Thundercloud assessment for the years 1990–2019 over the Baghdad airport station

Thundercloud assessment for the years 1990–2019 over the Baghdad airport station | 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 Thundercloud assessment for the years 1990–2019 over the Baghdad airport station Imad Al- Khulaifawi, Aqeel Mutar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8683928/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Apr, 2026 Read the published version in Theoretical and Applied Climatology → Version 1 posted 9 You are reading this latest preprint version Abstract Thunderstorms are a meteorological phenomenon caused by atmospheric instability. Temperature, wind speed, barometric pressure, rain, and hail all abruptly vary during thunderstorms. In order to create statistical classifications of thundercloud frequencies in the city of Baghdad, this work assesses thunderclouds in terms of their structure, physical attributes, and accompanying atmospheric stability indicators. The Baghdad airport weather station and ERA-5 reanalysis data provided the data for the years 1990 to 2019. Following processing and calculation, the outcome reveals that 1994 had the highest yearly frequency of thunderstorms. According to the study, thunderstorm frequency is highest in the spring (58.56%). The TT and K indices were computed. The TT index category 51–52, which accounts for roughly 36.75% of all cases, is most frequently linked to thundercloud development. About 35.26% of all occurrences fall into the k-index category 26–30, which is most commonly linked to thundercloud production. Temperature, lifting condensation level (LCL), and equilibrium level (EL) are all moderately to significantly positively connected with one another, according to Pearson correlation analysis. However, the equilibrium level and thickness have been found to be strongly positively correlated with the convective accessible potential energy (CAPE). Thunderstorm Atmospheric instability K-index Totals Totals Index CAPE Baghdad airport 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 1. Introduction One of the most dangerous mesoscale convective systems in the atmosphere, thunderstorms are caused by a number of factors, including moisture in the lower atmosphere and the instability and lifting of air parcels. When thick layers of warm, humid air ascend to colder parts of the atmosphere, thunderstorms are created. Updraft moisture condenses there to create cumulonimbus clouds, which in turn produce precipitation, snow, and hail storms, which result in fatalities and crop damage. A thunderstorm is defined by the World Meteorological Organization as one or more sudden electrical discharges accompanied by an intense or thundering sound and a burst of light (lightning) (Encyclopedia Britannica, 2021 ; Mondal et al., 2022 ; Singh & Bhardwaj, 2019 ). Although thunderstorms can form and grow anywhere in the world, they are most common in the mid-latitudes, where cooler air from the polar latitudes collides with warm, humid air from the tropical latitudes during the period of instability that occurs in the spring and autumn (National Severe Storms Laboratory, 2020 ). Atmospheric instability, adequate moisture in the lower troposphere, and lifting mechanisms (such as convection, frontal lift, and convergence) are the three fundamental conditions for thunderstorm development (Arora et al., 2023 ; Bennett et al., 2006 ; Kunz et al., 2020 ; Trapp, 2013 ). They also found that Thunderstorms (TSs) were most commonly observed between March to May in Iraq. One of the most catastrophic meteorological phenomena, thunderstorms have a duration of less than an hour and a spatial extent of a few kilometers to several hundred kilometers (Saha et al., 2014 ; Umakanth et al., 2020 ). Deep convective systems and occasionally cumulonimbus clouds, which produce intense lightning, strong winds, and heavy rainfall, can form from thunderstorms. Despite their brief duration, these thunderstorms have the potential to cause significant harm to people and property. Most of the world's thunderstorms happen over tropical areas. The hot and humid conditions characteristic of tropical regions plays a significant role in the development of thunderstorms (Bhardwaj et al., 2017 ). Thermodynamic and kinematic conditions are also important. Studies on the effects of Convective Inhibition (CIN) and the Lifting Condensation Level (LCL) on the occurrence of strong convective events are among the body of studies in this field (Chaboureau et al., 2004 ; Davies, 2004 ). Indices like the K-index, Lifted Index, and Convective Available Potential Energy (CAPE) are crucial for meteorologists to assess regional convective potential. They help pinpoint areas with an unstable atmosphere, which is a key condition for thunderstorm development (Humood & Abbas, 2017 ). When predicting strong convective phenomena, atmospheric instability indices are widely used. However, a variety of factors that prevent the development of the vertical movement of air can have a significant impact on their verifiability. Either upper-air sounding data was used in published studies on the temporal and spatial variability of instability indices (Derubertis, 2006 ; Madineni et al., 2013 ). There are numerous studies in the literature that have conducted TS analyses of the world (for different regions). Tyagi, ( 2007 ). A climatological study of thunderstorms was conducted, encompassing 450 observation stations in India and neighboring countries. Of these, 390 were surface observatories operated by the Indian Meteorological Department, 50 were managed by the Indian Air Force, and the remainder were run by neighboring countries. Mohee & Miller, ( 2010 ). Thunderstorms in North Dakota were analyzed using radar and ground observations data collected from 2002 to 2006. Allen & Karoly, ( 2013 ). Conducted a climatological study of severe thunderstorms in Australia, which were analyzed using the ERA-Interim reanalysis, observed between 1979 and 2011, and investigated their relationship with El-Nino during the warm season (April to September). Jussi et al. ( 2021 ). Researchers studied the risks associated with thunderstorms and analyzed the usefulness of different data sources in machine learning models. The study showed that radar data is the most important variable for forecasting in general. Satellite imagery was also found to be useful for all forecast variables and provides a viable alternative in areas where radar data is unavailable. While lightning data were particularly useful for lightning forecasting, their usefulness was limited for other hazards. The study found that information contained in observational data during the forecast period could reasonably compensate for the omission of NWP data. Finally, the study found no evidence that digital elevation model (DEM) data provide any forecasting benefit. Al-Khulaifawi & Ioshpa. ( 2025 ). A study on the development and frequency of thunderstorms is of particular importance. The analysis of thunderstorm activity relies on visual data obtained from long-term observations. This study is based on daily archive data collected from two meteorological stations in Russia and three stations in Iraq over twenty years (2000–2019). The analysis revealed that thunderstorms in Iraq are most prevalent in March and April, particularly at the Khanaqin station, where 42% of thunderstorms occur in the mountainous northern region of Iraq. In the Rostov region of Russia, the highest frequency of thunderstorms occurs in June and July, accounting for 50.2%. The total number of days with thunderstorms at research stations in Iraq was 589, while at research stations in Russia it was 729. Despite the general decline in thunderstorm activity in the Rostov region, it is still classified as an area with high thunderstorm activity. In contrast, Iraq, particularly in the mountainous Khanaqin region, exhibits an increasing trend in thunderstorm activity over the past 20 years. Researchers frequently utilize atmospheric stability indices to analyze thunderstorms. Kunz, ( 2007 ). Utilized 20 distinct stability indices, such as the convective available potential energy (CAPE), K index, and total totals index, to assess Germany's thunderstorm features using radiosonde and SYNOP observations. Kolay et al. ( 2025 ). This study analyzes thunderstorm activity at Istanbul International Airport using atmospheric stability indices data for the ground level, and upper levels were obtained from the Turkish State Meteorological Service for 29 October 2018 and 1 January 2023. With a total of 51 occurrences, thunderstorms were seen to occur more frequently in the summer. With 32 thunderstorm events, June had the most of any month. This results in eight incidents annually on average. Transitions between troughs and cold fronts accounted for 72.22% of the total. Compared to other stability indices, the K index and Total Totals index provided a better representation of the thunderstorm events. These two stability indicators accounted for 75% of the total thunderstorm days. During thunderstorm events, the K and Total Totals indices are better represented, and the convective available potential energy (CAPE) values, which are low. (Yavuz, 2024 ). This study analyzed thunderstorms in Istanbul from 2001 to 2022. Using thermodynamic indices and atmospheric stability criteria, 703 thunderstorms were identified during this period. The study revealed that most of the storms (69%) occurred during the warm season (May–September), and that the majority (93%) lasted for a few hours (0–3 hours). The thermodynamic indices and atmospheric stability criteria used in the research included the (SI), (LI), (SWEAT), (KI), (TTI), (CAPE), (CIN), and (BRN). Analyses revealed significant differences between the index values ​​on thunderstorm and non-thunderstorm days. The highest predictive power was observed in the summer, while the lowest was recorded in the winter. This work aims to analyze and evaluate the characteristics of thunderstorms over Baghdad airport stations during the period 1990–2019 and their frequency, temporal distribution, and associated weather factors. This analysis attempts to estimate the trends of these thermodynamic indices and parameters throughout the study period, in light of the continuous climate change in Baghdad. 2. Materials and methods 2.1. Study stations Iraq is situated between 29˚5´ and 37˚22' N latitude and 38˚45´ and 48˚45' E longitude. Iraq is bordered to the north by Turkey, to the east by Iran, to the south and east by Saudi Arabia, Kuwait, and the Persian Gulf, and to the west by Syria and Jordan. The country's capital, Baghdad, is situated in the center of the country. The Tigris and Euphrates are the two major rivers that flow through the country from north to south. Its geographical diversity includes mountainous regions in the north and northeast, plateau regions at lower altitudes to the south of the mountains, alluvial plains, and western plateaus. The elevation and location of these regions significantly influence the climatic conditions across Iraq (Al-Ansari, 2013 ; Sultan, 1986 ; Zakaria et al., 2012 ). Figure 1 shows the topography of Iraq and the locations of the selected Baghdad airport station. 2.2. data sources 2.2.1. Meteorological Stations Observations Hourly meteorological data (archived) on thunderstorms, temperature, relative humidity, and pressure from 1990 to 2019, specifically for key times (00:00, 06:00, 12:00, and 18:00) for the Baghdad airport meteorological station, were obtained in real-time from the Iraqi Meteorological Organization. Before analysis, the quality and continuity of the data series were checked. Baghdad airport station is located at 33.18° N, 44.24° E, and 31.7 m above sea level. 2.2.2. Reanalysis Data (ERA5) The fifth iteration of the ECMWF Integrated Forecast System is called ERA5. Three completely connected components—atmosphere, land surface, and ocean waves—are incorporated into the forecast model. On the surface (single or upper air level), it offers data on a variety of factors (Hersbach et al., 2023 ). The utilized data have a temporal resolution of 00, 06, 12, and 18 UTC and a spatial resolution of 0.25° × 0.25° for the atmosphere throughout the 1990–2019 period. The pressure levels are 850, 700, and 500 hPa. Data was obtained temporally and spatially for the station in NetCD format and processed using MATLAB 2020. The result was plotted using Origin Pro-21. The model synopsis is summarized in Table 1 . 2.2.3. Diagnosis of synoptic conditions Diagnosing the atmospheric conditions associated with a thunderstorm event can provide a clear picture of the dynamic causes and the general atmospheric flow pattern underlying the signal or trigger that initiated the baroclinic instability, which ultimately caused the event. Analyzing upper-level synoptic weather maps, spatial wind speed, and streamlines at the 300 hPa pressure level can reveal the nature of the polar jet stream's movement and its northward or southward meander patterns. This allows for the identification of the weather event's initiation and the intensity of the baroclinic instability, depending on the direction and amplitude of the meander pattern in the polar jet stream (Rossby wave). Identifying extratropical troughs and ridges at the 500 hPa pressure level is crucial for understanding how the Rossby wave's meander pattern deepens to form a mid-latitude depression, what the prevailing pattern is, and determining cyclogenesis. Meanwhile, the associated surface frontal system can be identified by analyzing surface wind patterns and surface temperature distributions (Mutar et al., 2021 ). 