{"paper_id":"13a0d7ab-2395-43c5-bcec-4aa6f01e29a2","body_text":"Analyze of atmospheric variations using GNSS signal as atmospheric sensor (PWV-GNSS) in the extreme rainfall events in Rio Grande do Sul (Brazil) in 2024 | 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 Analyze of atmospheric variations using GNSS signal as atmospheric sensor (PWV-GNSS) in the extreme rainfall events in Rio Grande do Sul (Brazil) in 2024 Tayná Aparecida Ferreira Gouveia, Helena Barbieri Azevedo, Afonso Marques Albuquerque, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6456625/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Feb, 2026 Read the published version in Natural Hazards → Version 1 posted 5 You are reading this latest preprint version Abstract At the end of April and beginning of May 2024, the state of Rio Grande do Sul, Brazil, experienced an unprecedented climatic disaster. A combination of meteorological factors—including extreme accumulated precipitation—and the region’s topography led to a rapid and severe rise in river levels. Therefore, numerous cities were inundated, leaving both people and animals homeless, and resulting in several fatalities. Given the magnitude of these events, this study aims to understand their underlying causes and explore strategies for identifying them both retrospectively and in near-real-time (nowcasting). To this end, we analyzed direct and indirect atmospheric measurements over the most affected areas in Rio Grande do Sul, particularly the cities of Porto Alegre and Santa Maria. Data were obtained from multiple sources, encompassing observations at the surface and throughout the atmospheric column. We mapped precipitable water vapor (PWV), precipitation, and flood extents. The recorded disasters were associated with accumulated rainfall levels that were over three times higher in Porto Alegre (457 mm) and five times higher in Santa Maria (719 mm) compared to previous El Niño events (2015 and 2016). Our findings demonstrate that GNSS-derived PWV emerges as a promising atmospheric sensor for quantifying water vapor, with the additional advantage of enabling nowcasting applications approximately 60 minutes (or less) ahead of extreme weather events. Extreme Weather Events GNSS Meteorology Precipitable Water Vapor (PWV) Flood Monitoring Rio Grande do Sul Disaster 2024 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION The precipitation regime in the southern region of Brazil, particularly in the state of Rio Grande do Sul (RS), is characterized by a relatively uniform distribution throughout the year. This pattern results from the influence of various meteorological systems that act across different seasons, continuously altering the region's weather conditions. These systems include frontal systems, cyclones, Mesoscale Convective Complexes (MCCs), Upper Tropospheric Cyclonic Vortices (UTCVs), sea breeze circulations, and instability lines (Reboita et al. 2015 ). In addition to these synoptic and mesoscale systems, interannual-scale phenomena also contribute to modulating the precipitation regime in RS. One of the most prominent is the El Niño–Southern Oscillation (ENSO), a coupled ocean–atmosphere phenomenon in which changes in Sea Surface Temperature (SST) lead to variations in atmospheric pressure and wind patterns. These alterations influence large-scale atmospheric circulation and consequently affect the precipitation regime across several regions of the globe, including South America (Coelho et al. 2002 ; Marengo et al. 2012 ; Tedeschi and Collins 2016 ). This phenomenon exhibits two distinct phases: El Niño, characterized by anomalous warming of the waters in the equatorial Pacific Ocean, and La Niña, marked by cooler-than-average SSTs in the same region. Both phases significantly influence the precipitation regime in the state of Rio Grande do Sul, with El Niño typically enhancing rainfall, while La Niña is often associated with drought conditions. Between April and May 2024, the state of RS experienced unprecedented rainfall. According to the Brazilian National Institute of Meteorology (INMET), Santa Maria recorded an accumulated precipitation of 450.2 mm between April 29 and May 2, while Porto Alegre registered 297.2 mm during the same period. These values represent more than twice the climatological average expected for the entire month. The climatological normals for April and May in Santa Maria and Porto Alegre are 151.1 mm (114.4 mm) and 136.6 mm (112.8 mm), respectively, based on the 30-year average from 