/s00382-021-05645-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Alexander L, Hegerl GC, Jones P, Tank AK, Peterson TC, Trewin B, Zwiers FW (2011) Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdiscip Rev Clim Change 2(6):851\u0026ndash;870. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/wcc.147\u003c/span\u003e\u003cspan address=\"10.1002/wcc.147\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZin EE, Aung LL, Zin EE, Theingi P, Elvera N, Aung PP, Han TT, Oo Y, Skaland RG (2017) Myanmar Climate Report. \u003cem\u003eNorwgian Meterological Institute\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, 105. http://files/679/MyanmarClimateReportFINAL11Oct2017.pdf\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Monsoon Rainfall, Monsoon Break, MSWM, Myanmar Rainfall","lastPublishedDoi":"10.21203/rs.3.rs-4936295/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4936295/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe term ``break`` is traditionally applied only to dry spells occurring after the monsoon onset in the region. Simply put, the daily rainfall of the monsoon is paused over the region, for a few days, called a \u0026ldquo;break spell.\u0026rdquo; The researchers have suggested that the standardized anomalies of three consecutive days of rainfall should prevail to categorize the active and break spells. This study examined break spells and active spells on the inter-annual, intra-seasonal, and decadal scales by examining the frequency and spatial distribution of daily rainfall occurrences of different intensities linked to break and active events over the mainland Indochina region. The difference in the vertical meridional circulation between the active spells with moist convection and intense break events with heat through circulation was explained by various atmospheric parameters. La Ni\u0026ntilde;a encourages more break days than active days, and the distinction in vertical meridional circulation between intense break events with a heat trough type circulation and active spells with moist convection is crucial for developing suitable prediction tools.\u003c/p\u003e","manuscriptTitle":"Intra-seasonal Variability and Physical Characteristics of Break and Active Phases in the Mainland Indochina Southwest Monsoon","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-23 14:42:24","doi":"10.21203/rs.3.rs-4936295/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"178abf9f-31f9-444e-b27a-9e9b522d08d2","owner":[],"postedDate":"August 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-23T14:42:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-23 14:42:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4936295","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4936295","identity":"rs-4936295","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}