2.3. The Calculation of some indices and parameter 2.3.1. K – index This index considers the contribution of moist air at 700 hPa to the formation of thunderstorms. KI rises when static stability decreases between 850 and 500 hPa, moisture increases at 850 hPa, and relative humidity increases at 700 hPa. The definition of the K index is as follows. Table 2 shows atmospheric conditions by K-index values (George, 1960 ). $$\:K=T\:\left(850mb\right)+Td\:\left(850mb\right)-T\:\left(500mb\right)-DD\:\left(700mb\right)$$ 1 2.3.2. Totals Totals index Total Totals Index is similar to K-Index. The temperature difference between (T 850) and (T500) as well as the difference between the dew point at the (Td850) and the temperature at 500 hPa are used to express (formula) the Total Totals Index (Miler, 1972 ; Showalter, 1953 ; Peppler & Lamb, 1989 ). $$\:TT=T850mb+Td850mb-2\left(T500mb\right)$$ 2 Information about the state of the atmosphere according to the value Totals Totals Index ranges of this parameter, as shown in Table 3 . 2.3.3. Convective Available Potential Energy (CAPE) The quantity of energy accessible during convection, or CAPE, is determined by integrating the parcel's local buoyancy vertically from the level of free convection (LFC) to the equilibrium level (EL). Its foundation is the integration of the atmosphere's vertical profile. Smaller values of this parameter show that the atmosphere is stable. While larger values show that the atmosphere is unstable (Lucas et al., 1994 ; Miler, 1972 ; Showalter, 1953 ). It is calculated by the following formula: $$\:CAPE=g*{\int\:}_{LFC}^{EL}\left(Tp-Te\right)*dz/Te$$ 3 In this formula: LFC: is the free convection level, expressed in hPa or mb; LE: level of equilibrium, expressed in hPa or mb; Te: is the surrounding temperature in degrees Celsius; Tc: is the temperature of the cloud parcel in degrees Celsius; g: is the gravitational acceleration, expressed in m/s 2 . dz: is the layer's thickness between the LFC and EL levels, expressed in m or km. Information about the state of the atmosphere according to the value (CAPE) ranges of this parameter, as shown in Table 4 . Using the LCL values, the cloud base height is roughly estimated, which is obtained using surface temperature and dew point temperature information (the air temperature equals to the dew point temperature at this altitude). In order to calculate convective available potential energy (CAPE) and convective inhibition (CIN) and analyze thunderstorm potential and severity across any region, the LCL values are crucial. To determine the direct relationship with the total water vapor content, cloud height variability, seasonal and regional variability, etc., the LCL values can be employed as CBH (Craven et al., 2002b ; Xu et al., 2019 ). While EL, the height at which the temperature of the flowing air parcel equals the ambient temperature, is known as the equilibrium level, the EL values can be employed as CTH (Cinaroglu & Unutulmaz, 2018 ). And calculating the depth of the cloud, or vertical thickness, by the disparity between the cloud's base and summit heights is known as this parameter (Shmeter, 1972 ). This research plan describes (Fig. 2 ) a methodology for analyzing climate data to study and classify thunderstorm events using an integrated approach. It is based on, first, the primary data used, which are on ERA-5 (the fifth generation of atmospheric reanalysis from the European Centre for Medium-Range Weather Forecasts) and data from the Baghdad airport meteorological station. Second, the process includes data collection and preprocessing, followed by climate analysis and calculation of key atmospheric instability indices and parameters. These help assess the probability of convective events. Third, atmospheric maps are analyzed to identify the systems influencing the weather, and then the events are classified. Finally, linear and nonlinear correlation testing is used to determine the relationships between the various parameters. 3. Results and discussion 3.1. Analysis of the nature and distribution of thunderstorms with the main observation time Figure 3 shows the time distribution of the annual frequency of thunderstorms at the Baghdad airport station for the period from 1990 to 2019. The figure shows that the number of thunderstorms has fluctuated significantly from year to year, without a clear long-term trend of increasing or decreasing. This fluctuation reflects the changing nature of the weather factors influencing thunderstorm formation, which are directly related to the region's dynamic and thermal conditions. The highest frequency of thunderstorms was recorded in 1994, with around 26 storms, indicating exceptional thunderstorm activity that may be related to unusual weather conditions such as increased atmospheric instability or an increase in (CAPE) during that season. In contrast, the lowest frequency was observed in 2005, with the number of storms not exceeding approximately four storms, which may reflect relative atmospheric stability and a decrease in atmospheric humidity or a weakness in the development of cumulonimbus clouds. It is also noted that the periods of the nineties (1991–1997) were characterized by a relative increase in the number of thunderstorms, which may coincide with periods active in terms of regional climate oscillations such as the North Atlantic Oscillation (NAO) or the El Niño phenomenon (ENSO) (NOAA, 2016 ). The phases that are positive and negative large anomalies in precipitation, surface air temperatures, and mid-latitude westerly and subtropical trade winds can be caused by NAO. The extratropical low-pressure systems and storm paths across the North Atlantic tend to travel north during the positive phase, which also enhances the westerly flow over the region. While southern Europe, the Mediterranean Basin, and western Asia experience cold and dry weather, northern Europe experiences more precipitation and warmer temperatures. The extratropical low-pressure systems typically shift south during the negative phase. In southern Europe, the Mediterranean Basin, and western Asia, the result is increased precipitation and warmer temperatures (Cullen et al., 2002 ; Vorhees, 2006 ; Hanson, 2007 ; Khidher & Pilesjö, 2014 ). Which affect rainfall patterns and atmospheric instability in the Middle East regions. This contrasts with the 2000s, during which no significant increase in thunderstorms was observed. The climate record for thunderstorms at Baghdad airport station does not show a clear linear trend towards increasing or decreasing over the past three decades (although the 1990s saw a greater increase in thunderstorms than the 2000s). This suggests that the change in storm frequency is driven more by short- to medium-term climate fluctuations. However, continued monitoring and analysis of thunderstorm data over longer time periods, and linking them to factors such as surface humidity, lower atmospheric temperatures, and atmospheric pressure distribution, may help to clarify future trends in thunderstorm activity in the region. Figure 4 shows the monthly distribution of thunderstorm frequency recorded at Baghdad airport station during the period from 1990 to 2019. Analysis shows that thunderstorm activity in the region is characterized by a distinct seasonality, with most storms concentrated during the spring and autumn seasons, peaking in March to May. The highest frequency of thunderstorms was recorded in April, with the number of cases 100 storms during the study period, followed by March with more than 60 storms, reflecting an increase in atmospheric instability rates during the spring months as a result of the intersection of cold air masses coming from the upper latitudes with a warm, moist air masses from the lower latitudes. These conditions lead to enhanced convection and the formation of cumulonimbus clouds, which are responsible for thunderstorms. On the other hand, no thunderstorms were recorded or observed during the summer season. As for the autumn season, September is the least active month, with the number of recorded storms not exceeding approximately 4. This is attributed to the dominance of subtropical high-pressure systems during the summer months, which stabilize the atmosphere and prevent the development of cumulonimbus clouds. During the months of October and November, moderate thunderstorm activity is recorded as a result of the beginning of the decline of the thermal influence and the return of low-pressure systems passing through the region. During winter, thunderstorms occur at below-moderate rates, ranging from 19 to 27 events, which reflects limited instability associated with cold fronts from mid-latitude low-pressure systems. This pattern suggests that seasonal dynamics and thermal factors are primary determinants of thunderstorm activity in Baghdad. Spring emerges as the most favorable period for storm occurrence due to elevated surface temperatures, increased relative humidity, and enhanced convective processes. The monthly distribution of thunderstorms aligns with the arid climate of the Baghdad region, where transitional seasons experience more frequent weather disturbances than the more stable summer and winter periods. Figure 5 shows the relative distribution of synoptic system types associated with thunderstorm occurrence at Baghdad airport station during the period from 1990 to 2019 and for six months of the year (March, April, May, September, October, and November). The results show that shallow mid-latitude depressions accounted for the largest proportion of impactful cases at 55.48% of the total depressions, while deep mid-latitude depressions accounted for 44.52%. This distribution indicates that most thunderstorms in the Baghdad region are generated under the influence of relatively shallow atmospheric systems, reflecting the transitional climatic nature of the region. The continued dominance of shallow depressions by a small percentage over the past three decades may reflect a gradual shift, like climatic influences towards more pronounced local warm patterns, which is consistent with regional climate change indicators that point to rising temperatures and declining atmospheric humidity in Iraq. This shift highlights the need for a long-term dynamic analysis of the weather systems affecting the region, as it is important for improving thunderstorm forecasting systems and reducing their impact on agricultural and urban activities in Baghdad Governorate and neighboring areas. Figure 6 shows a histogram for the thunderstorm (K-Index) for the period between 1990 and 2019 (468 events). The histogram displays a variation in index value frequency, reflecting changes in atmospheric instability characteristics over the past three decades. Results indicate that the category between 26–30 represents the proportion of observations at 35.26%. The categories 20–25 and 31–35 follows at 25.43% and 23.72%, respectively. These values express medium to high levels of thermal and moisture instability. They are often associated with the formation of cumulus clouds and local thunderstorms. The highest frequency ratios range between 26 and 35, indicating that the atmospheric environment during the study period was most favorable for moderate to intense convection. This matches the study area's climate, which is often affected by heat waves and periods of high relative humidity in the lower layers. These conditions create vertical instability. In contrast, categories with low values (K < 20), amounting to 12.61%, represent cases of atmospheric stability. Such cases are associated with dominant dry air masses or upper-level cooling, which limit convection and reduce the formation of cumulus clouds. Very high values (K ≥ 40) did not exceed 3% and reflect rare cases of extreme instability. This is often linked with the passage of deep low-pressure systems or unusually humid tropical activity. The results indicate that the K index at the study station has high values. This means the probability of thunderstorms is relatively frequent but mostly not severe. This pattern serves as an important indicator in short-term weather forecasting. The distribution of this indicator can help estimate the probability and assess the severity of regional weather phenomena. Additionally, changes in index value distribution over time can enhance understanding of the relationship between thermal and humidity activity and regional climate change in recent decades. The K index is based on the moisture content in the lower layers of the atmosphere, making it more suitable for diagnosing short-term local thunderstorms. Figure 7 shows the frequency distribution of the thunderstorm Total Totals index for the period (1990–2019), indicating that the category between (51–52) is the most frequent at (36.75%), followed by categories (45–50) at (34.83%) and (53–56) at (17.09%). These values indicate the prevalence of moderate atmospheric instability in the region, as TT values within this range are usually associated with a sufficient thermal and humid environment for the development of cumulonimbus clouds and the occurrence of local, limited-intensity thunderstorms. As for the low category (TT < 45), the amounted to (13.93%), while the very high values (TT ≥ 57), which amounted to (1.28%), reflect rare cases of extreme instability, often associated with strong thermal lifting processes or the passage of severe atmospheric depressions. Overall, this distribution shows that most of the study periods were characterized by moderately unstable conditions, making the TT index an effective tool for diagnosing thunderstorm activity