1991 to 2020. The 2024 rainfall event was classified as the most severe climate-related disaster in the state's history, marked not only by exceptionally high precipitation levels but also by widespread landslides and severe flooding. According to Silveira et al. ( 2024 ), the northern region of Porto Alegre experienced the highest flood levels, reaching up to 6.0 meters, particularly in the Jacuí River, one of the region’s main waterways. These levels were exacerbated by the backwater effect caused by barriers. The observed water levels exceeded the design capacity of dikes constructed with an overflow threshold of 6.0 meters and a safety margin of 1.25 meters, which had been planned to follow one of the most significant floods in 1941 (maximum flood level of 4.75 meters). Because of the 2024 events, numerous fatalities and disappearances were reported, alongside widespread displacement of people and animals, and the emergence of disease outbreaks (G1 2024 ; BBC 2024; The New York Times 2024 ). From a meteorological perspective, this extreme event was driven by a combination of factors acting at both climatic and synoptic scales. At the beginning of 2024, the region was under the influence of the El Niño phenomenon. During El Niño episodes, positive precipitation anomalies and more frequent storms are typically observed in southern Brazil (Cavalcanti et al. 2009 ; Araújo et al. 2023; Valente et al. 2023 ), along with an intensification of the Low-Level Jet (LLJ) (Silva and Ambrizzi 2006 ; Silva et al. 2009 ). The LLJ plays a key role in transporting moisture from the Amazon region to southern Brazil. Additionally, a cold front was present over the state of Rio Grande do Sul; however, its northward progression was blocked by an atmospheric blocking system over central Brazil, which prevented the front from advancing to lower latitudes. Satellites positioning using Global Navigation Satellite Systems (GNSS) are a well-established technique for monitoring precipitable water vapor (PWV) (Gutman et al. 2003; Sapucci et al. 2014; Sapucci et al. 2019 ). This approach exemplifies the synergy between Geodesy and Meteorology. Meteorology provides atmospheric profiles and forecasts of variable concentrations that are essential for modeling and minimizing the signal delay caused by atmospheric refractivity, thereby improving the accuracy of real-time GNSS positioning. Conversely, Geodesy, through GNSS technology, enables high temporal resolution (on the order of minutes) estimates of atmospheric parameters such as PWV (Gouveia et al. 2020 ) In meteorology, the atmosphere is stratified based on temperature gradients into four primary layers, separated by three boundary surfaces. Meteorological phenomena occur primarily in the troposphere, the lowest layer, which extends from the Earth's surface up to approximately 18 km (Wallace & Hobbs 2005). In geodesy, GNSS satellite signals (radio waves) are affected as they propagate through the atmosphere to ground-based receivers. The Neutrosphere, which extends vertically from the surface to about 50 km altitude, is the atmospheric portion responsible for inducing errors, known as zenith total delay (ZTD), in GNSS signals due to the spatial and temporal variability of atmospheric parameters (Mendes 1999 ; Nievinski 2010; Gouveia et al. 2020 ). The ZTD can be converted into PWV estimates, as detailed in the section PWV and GNSS Data In this way, we quantified the extreme rainfall and the variation of the Neutrosphere during the event, and we took measurements that express this variation from different equipment and techniques. Among them are conventional meteorological stations of surface and high-altitude atmospheric soundings (radiosondes), remote sensing images, and GNSS data. To quantify the extreme rainfall and the associated variations in the Neutrosphere during the event, we employed measurements derived from multiple instruments and methodologies. These included conventional surface meteorological stations, upper-atmosphere soundings (radiosondes), remote sensing imagery, and GNSS data. This study aims to analyze the response of neutral atmospheric variations and zenithal delay (ZTD) during the extreme rainfall events that occurred in Rio Grande do Sul (Brazil) in April and May 2024. The results and analyses based on different data sources are presented in Sections 3 to 7. Section 3 addresses the variability of atmospheric parameters using a 10-year time series; Section 4 presents the ZTD derived from radiosonde data; and Section 