and estimating the likelihood of atmospheric disturbances in the region. The results of the frequency distribution of both the K and TT indices for the period (1990–2019) showed that both clearly reflect the thermal and humid nature of the atmospheric layers in the study area, but there is a difference in the intensity of the response and the accuracy of representing instability conditions. K values were concentrated between 20 and 35 (84.40%), while TT values were concentrated between 45 and 52 (71.58%), indicating that both indices accurately describe the dominance of moderate-high instability in the local climate. However, the K index is more closely related to the moisture content in the lower layers of the atmosphere and, therefore, more sensitive to changes in relative humidity and more effective in predicting short-lived local storms. The TT index, however, relies more heavily on the vertical temperature gradient between the 850 and 500 hPa layers, making it better able to represent large-scale disturbances and deep thunderstorms. Therefore, it is recommended to use both indices together for a more accurate assessment of atmospheric instability levels and to predict thunderstorm activity in the region. The results of the analysis of the K-Index and Total Totals Index indicate that the atmospheric climate over Baghdad during the period (1990–2019) was characterized by a high frequency of moderate-high instability, with a scarcity of severe cases, which reflects a transitional nature of the atmosphere between near-permanent stability during periods of drought and limited instability during the transitional and spring seasons. This distribution reinforces the hypothesis that regional climate change has led to a redistribution of thermal energy and moisture characteristics in the lower layers of the atmosphere so that periods of atmospheric disturbance have become more frequent but less intense. Figure 8 illustrates the relative frequency distribution of the available convective thermal energy coefficient (CAPE) across different categories for the period from 1990 to 2019, providing a fundamental basis for understanding atmospheric stability and the potential for thunderstorm development in the study area. The data indicate a prevalence of relatively stable or weak convective weather, with only a limited increase in the potential for severe storms. The data show that the vast majority of CAPE parameter frequencies fall within the following categories: CAPE below 300: This is the most common category, representing 56.84% of all observations. This range of very low values ​​indicates stable or very stable atmospheric conditions, where the upward buoyancy is insufficient to support the development of strong thunderstorms. This translates into a significant absence or weakness in convection, which supports the presence of a strong Convective Inhibition (CIN) layer or insufficient heat in the surface layers. CAPE category 300 to 999: This category represents the third most common category, at 23.08%. These values ​​are usually classified as having below-medium convection energy and indicate the formation of cumulonimbus clouds or marginally unstable, but not intense enough to cause violent weather phenomena. The sum of these two categories is approximately 79.91%, which confirms that the atmospheric cover of the region was, mostly (during the study period), either stable or with a low susceptibility to convection. CAPE 1000 to 2499 category: This category represents a frequency of 13.46%. These values are considered medium, which indicates the possibility of strong thunderstorms accompanied by heavy rainfall and possibly small hail. This category represents most of the weather events that could be considered "storms" in the region. The frequency decreases sharply as the CAPE value increases, reflecting the rarity of extreme instability conditions. CAPE category 2500 to 3500: Represents a small percentage of 4.49%, which is very low. This range shows highly unstable atmospheric conditions, which increase the chance of severe thunderstorms. Higher values indicate stronger convective energy and a greater risk of severe weather, including supercells, heavy rainfall and hail high wind speed. CAPE category > 3500: This category is the least common, representing only 2.14%. Such elevated values suggest highly violent and rare weather phenomena. These include large hail or tornadoes, and confirm these events are exceptional within the region. These results can be used as a "ground truth" to evaluate the performance of regional climate models in simulating atmospheric stability conditions. The distribution provides the statistical basis for risk assessment; most days of the year require low warnings (within the range of CAPE 2500) are rare and warrant a special early warning system. This distribution can also be compared with the expected changes under global warming scenarios to estimate how much the frequency of strong storms will increase or decrease. The distribution suggests the need for future research to focus on identifying the dynamic and thermodynamic factors (such as wind shear and moisture distribution) that contribute to rare cases of extreme instability, rather than focusing solely on CAPE parameter averages. Based on the results, the TT index is considered one of the best indicators for predicting thunderstorms. Over 30 years, as TT values ranging between 51 and 52 indicate a high probability of thunderstorms occurring. The data shows that 36.75% of storms in Baghdad fall within this range, making it a reliable indicator. In contrast, the CAPE is less accurate, with 56.84% of thunderstorms falling within a range below 300 (atmospheric stability). Through skew-T analysis, it was found that the majority of convection energy falls within this level (from 900 or 850 to 700 or 650 hPa). However, forecasts often suggest that indicators based on available convective potential energy, such as CAPE, may be more effective in predicting thunderstorms. 3.2. Analysis and evaluation of the correlation coefficient between Temperature, LCL, EL, Thickness, and atmospheric stability parameter (CAPE) The Pearson correlation coefficient between the temperature, LCL, EL, thickness, and CAPE of thunderstorm days for 1990–2019 has been calculated for the study period. The results show a relatively strong positive relationship between surface temperature (T) and the lifting condensation level (LCL), with a correlation coefficient of r = 0.8 and R² = 0.6 in Fig. 9 , indicating that a rise in surface temperature leads to a rise in the level of condensation in the atmosphere. The empirical equation for linear regression (LCL = -1083 + 133.5*T) expresses this relationship, indicating that every 1°C increase in surface temperature corresponds to an increase of approximately 133.5 meters in the height of the condensation level. This behavior is attributed to the fact that a higher surface temperature increases the air's ability to hold water vapor, leading to a decrease in relative humidity and a move away from a state of saturation. As a result, the air needs to rise to higher levels in order to cool and reach the dew point, that is, the point at which actual condensation begins. This explains the marked increase in LCL values with rising temperature. As can be seen from the figure, the dispersion of data points around the regression line indicates that the relationship is also influenced by other factors such as the actual amount of water vapor in the surface layer, daily variations in terrestrial heating and radiation, as well as local surface characteristics (soil moisture, vegetation cover). However, the high correlation strength (r = 0.8) confirms that surface temperature remains a key and direct factor in determining the height of the condensation level. In general, this relationship reflects that hot, dry conditions lead to a higher condensation level (LCL), which reduces the likelihood of low cloud formation, while cold, humid conditions lower the LCL and increase the chances of vapor condensation and cumulus cloud formation. Figure 10 illustrates the relationship between temperature and equilibrium level (values of cape equal to zero have been removed; there are no data for the equilibrium level). The data were analyzed using a linear regression model. The data show a clear positive correlation between them (r = 0.5). That is, the rise in temperature is associated with an increase in the height of the equilibrium level in the atmosphere, which is consistent with the thermodynamic principles that link the heating of the lower layers (high temperatures) with the rise in equilibrium levels. The empirical equation for linear regression (LCL = 4385.1 + 205.9*T) expresses this relationship, indicating that every 1°C increase in surface temperature corresponds to an increase of approximately 205.9 meters in the height of the condensation level. Despite the clear positive trend, the graph points show a large dispersion around the red regression line. This dispersion indicates that the strength of the correlation is moderate and that temperature, although an important factor, does not alone explain the entire variance in the equilibrium level, indicating the intervention of other atmospheric factors such as (humidity content, wind shear, and vertical changes in the atmosphere) in predictive models to increase the accuracy of explaining the variance in the equilibrium level. This is because surface heating raises the temperature of air parcels near the surface, which leads to a decrease in their density and gives them the necessary acceleration to begin the process of floating within the atmosphere, and thus they rise to higher layers, where they can release their latent energy in the form of effective convection. This indicates that surface temperature is the primary factor in the initiation and maintenance of thermal instability. From Fig. 11 , (values of cape equal to zero have been removed; there are no data for the equilibrium level) the data indicate a strong positive correlation (R² = 0.7) between the potential available convective energy (CAPE) and the equilibrium level (EL). This relationship was plotted using an exponential growth model (ExpGro2). As the CAPE value increases, the equilibrium level (EL) also increases. The increase in the equilibrium level is sharp and rapid at low and medium CAPE values (less than about 2000 J/kg), then the curve begins to saturate or flatten at high CAPE values. This indicates that the effect of increasing CAPE on raising the equilibrium level becomes less pronounced after a certain limit, and the equilibrium level approaches an approximate maximum value. The value of the adjusted coefficient of determination (Adj. R-Square) of 0.7 provides strong predictive power. Although there are scattered points with high dispersion. This analysis confirms the important role of CAPE as a catalyst for convection and its direct relationship to the maximum height of deep convective clouds. From Fig. 12 , (values of cape equal to zero have been removed; there are no data for the equilibrium level) the data clearly indicate a strong positive and non-linear correlation (R² = 0.9) between the potential available convection energy (CAPE) and thickness. Using an exponential model (ExpGro2), the layer thickness increases as the CAPE value increases. This increase is more pronounced and rapid at lower CAPE values (below approximately 1500 J/kg). As CAPE continues to increase, the curve begins to flatten (saturate), indicating that the effect of subsequent CAPE increases on layer thickness becomes less significant at higher values. The value of the adjusted coefficient of determination (Adj. R-Square) of 0.7 provides strong predictive power. Despite the strong correlation, a noticeable dispersion is observed in the graphs, particularly at high CAPE values. This dispersion suggests that CAPE is not the sole factor influencing layer thickness and that other atmospheric factors play a significant role in the remaining variation. This indicates that surface temperature is the primary factor in initiating and sustaining thermal and atmospheric instability. Tables 5 and 6 shows statistical relationships using linear and non-linear/exponential growth (ExpGro2) models. Temperature (T) had medium to strong, significant linear correlations with EL and thermal lift (LCL) (R = 0.5–0.8, P-values = > 0.0002–>0.0003). Available convection energy (CAPE) showed a strong exponential relationship with equilibrium level (R² = 0.7, P = > 0.0005) and thickness (R² = 0.9, P = > 0.0004). The P-values ​​are considered reliable because they are less than 0.05, meaning the relationship between all variables was strong and significant (statistically significant). Table 5 Results of the linear regression analysis of the relationship between the selected variables. Variables (Relationship) Model a b R R 2 P-value T and LCL Linear -1083 133.5 0.8 0.6 0.0002> T and EL Linear 4385.1 205.9 0.5 0.3 0.0003> Table 6 Results of the non-linear regression analysis of the relationship between the selected variables. Variables (Relationship) Model A 1 t 1 A 2 t 2 Y 0 R R 2 P-value CAPE and EL ExpGro2 -3973.2 -185.6 -5145 -3197.2 13805.3 0.7 0.7 0.0005> CAPE and Thickness ExpGro2 -3897.1 -123.2 -4623 -1030.9 9196.1 0.7 0.9 0.0004> Table 7 The relationship or correlation coefficient between temperature, LCL, LFC, EL, thickness, and the atmospheric stability parameter (CAPE). R T CAPE LCL LFC EL Thickness T 1 0.3 0.8 0.5 0.5 0.2 CAPE 0.3 1 0.3 -0.1 0.7 0.7 LCL 0.8 0.3 1 0.6 0.4 0.1 LFC 0.5 -0.1 0.6 1 0.2 -0.3 EL 0.5 0.7 0.4 0.2 1 0.9 Thickness 0.2 0.7 0.1 -0.3 0.9 1 To understand the correlation coefficient (R) in the table (7), it is a statistical measure (ranging from − 1 to + 1) that indicates the strength and direction of the linear relationship between two variables. To understand the results more deeply, classify the correlations as positive and negative. Very strong correlation between EL and Thickness (R = 0.9): This is the highest positive correlation. It indicates that any increase in EL (the altitude at which rising convection currents cease to ascend) is closely related to an increase in Thickness (the thickness of a given atmospheric layer, which is closely related to the average temperature of that layer). Greater thickness (warmer atmosphere) typically allows convection cells to develop and reach higher altitudes (higher EL). Strong Correlation Between (T) and (LCL) (R = 0.8): A strong positive correlation. This indicates that a rise in T is strongly correlated with a rise in LCL (the level at which moisture condenses to form clouds). Warmer air can hold more moisture before reaching the condensation point, thus raising the LCL level. As a result, the air needs to rise to higher levels in order to cool and reach the dew point, that is, the point at which actual condensation begins. In general, this relationship reflects that hot, dry conditions lead to a higher condensation level, while cold, humid conditions lower the LCL and increase the chances of vapor condensation and cumulus cloud formation. Strong correlations between CAPE, EL, and Thickness (R = 0.7 for both): This indicates that an increase in CAPE (available convective potential energy, a measure of atmospheric instability) is strongly associated with warmer atmospheres (thickness) and longer vertical convective cells (EL). High energy (CAPE) is the driver of intense weather phenomena (such as thunderstorms), which often occur in thick atmospheric environments. Moderate Correlations between T, LFC, and EL, and between LCL and LFC (R = 0.5–0.6): These values ​​indicate a significant and statistically significant relationship, suggesting that the two variables tend to change together. However, other factors (third variables) also influence the final outcome. These correlations demonstrate physical connections that exist but are not as deterministic or direct as very strong correlations. Other factors (such as humidity, wind, and vertical changes in the atmosphere) also play an important role in determining the CAPE value. They were the most frequent mechanisms inhibiting the vertical air movement. Negative correlation between LFC, Thickness, and CAPE (R= (-0.1) – (-0.3)): This is a weak to moderate negative correlation. It indicates that as Thickness (atmospheric warmth) increases, LFC (the free convection level) tends to decrease slightly. This may indicate a complex dynamic, where some warm instability allows convection to begin at lower altitudes. That is, the temperature reaches the condensation point, but weak vertical motion prevents the potential energy from rising upwards. Conclusion 1- Clear time-based distribution of thunderstorms: The results showed that the highest frequency of thunderstorms in the Baghdad airport station occurs during March to May, accounting for more than 58.56% of all recorded events. This act is due to the surface temperature increasing, which leads to more convection when unstable conditions are formed. 2- Annual peak in 1994: The highest annual frequency of thunderstorms was recorded in 1994, with 26 days of storms. This indicates unusual atmospheric activity associated with increased thermal and humidity instability that year. 3- Influencing synoptic systems: Most thunderstorms are found to be caused by shallow mid-latitude lows (55.48%), compared to 44.52% for deep lows, reflecting the transitional nature of the region's climate between arid and semi-humid. 4- Atmospheric stability indicators and their importance in local forecasts: The K-index ranged between 26 and 30 in most cases (approximately 35.26%), indicating high atmospheric instability. The TT-index ranged between 51 and 52 (36.75%), which is the best indicator for predicting thunderstorms. CAPE values were low for most periods (<300 J/kg), meaning that strong storms are rare in the region. The work demonstrated that combining the K-index and TT-index provides a more accurate diagnosis of atmospheric instability, which is very useful in the development of early warning systems for thunderstorms in semi-arid environments, such as Iraq. 5- Statistical correlations: Correlation analysis revealed moderate to strong positive relationships between temperature, lifting condensation level (LCL) and equilibrium level (EL), confirming that surface temperature is a key factor in promoting thermal turbulence. 6- Absence of a long-term trend: No clear trend toward an increase or decrease in the number of storms was observed over 30 years, suggesting that short- and medium-term oscillations (such as ENSO and NAO) are the primary drivers of storm activity. Declarations Conflict of Interest Statement The authors declare that they have no conflicts of interest to report. Funding Statement This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contributions “All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Imad Abdulridha Jasim Al- Khulaifawi] and [Aqeel Ghazi Mutar]. The first draft of the manuscript was written by and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.” Data Availability “The datasets generated during and analysed during the current study are available in the [ECMWF/ERA-5] repository, [https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=download] and [https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=download]” References Allen JT, Karoly DJ (2013) A climatology of Australian severe thunderstorm environments 1979–2011: Inter-annual variability and ENSO influence. 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Supplementary Files Tables.docx Cite Share Download PDF Status: Published Journal Publication published 10 Apr, 2026 Read the published version in Theoretical and Applied Climatology → Version 1 posted Editorial decision: Revision requested 22 Feb, 2026 Reviews received at journal 22 Feb, 2026 Reviews received at journal 09 Feb, 2026 Reviewers agreed at journal 30 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers invited by journal 29 Jan, 2026 Editor assigned by journal 26 Jan, 2026 Submission checks completed at journal 26 Jan, 2026 First submitted to journal 24 Jan, 2026 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. 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05:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8683928/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8683928/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00704-026-06187-x","type":"published","date":"2026-04-10T15:58:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":101648808,"identity":"9d1eb16c-1463-4ec7-a3a4-5637b1019cae","added_by":"auto","created_at":"2026-02-02 08:59:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":183354,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Iraq with the selected station.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8683928/v1/697ea6e740e7acb46c4cf4a1.png"},{"id":101648716,"identity":"88f971cd-c5f6-4179-ac45-15cfeed462dc","added_by":"auto","created_at":"2026-02-02 08:59:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":118843,"visible":true,"origin":"","legend":"\u003cp\u003eWork Methodology Flowchart.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8683928/v1/0f912aa62f0a0548004350a7.png"},{"id":101753317,"identity":"ed83bfae-cec2-4317-8340-02d534e27155","added_by":"auto","created_at":"2026-02-03 10:39:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":132923,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual distribution of thunderstorms at Baghdad airport station for the period (1990-2019).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8683928/v1/38441d1fe91722f411002228.png"},{"id":101648745,"identity":"22fb913c-643f-4c33-afb7-f9b854b3de57","added_by":"auto","created_at":"2026-02-02 08:59:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":69911,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly distribution of thunderstorms at Baghdad airport station for the period 1990-2019.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8683928/v1/a345decba898e2a831c7a9b0.png"},{"id":101648744,"identity":"904283f2-504c-42ef-83ee-d41c9add9266","added_by":"auto","created_at":"2026-02-02 08:59:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":52422,"visible":true,"origin":"","legend":"\u003cp\u003eThe relative distribution of Shallow and Deep mid-latitude depressions at Baghdad airport station for the period 1990-2019.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8683928/v1/94a77e930f5a24581b28f0d0.png"},{"id":101648742,"identity":"77ac5db0-4703-45ee-af0b-ab5cec6ebb1f","added_by":"auto","created_at":"2026-02-02 08:59:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":54534,"visible":true,"origin":"","legend":"\u003cp\u003eK-index frequency of thunderstorms at Baghdad airport station for the period (1990-2019).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8683928/v1/f140d3bf7b0a47d3eeb52b2e.png"},{"id":101648810,"identity":"d657d218-4718-4ff5-8edf-1eecd9a133aa","added_by":"auto","created_at":"2026-02-02 08:59:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":56586,"visible":true,"origin":"","legend":"\u003cp\u003eTotal Totals index frequency of thunderstorms at Baghdad airport station for the period (1990-2019).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8683928/v1/9726ef815572d414af5af336.png"},{"id":101648743,"identity":"1132463f-7c94-42f2-ac4a-aa60f4bd0493","added_by":"auto","created_at":"2026-02-02 08:59:44","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":49403,"visible":true,"origin":"","legend":"\u003cp\u003eCAPE parameter frequency of thunderstorms at Baghdad airport station for the period (1990-2019).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8683928/v1/9833c56d6eaafcc9ef895704.png"},{"id":101648746,"identity":"a65f51cd-0d84-42cf-880d-e583ffee243f","added_by":"auto","created_at":"2026-02-02 08:59:44","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":81555,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation coefficient between Temperature and LCL.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8683928/v1/36af60aa49463f62f1742568.png"},{"id":101648757,"identity":"724de92c-c31c-4807-8bf1-e699e7472a49","added_by":"auto","created_at":"2026-02-02 08:59:45","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":74866,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation coefficient between Temperature and EL.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-8683928/v1/52e36e5248011a37d78e0aca.png"},{"id":101648797,"identity":"b4dc7ae1-99d8-4951-a683-a6c5c2328b2d","added_by":"auto","created_at":"2026-02-02 08:59:54","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":76283,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation coefficient between CAPE and EL.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-8683928/v1/6a741c1872a2eb52391738a0.png"},{"id":101648798,"identity":"98946fa3-a4a1-4fca-a513-425e64827c2d","added_by":"auto","created_at":"2026-02-02 08:59:54","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":76990,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation coefficient between CAPE and Thickness.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-8683928/v1/cbec81072210f96dd332e76e.png"},{"id":106809170,"identity":"3d954b50-667c-4e4a-85ec-270fc1d73ca6","added_by":"auto","created_at":"2026-04-13 16:07:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1874652,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8683928/v1/22e2bc23-0536-43c8-83fc-4c7ae6df55d8.pdf"},{"id":101648718,"identity":"370a6423-4758-4eb4-9ac0-6a56573fd12b","added_by":"auto","created_at":"2026-02-02 08:59:43","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":96022,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8683928/v1/f6c665850c6b87344adfd172.