5 focuses on remote sensing observations. Finally, Sections 6 and 7 provide a discussion of the results and the final considerations, respectively. DATA AND METHODOLOGY The dataset used in this study integrates multiple data sources, reflecting both the complexity of modeling variations in the neutral atmosphere, particularly during extreme precipitation events—and the limitations imposed by data gaps resulting from the impacts of the disaster. The primary analysis focused on the period from April 6 to May 6, 2024, encompassing conditions before and after the extreme precipitation event that occurred between April 26 and May 5. For the climatological context, a 10-year time series (2015–2024) was considered for the same temporal window (April 6 to May 6). The integration of multiple data sources enabled the analysis of the Neutrosphere across different altitudinal layers and ensured continuity in the dataset when information was missing from any individual source. The data and methodology are organized into four sections: (i) study areas; (ii) surface meteorological stations (INMET 2024) and upper-air observations from radiosondes (with measurements from the surface up to approximately 28 km) (University of Wyoming 2024), as detailed in the Meteorological Data section; (iii) PWV/GNSS data, which provide ZTD estimates from the top of the Neutrosphere (~50 km) to the surface; and (iv) optical imagery from remote sensing satellites, with observations captured at an altitude of approximately 790 km. Study areas In Figure 1, the selected study areas are presented, covering the two cities most affected by the tragedy: Brazil (a), in the state of Rio Grande do Sul (b) Porto Alegre and Santa Maria (c). The average altitudes of the cities of Porto Alegre and Santa Maria (Figure 1(c)) are 10 meters and 115 meters above sea level, respectively. Figure 1 presents the selected study areas, highlighting the two cities most severely affected by the disaster: Brazil (Figure 1a), in the state of Rio Grande do Sul (Figure 1b), Porto Alegre and Santa Maria (Figure 1c) municipalities. The mean altitudes of Porto Alegre and Santa Maria are approximately 10 meters and 115 meters above sea level, respectively. The elevation data shown in Figure 1 were obtained from the ANADEM (National Water Agency Digital Elevation Model) (Laipelt et al. 2024). ANADEM is a 30-meter resolution digital terrain model (DTM) that corrects vegetation bias present in the Copernicus GLO-30 digital elevation model (DEM). It encompasses the entirety of South America and was developed by the Hydraulic Research Institute (Instituto de Pesquisas Hidráulicas, IPH) at the Federal University of Rio Grande do Sul (Universidade Federal do Rio Grande do Sul, UFRGS), in partnership with the National Water and Basic Sanitation Agency (Agência Nacional de Águas e Saneamento Básico, ANA). The data can be accessed at http://ufrgs.br/hge/anadem. Surface and altitude meteorological data Meteorological data were obtained from automatic surface stations and radiosondes. Surface measurements were sourced from the Brazilian National Institute of Meteorology (INMET 2024), specifically from the automatic stations located in Porto Alegre (A801) and Santa Maria (A803). Hourly data on temperature, relative humidity, and precipitation were analyzed for the period from April 1 to May 6 over the past 10 years. Additionally, precipitation data from more than 43 INMET stations across Rio Grande do Sul were utilized for the months of April and May 2024. The identification codes and geographic coordinates of the 45 stations used in the analysis are summarized in Table 1 and spatially represented in Figure 3. Table 1: INMET station codes and coordinates (latitude, longitude, altitude). City Code Latitude Longitude Altitude Santa Vitória Do Palmar A899 -33,74 -53,37 7,41 Jaguarão A836 -32,53 -53,38 31,48 Rio Grande A802 -32,08 -52,17 4,92 Capão Do Leão (Pelotas) A887 -31,80 -52,41 13,00 Cangucu A811 -31,40 -52,70 446,81 Bagé A827 -31,35 -54,01 226,19 Mostardas A878 -31,25 -50,91 3,82 Dom Pedrito A881 -31,00 -54,62 150,00 Camaquã A838 -30,81 -51,83 92,30 Santana Do Livramento A804 -30,75 -55,40 196,00 Cacapava Do Sul A812 -30,55 -53,47 420,82 Encruzilhada Do Sul A893 -30,54 -52,52 427,75 Quaraí A831 -30,37 -56,44 113,05 São Gabriel A832 -30,34 -54,31 114,89 Porto Alegre- Belém Novo B807 -30,19 -51,18 3,30 Porto Alegre - Jardim Botânico A801 -30,05 -51,17 41,18 Tramandaí A834 -30,01 -50,14 4,56 Rio Pardo A813 -29,87 -52,38 106,99 Uruguaiana A809 -29,84 -57,08 74,29 Santa Maria A803 -29,72 -53,72 103,10 Alegrete A826 -29,71 -55,53 120,88 São Vicente Do Sul A889 -29,70 -54,69 134,00 Campo Bom A884 -29,67 -51,06 23,35 Teutônia A882 -29,45 -51,82 81,00 Canela A879 -29,37 -50,83 830,93 Torres A808 -29,35 -49,73 8,44 Santiago A833 -29,19 -54,89 390,03 Bento Gonçalves A840 -29,16 -51,53 623,27 Tupanciretã A886 -29,09 -53,83 462,00 Cambará do Sul A897 -29,05 -50,15 1017,00 Soledade A837 -28,86 -52,54 660,44 São José Dos Ausentes A829 -28,75 -50,06 1228,59 Serafina Corrêa A894 -28,70 -51,87 485,29 Ibirubá A883 -28,65 -53,11 455,27 São Borja A830 -28,65 -56,02 81,08 Cruz Alta A853 -28,60 -53,67 426,69 Vacaria A880 -28,51 -50,88 969,89 São Luiz Gonzaga A852 -28,42 -54,96 245,50 Passo Fundo A839 -28,23 -52,40 680,67 Lagoa Vermelha A844 -28,22 -51,51 833,83 Palmeira Das Missões A856 -27,92 -53,32 614,11 Santa Rosa A810 -27,89 -54,48 272,84 Santo Augusto A805 -27,85 -53,79 489,67 Erechim A828 -27,66 -52,31 777,08 Frederico Westphalen A854 -27,40 -53,43 489,42 The radiosonde data were obtained from University of Wyoming (University of Wyoming 2024) (http://www.weather.uwyo.edu/upperair/sounding.html). A radiosonde is a meteorological instrument carried by a balloon to collect atmospheric data at different altitudes. Radiosonde profiles collect measurements from the balloon's launch point at the Earth's surface up to its burst altitude, approximately 28 km. During its ascent, it records variables such as temperature (T, in °C), atmospheric pressure (P, in hPa), relative humidity (RH - in %), and mixing ratio (MIXR - in g/kg) providing a detailed vertical profile of atmospheric conditions. These high-resolution observations are essential for weather forecasting, climate monitoring, and atmospheric research (Durre et al. 2018; Seidel et al. 2011). In this study, three of these variables—temperature, pressure, and relative humidity—were utilized for further analysis. Although radiosondes are highly effective tools for investigating extreme weather events, their deployment can be hindered under adverse conditions. During the extreme events in Rio Grande do Sul, particularly in Porto Alegre and Santa Maria, intense rainfall and widespread flooding significantly disrupted radiosonde operations. As a result, atmospheric profiling and the derivation of products such as radiosonde-based PWV were limited due to a substantial number of missed launches and data gaps. Table 2 presents the geometric (Geo_Dist, in km) and altimetric (Alt_Dist, in m) distances between the meteorological stations in Porto Alegre and Santa Maria. These values enabled the analysis of atmospheric variable variations based on surface data collected at different altitudes. Notably, in Porto Alegre, the maximum geometric distance between the INMET and radiosonde stations was 6 km, with an altimetric difference of approximately 38 m. Table 2 : Geometric distance (km) (Geo_Dist) and absolute altitude difference (m) (Alt_Dist) between surface meteorological stations (INMET) and altitude stations (radiosondes) in Porto Alegre and Santa Maria and their identification codes (Code). City INMET station Radiosondes station Geo_Dist (Km) Alt_Dist (m) Code Code Santa Maria A803 SBSM 2.06 18.10 Porto Alegre A801 SBPA 5.98 38.18 PWV and GNSS Data The ZTD estimated from GNSS (ZTD/GNSS) quantifies Neutrospheric delay experienced by signals received at a GNSS station with known geographic coordinates. This technique offers high temporal resolution, ranging from one to five minutes, and continuous temporal coverage (24 hours a day, 365 days a year). Although GNSS satellites orbit at altitudes exceeding 20,000 km above the Earth's surface. The ZTD is predominantly induced by the signal’s propagation through the Neutrosphere, which extends up to approximately 50 km. For further details on ZTD/GNSS estimation, refer to Nievinski et al. (2010), Elgered; Wickert (2017b), and Gouveia et al. (2020). There are networks of regional and local GNSS stations worldwide that provide this data free of charge. The Santa Maria and Porto Alegre stations belong to the Brazilian Network for Continuous Monitoring of GNSS Systems (RBMC) (RBMC, 2024). PWV represents the total amount of water vapor contained within a vertical column of the atmosphere, expressed in millimeters of equivalent liquid water. This parameter provides a physically intuitive measure of atmospheric moisture content and can be directly compared with precipitation estimates derived from other observational sources, such as those