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Thundercloud assessment for the years 1990–2019 over the Baghdad airport station","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOne of the most dangerous mesoscale convective systems in the atmosphere, thunderstorms are caused by a number of factors, including moisture in the lower atmosphere and the instability and lifting of air parcels. When thick layers of warm, humid air ascend to colder parts of the atmosphere, thunderstorms are created. Updraft moisture condenses there to create cumulonimbus clouds, which in turn produce precipitation, snow, and hail storms, which result in fatalities and crop damage. A thunderstorm is defined by the World Meteorological Organization as one or more sudden electrical discharges accompanied by an intense or thundering sound and a burst of light (lightning) (Encyclopedia Britannica, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mondal et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Singh \u0026amp; Bhardwaj, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Although thunderstorms can form and grow anywhere in the world, they are most common in the mid-latitudes, where cooler air from the polar latitudes collides with warm, humid air from the tropical latitudes during the period of instability that occurs in the spring and autumn (National Severe Storms Laboratory, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2020\u003c/span\u003e). Atmospheric instability, adequate moisture in the lower troposphere, and lifting mechanisms (such as convection, frontal lift, and convergence) are the three fundamental conditions for thunderstorm development (Arora et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bennett et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Kunz et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Trapp, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). They also found that Thunderstorms (TSs) were most commonly observed between March to May in Iraq. One of the most catastrophic meteorological phenomena, thunderstorms have a duration of less than an hour and a spatial extent of a few kilometers to several hundred kilometers (Saha et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Umakanth et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Deep convective systems and occasionally cumulonimbus clouds, which produce intense lightning, strong winds, and heavy rainfall, can form from thunderstorms. Despite their brief duration, these thunderstorms have the potential to cause significant harm to people and property. Most of the world's thunderstorms happen over tropical areas. The hot and humid conditions characteristic of tropical regions plays a significant role in the development of thunderstorms (Bhardwaj et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Thermodynamic and kinematic conditions are also important. Studies on the effects of Convective Inhibition (CIN) and the Lifting Condensation Level (LCL) on the occurrence of strong convective events are among the body of studies in this field (Chaboureau et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Davies, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Indices like the K-index, Lifted Index, and Convective Available Potential Energy (CAPE) are crucial for meteorologists to assess regional convective potential. They help pinpoint areas with an unstable atmosphere, which is a key condition for thunderstorm development (Humood \u0026amp; Abbas, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). When predicting strong convective phenomena, atmospheric instability indices are widely used. However, a variety of factors that prevent the development of the vertical movement of air can have a significant impact on their verifiability. Either upper-air sounding data was used in published studies on the temporal and spatial variability of instability indices (Derubertis, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Madineni et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere are numerous studies in the literature that have conducted TS analyses of the world (for different regions). Tyagi, (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). A climatological study of thunderstorms was conducted, encompassing 450 observation stations in India and neighboring countries. Of these, 390 were surface observatories operated by the Indian Meteorological Department, 50 were managed by the Indian Air Force, and the remainder were run by neighboring countries. Mohee \u0026amp; Miller, (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Thunderstorms in North Dakota were analyzed using radar and ground observations data collected from 2002 to 2006. Allen \u0026amp; Karoly, (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Conducted a climatological study of severe thunderstorms in Australia, which were analyzed using the ERA-Interim reanalysis, observed between 1979 and 2011, and investigated their relationship with El-Nino during the warm season (April to September). Jussi et al. (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2021\u003c/span\u003e). Researchers studied the risks associated with thunderstorms and analyzed the usefulness of different data sources in machine learning models. The study showed that radar data is the most important variable for forecasting in general. Satellite imagery was also found to be useful for all forecast variables and provides a viable alternative in areas where radar data is unavailable. While lightning data were particularly useful for lightning forecasting, their usefulness was limited for other hazards. The study found that information contained in observational data during the forecast period could reasonably compensate for the omission of NWP data. Finally, the study found no evidence that digital elevation model (DEM) data provide any forecasting benefit. Al-Khulaifawi \u0026amp; Ioshpa. (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2025\u003c/span\u003e). A study on the development and frequency of thunderstorms is of particular importance. The analysis of thunderstorm activity relies on visual data obtained from long-term observations. This study is based on daily archive data collected from two meteorological stations in Russia and three stations in Iraq over twenty years (2000\u0026ndash;2019). The analysis revealed that thunderstorms in Iraq are most prevalent in March and April, particularly at the Khanaqin station, where 42% of thunderstorms occur in the mountainous northern region of Iraq. In the Rostov region of Russia, the highest frequency of thunderstorms occurs in June and July, accounting for 50.2%. The total number of days with thunderstorms at research stations in Iraq was 589, while at research stations in Russia it was 729. Despite the general decline in thunderstorm activity in the Rostov region, it is still classified as an area with high thunderstorm activity. In contrast, Iraq, particularly in the mountainous Khanaqin region, exhibits an increasing trend in thunderstorm activity over the past 20 years.\u003c/p\u003e \u003cp\u003eResearchers frequently utilize atmospheric stability indices to analyze thunderstorms. Kunz, (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Utilized 20 distinct stability indices, such as the convective available potential energy (CAPE), K index, and total totals index, to assess Germany's thunderstorm features using radiosonde and SYNOP observations. Kolay et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This study analyzes thunderstorm activity at Istanbul International Airport using atmospheric stability indices data for the ground level, and upper levels were obtained from the Turkish State Meteorological Service for 29 October 2018 and 1 January 2023. With a total of 51 occurrences, thunderstorms were seen to occur more frequently in the summer. With 32 thunderstorm events, June had the most of any month. This results in eight incidents annually on average. Transitions between troughs and cold fronts accounted for 72.22% of the total. Compared to other stability indices, the K index and Total Totals index provided a better representation of the thunderstorm events. These two stability indicators accounted for 75% of the total thunderstorm days. During thunderstorm events, the K and Total Totals indices are better represented, and the convective available potential energy (CAPE) values, which are low. (Yavuz, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This study analyzed thunderstorms in Istanbul from 2001 to 2022. Using thermodynamic indices and atmospheric stability criteria, 703 thunderstorms were identified during this period. The study revealed that most of the storms (69%) occurred during the warm season (May\u0026ndash;September), and that the majority (93%) lasted for a few hours (0\u0026ndash;3 hours). The thermodynamic indices and atmospheric stability criteria used in the research included the (SI), (LI), (SWEAT), (KI), (TTI), (CAPE), (CIN), and (BRN). Analyses revealed significant differences between the index values ​​on thunderstorm and non-thunderstorm days. The highest predictive power was observed in the summer, while the lowest was recorded in the winter.\u003c/p\u003e \u003cp\u003eThis work aims to analyze and evaluate the characteristics of thunderstorms over Baghdad airport stations during the period 1990\u0026ndash;2019 and their frequency, temporal distribution, and associated weather factors. This analysis attempts to estimate the trends of these thermodynamic indices and parameters throughout the study period, in light of the continuous climate change in Baghdad.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study stations\u003c/h2\u003e \u003cp\u003eIraq is situated between 29˚5\u0026acute; and 37˚22' N latitude and 38˚45\u0026acute; and 48˚45' E longitude. Iraq is bordered to the north by Turkey, to the east by Iran, to the south and east by Saudi Arabia, Kuwait, and the Persian Gulf, and to the west by Syria and Jordan. The country's capital, Baghdad, is situated in the center of the country. The Tigris and Euphrates are the two major rivers that flow through the country from north to south. Its geographical diversity includes mountainous regions in the north and northeast, plateau regions at lower altitudes to the south of the mountains, alluvial plains, and western plateaus. The elevation and location of these regions significantly influence the climatic conditions across Iraq (Al-Ansari, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sultan, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Zakaria et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the topography of Iraq and the locations of the selected Baghdad airport station.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.2. data sources\u003c/b\u003e\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Meteorological Stations Observations\u003c/h2\u003e \u003cp\u003eHourly meteorological data (archived) on thunderstorms, temperature, relative humidity, and pressure from 1990 to 2019, specifically for key times (00:00, 06:00, 12:00, and 18:00) for the Baghdad airport meteorological station, were obtained in real-time from the Iraqi Meteorological Organization. Before analysis, the quality and continuity of the data series were checked. Baghdad airport station is located at 33.18\u0026deg; N, 44.24\u0026deg; E, and 31.7 m above sea level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Reanalysis Data (ERA5)\u003c/h2\u003e \u003cp\u003eThe fifth iteration of the ECMWF Integrated Forecast System is called ERA5. Three completely connected components\u0026mdash;atmosphere, land surface, and ocean waves\u0026mdash;are incorporated into the forecast model. On the surface (single or upper air level), it offers data on a variety of factors (Hersbach et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The utilized data have a temporal resolution of 00, 06, 12, and 18 UTC and a spatial resolution of 0.25\u0026deg; \u0026times; 0.25\u0026deg; for the atmosphere throughout the 1990\u0026ndash;2019 period. The pressure levels are 850, 700, and 500 hPa. Data was obtained temporally and spatially for the station in NetCD format and processed using MATLAB 2020. The result was plotted using Origin Pro-21. The model synopsis is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Diagnosis of synoptic conditions\u003c/h2\u003e \u003cp\u003eDiagnosing the atmospheric conditions associated with a thunderstorm event can provide a clear picture of the dynamic causes and the general atmospheric flow pattern underlying the signal or trigger that initiated the baroclinic instability, which ultimately caused the event. Analyzing upper-level synoptic weather maps, spatial wind speed, and streamlines at the 300 hPa pressure level can reveal the nature of the polar jet stream's movement and its northward or southward meander patterns. This allows for the identification of the weather event's initiation and the intensity of the baroclinic instability, depending on the direction and amplitude of the meander pattern in the polar jet stream (Rossby wave). Identifying extratropical troughs and ridges at the 500 hPa pressure level is crucial for understanding how the Rossby wave's meander pattern deepens to form a mid-latitude depression, what the prevailing pattern is, and determining cyclogenesis. Meanwhile, the associated surface frontal system can be identified by analyzing surface wind patterns and surface temperature distributions (Mutar et al., \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3. The Calculation of some indices and parameter\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. K \u0026ndash; index\u003c/h2\u003e \u003cp\u003eThis index considers the contribution of moist air at 700 hPa to the formation of thunderstorms. KI rises when static stability decreases between 850 and 500 hPa, moisture increases at 850 hPa, and relative humidity increases at 700 hPa. The definition of the K index is as follows. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows atmospheric conditions by K-index values (George, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1960\u003c/span\u003e).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:K=T\\:\\left(850mb\\right)+Td\\:\\left(850mb\\right)-T\\:\\left(500mb\\right)-DD\\:\\left(700mb\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Totals Totals index\u003c/h2\u003e \u003cp\u003eTotal Totals Index is similar to K-Index. The temperature difference between (T 850) and (T500) as well as the difference between the dew point at the (Td850) and the temperature at 500 hPa are used to express (formula) the Total Totals Index (Miler, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e1972\u003c/span\u003e; Showalter, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1953\u003c/span\u003e; Peppler \u0026amp; Lamb, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1989\u003c/span\u003e).\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:TT=T850mb+Td850mb-2\\left(T500mb\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eInformation about the state of the atmosphere according to the value Totals Totals Index ranges of this parameter, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Convective Available Potential Energy (CAPE)\u003c/h2\u003e \u003cp\u003eThe quantity of energy accessible during convection, or CAPE, is determined by integrating the parcel's local buoyancy vertically from the level of free convection (LFC) to the equilibrium level (EL). Its foundation is the integration of the atmosphere's vertical profile. Smaller values of this parameter show that the atmosphere is stable. While larger values show that the atmosphere is unstable (Lucas et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Miler, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e1972\u003c/span\u003e; Showalter, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1953\u003c/span\u003e). It is calculated by the following formula:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:CAPE=g*{\\int\\:}_{LFC}^{EL}\\left(Tp-Te\\right)*dz/Te$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this formula:\u003c/p\u003e \u003cp\u003eLFC: is the free convection level, expressed in hPa or mb;\u003c/p\u003e \u003cp\u003eLE: level of equilibrium, expressed in hPa or mb;\u003c/p\u003e \u003cp\u003eTe: is the surrounding temperature in degrees Celsius;\u003c/p\u003e \u003cp\u003eTc: is the temperature of the cloud parcel in degrees Celsius;\u003c/p\u003e \u003cp\u003eg: is the gravitational acceleration, expressed in m/s\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003edz: is the layer's thickness between the LFC and EL levels, expressed in m or km.\u003c/p\u003e \u003cp\u003eInformation about the state of the atmosphere according to the value (CAPE) ranges of this parameter, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eUsing the LCL values, the cloud base height is roughly estimated, which is obtained using surface temperature and dew point temperature information (the air temperature equals to the dew point temperature at this altitude). In order to calculate convective available potential energy (CAPE) and convective inhibition (CIN) and analyze thunderstorm potential and severity across any region, the LCL values are crucial. To determine the direct relationship with the total water vapor content, cloud height variability, seasonal and regional variability, etc., the LCL values can be employed as CBH (Craven et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002b\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). While EL, the height at which the temperature of the flowing air parcel equals the ambient temperature, is known as the equilibrium level, the EL values can be employed as CTH (Cinaroglu \u0026amp; Unutulmaz, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). And calculating the depth of the cloud, or vertical thickness, by the disparity between the cloud's base and summit heights is known as this parameter (Shmeter, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1972\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis research plan describes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) a methodology for analyzing climate data to study and classify thunderstorm events using an integrated approach. It is based on, first, the primary data used, which are on ERA-5 (the fifth generation of atmospheric reanalysis from the European Centre for Medium-Range Weather Forecasts) and data from the Baghdad airport meteorological station. Second, the process includes data collection and preprocessing, followed by climate analysis and calculation of key atmospheric instability indices and parameters. These help assess the probability of convective events. Third, atmospheric maps are analyzed to identify the systems influencing the weather, and then the events are classified. Finally, linear and nonlinear correlation testing is used to determine the relationships between the various parameters.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Analysis of the nature and distribution of thunderstorms with the main observation time\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the time distribution of the annual frequency of thunderstorms at the Baghdad airport station for the period from 1990 to 2019. The figure shows that the number of thunderstorms has fluctuated significantly from year to year, without a clear long-term trend of increasing or decreasing. This fluctuation reflects the changing nature of the weather factors influencing thunderstorm formation, which are directly related to the region's dynamic and thermal conditions. The highest frequency of thunderstorms was recorded in 1994, with around 26 storms, indicating exceptional thunderstorm activity that may be related to unusual weather conditions such as increased atmospheric instability or an increase in (CAPE) during that season. In contrast, the lowest frequency was observed in 2005, with the number of storms not exceeding approximately four storms, which may reflect relative atmospheric stability and a decrease in atmospheric humidity or a weakness in the development of cumulonimbus clouds. It is also noted that the periods of the nineties (1991\u0026ndash;1997) were characterized by a relative increase in the number of thunderstorms, which may coincide with periods active in terms of regional climate oscillations such as the North Atlantic Oscillation (NAO) or the El Ni\u0026ntilde;o phenomenon (ENSO) (NOAA, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2016\u003c/span\u003e). The phases that are positive and negative large anomalies in precipitation, surface air temperatures, and mid-latitude westerly and subtropical trade winds can be caused by NAO. The extratropical low-pressure systems and storm paths across the North Atlantic tend to travel north during the positive phase, which also enhances the westerly flow over the region. While southern Europe, the Mediterranean Basin, and western Asia experience cold and dry weather, northern Europe experiences more precipitation and warmer temperatures. The extratropical low-pressure systems typically shift south during the negative phase. In southern Europe, the Mediterranean Basin, and western Asia, the result is increased precipitation and warmer temperatures (Cullen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Vorhees, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Hanson, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Khidher \u0026amp; Pilesj\u0026ouml;, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhich affect rainfall patterns and atmospheric instability in the Middle East regions. This contrasts with the 2000s, during which no significant increase in thunderstorms was observed. The climate record for thunderstorms at Baghdad airport station does not show a clear linear trend towards increasing or decreasing over the past three decades (although the 1990s saw a greater increase in thunderstorms than the 2000s). This suggests that the change in storm frequency is driven more by short- to medium-term climate fluctuations. However, continued monitoring and analysis of thunderstorm data over longer time periods, and linking them to factors such as surface humidity, lower atmospheric temperatures, and atmospheric pressure distribution, may help to clarify future trends in thunderstorm activity in the region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the monthly distribution of thunderstorm frequency recorded at Baghdad airport station during the period from 1990 to 2019. Analysis shows that thunderstorm activity in the region is characterized by a distinct seasonality, with most storms concentrated during the spring and autumn seasons, peaking in March to May. The highest frequency of thunderstorms was recorded in April, with the number of cases 100 storms during the study period, followed by March with more than 60 storms, reflecting an increase in atmospheric instability rates during the spring months as a result of the intersection of cold air masses coming from the upper latitudes with a warm, moist air masses from the lower latitudes. These conditions lead to enhanced convection and the formation of cumulonimbus clouds, which are responsible for thunderstorms.\u003c/p\u003e \u003cp\u003eOn the other hand, no thunderstorms were recorded or observed during the summer season. As for the autumn season, September is the least active month, with the number of recorded storms not exceeding approximately 4. This is attributed to the dominance of subtropical high-pressure systems during the summer months, which stabilize the atmosphere and prevent the development of cumulonimbus clouds. During the months of October and November, moderate thunderstorm activity is recorded as a result of the beginning of the decline of the thermal influence and the return of low-pressure systems passing through the region.\u003c/p\u003e \u003cp\u003eDuring winter, thunderstorms occur at below-moderate rates, ranging from 19 to 27 events, which reflects limited instability associated with cold fronts from mid-latitude low-pressure systems. This pattern suggests that seasonal dynamics and thermal factors are primary determinants of thunderstorm activity in Baghdad. Spring emerges as the most favorable period for storm occurrence due to elevated surface temperatures, increased relative humidity, and enhanced convective processes. The monthly distribution of thunderstorms aligns with the arid climate of the Baghdad region, where transitional seasons experience more frequent weather disturbances than the more stable summer and winter periods.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the relative distribution of synoptic system types associated with thunderstorm occurrence at Baghdad airport station during the period from 1990 to 2019 and for six months of the year (March, April, May, September, October, and November). The results show that shallow mid-latitude depressions accounted for the largest proportion of impactful cases at 55.48% of the total depressions, while deep mid-latitude depressions accounted for 44.52%. This distribution indicates that most thunderstorms in the Baghdad region are generated under the influence of relatively shallow atmospheric systems, reflecting the transitional climatic nature of the region. The continued dominance of shallow depressions by a small percentage over the past three decades may reflect a gradual shift, like climatic influences towards more pronounced local warm patterns, which is consistent with regional climate change indicators that point to rising temperatures and declining atmospheric humidity in Iraq. This shift highlights the need for a long-term dynamic analysis of the weather systems affecting the region, as it is important for improving thunderstorm forecasting systems and reducing their impact on agricultural and urban activities in Baghdad Governorate and neighboring areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows a histogram for the thunderstorm (K-Index) for the period between 1990 and 2019 (468 events). The histogram displays a variation in index value frequency, reflecting changes in atmospheric instability characteristics over the past three decades. Results indicate that the category between 26\u0026ndash;30 represents the proportion of observations at 35.26%. The categories 20\u0026ndash;25 and 31\u0026ndash;35 follows at 25.43% and 23.72%, respectively. These values express medium to high levels of thermal and moisture instability. They are often associated with the formation of cumulus clouds and local thunderstorms. The highest frequency ratios range between 26 and 35, indicating that the atmospheric environment during the study period was most favorable for moderate to intense convection. This matches the study area's climate, which is often affected by heat waves and periods of high relative humidity in the lower layers. These conditions create vertical instability. In contrast, categories with low values (K\u0026thinsp;\u0026lt;\u0026thinsp;20), amounting to 12.61%, represent cases of atmospheric stability. Such cases are associated with dominant dry air masses or upper-level cooling, which limit convection and reduce the formation of cumulus clouds. Very high values (K\u0026thinsp;\u0026ge;\u0026thinsp;40) did not exceed 3% and reflect rare cases of extreme instability. This is often linked with the passage of deep low-pressure systems or unusually humid tropical activity. The results indicate that the K index at the study station has high values. This means the probability of thunderstorms is relatively frequent but mostly not severe. This pattern serves as an important indicator in short-term weather forecasting. The distribution of this indicator can help estimate the probability and assess the severity of regional weather phenomena. Additionally, changes in index value distribution over time can enhance understanding of the relationship between thermal and humidity activity and regional climate change in recent decades. The K index is based on the moisture content in the lower layers of the atmosphere, making it more suitable for diagnosing short-term local thunderstorms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the frequency distribution of the thunderstorm Total Totals index for the period (1990\u0026ndash;2019), indicating that the category between (51\u0026ndash;52) is the most frequent at (36.75%), followed by categories (45\u0026ndash;50) at (34.83%) and (53\u0026ndash;56) at (17.09%). These values indicate the prevalence of moderate atmospheric instability in the region, as TT values within this range are usually associated with a sufficient thermal and humid environment for the development of cumulonimbus clouds and the occurrence of local, limited-intensity thunderstorms. As for the low category (TT\u0026thinsp;\u0026lt;\u0026thinsp;45), the amounted to (13.93%), while the very high values (TT\u0026thinsp;\u0026ge;\u0026thinsp;57), which amounted to (1.28%), reflect rare cases of extreme instability, often associated with strong thermal lifting processes or the passage of severe atmospheric depressions. Overall, this distribution shows that most of the study periods were characterized by moderately unstable conditions, making the TT index an effective tool for diagnosing thunderstorm activity and estimating the likelihood of atmospheric disturbances in the region.\u003c/p\u003e \u003cp\u003eThe results of the frequency distribution of both the K and TT indices for the period (1990\u0026ndash;2019) showed that both clearly reflect the thermal and humid nature of the atmospheric layers in the study area, but there is a difference in the intensity of the response and the accuracy of representing instability conditions. K values were concentrated between 20 and 35 (84.40%), while TT values were concentrated between 45 and 52 (71.58%), indicating that both indices accurately describe the dominance of moderate-high instability in the local climate. However, the K index is more closely related to the moisture content in the lower layers of the atmosphere and, therefore, more sensitive to changes in relative humidity and more effective in predicting short-lived local storms. The TT index, however, relies more heavily on the vertical temperature gradient between the 850 and 500 hPa layers, making it better able to represent large-scale disturbances and deep thunderstorms. Therefore, it is recommended to use both indices together for a more accurate assessment of atmospheric instability levels and to predict thunderstorm activity in the region. The results of the analysis of the K-Index and Total Totals Index indicate that the atmospheric climate over Baghdad during the period (1990\u0026ndash;2019) was characterized by a high frequency of moderate-high instability, with a scarcity of severe cases, which reflects a transitional nature of the atmosphere between near-permanent stability during periods of drought and limited instability during the transitional and spring seasons. This distribution reinforces the hypothesis that regional climate change has led to a redistribution of thermal energy and moisture characteristics in the lower layers of the atmosphere so that periods of atmospheric disturbance have become more frequent but less intense.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e illustrates the relative frequency distribution of the available convective thermal energy coefficient (CAPE) across different categories for the period from 1990 to 2019, providing a fundamental basis for understanding atmospheric stability and the potential for thunderstorm development in the study area. The data indicate a prevalence of relatively stable or weak convective weather, with only a limited increase in the potential for severe storms. The data show that the vast majority of CAPE parameter frequencies fall within the following categories:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eCAPE below 300: This is the most common category, representing 56.84% of all observations. This range of very low values ​​indicates stable or very stable atmospheric conditions, where the upward buoyancy is insufficient to support the development of strong thunderstorms. This translates into a significant absence or weakness in convection, which supports the presence of a strong Convective Inhibition (CIN) layer or insufficient heat in the surface layers.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCAPE category 300 to 999: This category represents the third most common category, at 23.08%. These values ​​are usually classified as having below-medium convection energy and indicate the formation of cumulonimbus clouds or marginally unstable, but not intense enough to cause violent weather phenomena. The sum of these two categories is approximately 79.91%, which confirms that the atmospheric cover of the region was, mostly (during the study period), either stable or with a low susceptibility to convection.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCAPE 1000 to 2499 category: This category represents a frequency of 13.46%. These values are considered medium, which indicates the possibility of strong thunderstorms accompanied by heavy rainfall and possibly small hail. This category represents most of the weather events that could be considered \"storms\" in the region. The frequency decreases sharply as the CAPE value increases, reflecting the rarity of extreme instability conditions.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCAPE category 2500 to 3500: Represents a small percentage of 4.49%, which is very low. This range shows highly unstable atmospheric conditions, which increase the chance of severe thunderstorms. Higher values indicate stronger convective energy and a greater risk of severe weather, including supercells, heavy rainfall and hail high wind speed.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCAPE category\u0026thinsp;\u0026gt;\u0026thinsp;3500: This category is the least common, representing only 2.14%. Such elevated values suggest highly violent and rare weather phenomena. These include large hail or tornadoes, and confirm these events are exceptional within the region.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese results can be used as a \"ground truth\" to evaluate the performance of regional climate models in simulating atmospheric stability conditions. The distribution provides the statistical basis for risk assessment; most days of the year require low warnings (within the range of CAPE\u0026thinsp;\u0026lt;\u0026thinsp;1000), while severe storm days (CAPE\u0026thinsp;\u0026gt;\u0026thinsp;2500) are rare and warrant a special early warning system. This distribution can also be compared with the expected changes under global warming scenarios to estimate how much the frequency of strong storms will increase or decrease. The distribution suggests the need for future research to focus on identifying the dynamic and thermodynamic factors (such as wind shear and moisture distribution) that contribute to rare cases of extreme instability, rather than focusing solely on CAPE parameter averages.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the results, the TT index is considered one of the best indicators for predicting thunderstorms. Over 30 years, as TT values ranging between 51 and 52 indicate a high probability of thunderstorms occurring. The data shows that 36.75% of storms in Baghdad fall within this range, making it a reliable indicator. In contrast, the CAPE is less accurate, with 56.84% of thunderstorms falling within a range below 300 (atmospheric stability). Through skew-T analysis, it was found that the majority of convection energy falls within this level (from 900 or 850 to 700 or 650 hPa). However, forecasts often suggest that indicators based on available convective potential energy, such as CAPE, may be more effective in predicting thunderstorms.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2. Analysis and evaluation of the correlation coefficient between Temperature, LCL, EL, Thickness, and atmospheric stability parameter (CAPE)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Pearson correlation coefficient between the temperature, LCL, EL, thickness, and CAPE of thunderstorm days for 1990\u0026ndash;2019 has been calculated for the study period. The results show a relatively strong positive relationship between surface temperature (T) and the lifting condensation level (LCL), with a correlation coefficient of r\u0026thinsp;=\u0026thinsp;0.8 and R\u0026sup2; = 0.6 in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, indicating that a rise in surface temperature leads to a rise in the level of condensation in the atmosphere. The empirical equation for linear regression (LCL = -1083\u0026thinsp;+\u0026thinsp;133.5*T) expresses this relationship, indicating that every 1\u0026deg;C increase in surface temperature corresponds to an increase of approximately 133.5 meters in the height of the condensation level. This behavior is attributed to the fact that a higher surface temperature increases the air's ability to hold water vapor, leading to a decrease in relative humidity and a move away from a state of saturation. As a result, the air needs to rise to higher levels in order to cool and reach the dew point, that is, the point at which actual condensation begins. This explains the marked increase in LCL values with rising temperature. As can be seen from the figure, the dispersion of data points around the regression line indicates that the relationship is also influenced by other factors such as the actual amount of water vapor in the surface layer, daily variations in terrestrial heating and radiation, as well as local surface characteristics (soil moisture, vegetation cover). However, the high correlation strength (r\u0026thinsp;=\u0026thinsp;0.8) confirms that surface temperature remains a key and direct factor in determining the height of the condensation level. In general, this relationship reflects that hot, dry conditions lead to a higher condensation level (LCL), which reduces the likelihood of low cloud formation, while cold, humid conditions lower the LCL and increase the chances of vapor condensation and cumulus cloud formation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e illustrates the relationship between temperature and equilibrium level (values of cape equal to zero have been removed; there are no data for the equilibrium level). The data were analyzed using a linear regression model. The data show a clear positive correlation between them (r\u0026thinsp;=\u0026thinsp;0.5). That is, the rise in temperature is associated with an increase in the height of the equilibrium level in the atmosphere, which is consistent with the thermodynamic principles that link the heating of the lower layers (high temperatures) with the rise in equilibrium levels. The empirical equation for linear regression (LCL\u0026thinsp;=\u0026thinsp;4385.1\u0026thinsp;+\u0026thinsp;205.9*T) expresses this relationship, indicating that every 1\u0026deg;C increase in surface temperature corresponds to an increase of approximately 205.9 meters in the height of the condensation level. Despite the clear positive trend, the graph points show a large dispersion around the red regression line. This dispersion indicates that the strength of the correlation is moderate and that temperature, although an important factor, does not alone explain the entire variance in the equilibrium level, indicating the intervention of other atmospheric factors such as (humidity content, wind shear, and vertical changes in the atmosphere) in predictive models to increase the accuracy of explaining the variance in the equilibrium level. This is because surface heating raises the temperature of air parcels near the surface, which leads to a decrease in their density and gives them the necessary acceleration to begin the process of floating within the atmosphere, and thus they rise to higher layers, where they can release their latent energy in the form of effective convection. This indicates that surface temperature is the primary factor in the initiation and maintenance of thermal instability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrom Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, (values of cape equal to zero have been removed; there are no data for the equilibrium level) the data indicate a strong positive correlation (R\u0026sup2; = 0.7) between the potential available convective energy (CAPE) and the equilibrium level (EL). This relationship was plotted using an exponential growth model (ExpGro2). As the CAPE value increases, the equilibrium level (EL) also increases. The increase in the equilibrium level is sharp and rapid at low and medium CAPE values (less than about 2000 J/kg), then the curve begins to saturate or flatten at high CAPE values. This indicates that the effect of increasing CAPE on raising the equilibrium level becomes less pronounced after a certain limit, and the equilibrium level approaches an approximate maximum value. The value of the adjusted coefficient of determination (Adj. R-Square) of 0.7 provides strong predictive power. Although there are scattered points with high dispersion. This analysis confirms the important role of CAPE as a catalyst for convection and its direct relationship to the maximum height of deep convective clouds.\u003c/p\u003e \u003cp\u003eFrom Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e, (values of cape equal to zero have been removed; there are no data for the equilibrium level) the data clearly indicate a strong positive and non-linear correlation (R\u0026sup2; = 0.9) between the potential available convection energy (CAPE) and thickness. Using an exponential model (ExpGro2), the layer thickness increases as the CAPE value increases. This increase is more pronounced and rapid at lower CAPE values (below approximately 1500 J/kg). As CAPE continues to increase, the curve begins to flatten (saturate), indicating that the effect of subsequent CAPE increases on layer thickness becomes less significant at higher values. The value of the adjusted coefficient of determination (Adj. R-Square) of 0.7 provides strong predictive power. Despite the strong correlation, a noticeable dispersion is observed in the graphs, particularly at high CAPE values. This dispersion suggests that CAPE is not the sole factor influencing layer thickness and that other atmospheric factors play a significant role in the remaining variation. This indicates that surface temperature is the primary factor in initiating and sustaining thermal and atmospheric instability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows statistical relationships using linear and non-linear/exponential growth (ExpGro2) models. Temperature (T) had medium to strong, significant linear correlations with EL and thermal lift (LCL) (R\u0026thinsp;=\u0026thinsp;0.5\u0026ndash;0.8, P-values\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;0.0002\u0026ndash;\u0026gt;0.0003). Available convection energy (CAPE) showed a strong exponential relationship with equilibrium level (R\u0026sup2; = 0.7, P\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;0.0005) and thickness (R\u0026sup2; = 0.9, P\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;0.0004). The P-values ​​are considered reliable because they are less than 0.05, meaning the relationship between all variables was strong and significant (statistically significant).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the linear regression analysis of the relationship between the selected variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003cp\u003e(Relationship)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT and LCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e133.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0002\u0026gt;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT and EL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4385.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e205.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0003\u0026gt;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the non-linear regression analysis of the relationship between the selected variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003cp\u003e(Relationship)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAPE and EL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExpGro2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3973.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-185.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3197.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13805.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0005\u0026gt;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAPE and Thickness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExpGro2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3897.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-123.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1030.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9196.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0004\u0026gt;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe relationship or correlation coefficient between temperature, LCL, LFC, EL, thickness, and the atmospheric stability parameter (CAPE).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCAPE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLCL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThickness\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAPE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThickness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo understand the correlation coefficient (R) in the table (7), it is a statistical measure (ranging from \u0026minus;\u0026thinsp;1 to +\u0026thinsp;1) that indicates the strength and direction of the linear relationship between two variables. To understand the results more deeply, classify the correlations as positive and negative.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eVery strong correlation between EL and Thickness\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e(R\u0026thinsp;=\u0026thinsp;0.9): This is the highest positive correlation. It indicates that any increase in EL (the altitude at which rising convection currents cease to ascend) is closely related to an increase in Thickness (the thickness of a given atmospheric layer, which is closely related to the average temperature of that layer). Greater thickness (warmer atmosphere) typically allows convection cells to develop and reach higher altitudes (higher EL).\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStrong Correlation Between (T) and (LCL)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e(R\u0026thinsp;=\u0026thinsp;0.8): A strong positive correlation. This indicates that a rise in T is strongly correlated with a rise in LCL (the level at which moisture condenses to form clouds). Warmer air can hold more moisture before reaching the condensation point, thus raising the LCL level. As a result, the air needs to rise to higher levels in order to cool and reach the dew point, that is, the point at which actual condensation begins. In general, this relationship reflects that hot, dry conditions lead to a higher condensation level, while cold, humid conditions lower the LCL and increase the chances of vapor condensation and cumulus cloud formation.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStrong correlations between CAPE, EL, and Thickness\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e(R\u0026thinsp;=\u0026thinsp;0.7 for both): This indicates that an increase in CAPE (available convective potential energy, a measure of atmospheric instability) is strongly associated with warmer atmospheres (thickness) and longer vertical convective cells (EL). High energy (CAPE) is the driver of intense weather phenomena (such as thunderstorms), which often occur in thick atmospheric environments.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eModerate Correlations between T, LFC, and EL, and between LCL and LFC\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e(R\u0026thinsp;=\u0026thinsp;0.5\u0026ndash;0.6): These values ​​indicate a significant and statistically significant relationship, suggesting that the two variables tend to change together. However, other factors (third variables) also influence the final outcome. These correlations demonstrate physical connections that exist but are not as deterministic or direct as very strong correlations. Other factors (such as humidity, wind, and vertical changes in the atmosphere) also play an important role in determining the CAPE value. They were the most frequent mechanisms inhibiting the vertical air movement.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eNegative correlation between LFC, Thickness, and CAPE\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e(R= (-0.1) \u0026ndash; (-0.3)): This is a weak to moderate negative correlation. It indicates that as Thickness (atmospheric warmth) increases, LFC (the free convection level) tends to decrease slightly. This may indicate a complex dynamic, where some warm instability allows convection to begin at lower altitudes. That is, the temperature reaches the condensation point, but weak vertical motion prevents the potential energy from rising upwards.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003e1- Clear time-based distribution of thunderstorms:\u003c/p\u003e\n\u003cp\u003eThe results showed that the highest frequency of thunderstorms in the Baghdad airport station occurs during March to May, accounting for more than 58.56% of all recorded events. This act is due to the surface temperature increasing, which leads to more convection when unstable conditions are formed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2- Annual peak in 1994:\u003c/p\u003e\n\u003cp\u003eThe highest annual frequency of thunderstorms was recorded in 1994, with 26 days of storms. This indicates unusual atmospheric activity associated with increased thermal and humidity instability that year.\u003c/p\u003e\n\u003cp\u003e3- Influencing synoptic systems:\u003c/p\u003e\n\u003cp\u003eMost thunderstorms are found to be caused by shallow mid-latitude lows (55.48%), compared to 44.52% for deep lows, reflecting the transitional nature of the region\u0026apos;s climate between arid and semi-humid.\u003c/p\u003e\n\u003cp\u003e4- Atmospheric stability indicators and their importance in local forecasts:\u003c/p\u003e\n\u003cp\u003eThe K-index ranged between 26 and 30 in most cases (approximately 35.26%), indicating high atmospheric instability. The TT-index ranged between 51 and 52 (36.75%), which is the best indicator for predicting thunderstorms. CAPE values were low for most periods (\u0026lt;300 J/kg), meaning that strong storms are rare in the region. The work demonstrated that combining the K-index and TT-index provides a more accurate diagnosis of atmospheric instability, which is very useful in the development of early warning systems for thunderstorms in semi-arid environments, such as Iraq.\u003c/p\u003e\n\u003cp\u003e5- Statistical correlations:\u003c/p\u003e\n\u003cp\u003eCorrelation analysis revealed moderate to strong positive relationships between temperature, lifting condensation level (LCL) and equilibrium level (EL), confirming that surface temperature is a key factor in promoting thermal turbulence.\u003c/p\u003e\n\u003cp\u003e6- Absence of a long-term trend:\u003c/p\u003e\n\u003cp\u003eNo clear trend toward an increase or decrease in the number of storms was observed over 30 years, suggesting that short- and medium-term oscillations (such as ENSO and NAO) are the primary drivers of storm activity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Imad Abdulridha Jasim Al- Khulaifawi] and [Aqeel Ghazi Mutar]. The first draft of the manuscript was written by and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;The datasets generated during and analysed during the current study are available in the [ECMWF/ERA-5] repository, [https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=download] and [https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=download]\u0026rdquo;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAllen JT, Karoly DJ (2013) A climatology of Australian severe thunderstorm environments 1979\u0026ndash;2011: Inter-annual variability and ENSO influence. 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Geosci Res 3:100\u0026ndash;108\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Thunderstorm, Atmospheric instability, K-index, Totals Totals Index, CAPE, Baghdad airport","lastPublishedDoi":"10.21203/rs.3.rs-8683928/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8683928/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThunderstorms are a meteorological phenomenon caused by atmospheric instability. Temperature, wind speed, barometric pressure, rain, and hail all abruptly vary during thunderstorms. In order to create statistical classifications of thundercloud frequencies in the city of Baghdad, this work assesses thunderclouds in terms of their structure, physical attributes, and accompanying atmospheric stability indicators. The Baghdad airport weather station and ERA-5 reanalysis data provided the data for the years 1990 to 2019. Following processing and calculation, the outcome reveals that 1994 had the highest yearly frequency of thunderstorms. According to the study, thunderstorm frequency is highest in the spring (58.56%). The TT and K indices were computed. The TT index category 51\u0026ndash;52, which accounts for roughly 36.75% of all cases, is most frequently linked to thundercloud development. About 35.26% of all occurrences fall into the k-index category 26\u0026ndash;30, which is most commonly linked to thundercloud production. Temperature, lifting condensation level (LCL), and equilibrium level (EL) are all moderately to significantly positively connected with one another, according to Pearson correlation analysis. However, the equilibrium level and thickness have been found to be strongly positively correlated with the convective accessible potential energy (CAPE).\u003c/p\u003e","manuscriptTitle":"Thundercloud assessment for the years 1990–2019 over the Baghdad airport station","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-02 08:58:38","doi":"10.21203/rs.3.rs-8683928/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-22T17:40:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-22T10:43:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-09T14:29:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71548696459106839905865013982628304187","date":"2026-01-30T08:43:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272010259142830985968936083736765454087","date":"2026-01-29T08:29:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-29T07:01:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-26T23:07:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-26T23:06:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2026-01-24T05:16:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9904da1c-bcf6-492f-89ba-484e3bdde95d","owner":[],"postedDate":"February 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:04:10+00:00","versionOfRecord":{"articleIdentity":"rs-8683928","link":"https://doi.org/10.1007/s00704-026-06187-x","journal":{"identity":"theoretical-and-applied-climatology","isVorOnly":false,"title":"Theoretical and Applied Climatology"},"publishedOn":"2026-04-10 15:58:11","publishedOnDateReadable":"April 10th, 2026"},"versionCreatedAt":"2026-02-02 08:58:38","video":"","vorDoi":"10.1007/s00704-026-06187-x","vorDoiUrl":"https://doi.org/10.1007/s00704-026-06187-x","workflowStages":[]},"version":"v1","identity":"rs-8683928","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8683928","identity":"rs-8683928","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}