discussed in previous sections. PWV (determined in millimeters) can be determined from IWV by applying a scale factor, the density of liquid water ( ) (1 kg/m 3 ) (equation 3). If the IWV is determined from the IWV/GNSS, the PWV from equation 3 is called the PWV/GNSS (Bevis et al. 1992; Elgered & Wickert 2017; Gouveia et al. 2020): The IWV and consequently the PWV estimated from GNSS measurements in relative positioning were determined from the Bernese scientific software (Bern 2015). The main files used to obtain the ZTD in Bernese are: observation data, mapping function data (VMF1), precise ephemeris files, Earth rotation parameters, ionosphere, and coordinate files (Bern 2015). Table 3 shows the horizontal and vertical distances between the RBMC stations selected in the cities of Santa Maria (SMAR) and Porto Alegre (POAL) and the nearest INMET. Table 3: Geometric distance (km) (Geo_Dist) and absolute altitude difference (m) (Alt_Dist) between INMET stations and GNSS in Porto Alegre and Santa Maria and their identification codes (Code). City INMET station GNSS Station Geo_Dist (Km) Alt_Dist (m) Code Code Santa Maria A803 SMAR 5.75 0.05 Porto Alegre A801 POAL 0.77 1.47 The PWV obtained from radiosonde data is considered a reference for evaluating PWV-GNSS due to its ability to collect in situ atmospheric information. Radiosonde data will be processed using the automatic Neutrosphere Processing Tool (NPTool), to calculate the zenith delay and PWV (Lima et al. 2019; Albuquerque et al. 2024). Optical images from remote sensing satellites The optical imagery used in this study was acquired from the Sentinel-2 mission, part of the European Space Agency’s (ESA) Copernicus Program. This mission comprises a constellation of two satellites, Sentinel-2A (S2A) and Sentinel-2B (S2B), operating in tandem and positioned 180° apart in a Sun-synchronous orbit at an average altitude of 786 km. Together, they provide global coverage with a revisit frequency of five days. Sentinel-2 imagery is captured by the MultiSpectral Instrument (MSI), which offers 13 spectral bands at varying spatial resolutions. The data is openly and freely accessible, supporting a wide range of institutional and research applications. Further technical details on the sensor's specifications are available in ESA (2024a). The data were acquired as close as possible to the April–May 2024 flood event, ensuring cloud-free conditions and minimal influence from prior flooding. This approach aimed to accurately capture the original extent of water bodies and their spatial definitions. To calculate the water-covered area, representing the original delineation of water bodies, data from MapBiomas Collection 8 (2022) were utilized (Mapbiomas 2024). The MapBiomas Project, an initiative of the Climate Observatory, is developed through a collaborative network involving universities, Non-Governmental Organizations (NGOs), and technology companies. Its primary objective is to produce annual maps of land cover and land use in Brazil and to monitor changes in the national territory over time. To determine the extent of areas covered by water following the intense rainfall period, the Modified Normalized Difference Water Index (MNDWI), as proposed by Xu (2006), was computed using Sentinel-2 imagery acquired on May 6, 2024, for both Santa Maria and Porto Alegre. A thresholding technique was then applied to distinguish water bodies from other land cover types. The MNDWI is calculated according to equation 4: MNDWI allows highlighting water features and minimizing the rest of the targets, considering that the green band is located in the region of high water reflectance and the SWIR band in the region of high absorption of water contents. Many previous research works have demonstrated that MNDWI is a suitable spectral index to enhance water information and extract water bodies, such as the identification of watercourses (Li et al. 2013; Du et al. 2014), mapping of flooded areas (Huang et al. 2014; Nandi et al. 2017), among other applications. The value resulting from the MNDWI calculation (Equation 1) varies from -1 to 1. A threshold value of zero was further applied to extract water features from the MNDWI images. That is, the land cover type was classified as water if MNDWI ≥ 0 and non-water if MNDWI < 0. Sentinel-3 is a European Earth Observation satellite mission developed to support a wide range of applications within the Copernicus program, including ocean, land, atmospheric, emergency, security, and cryosphere monitoring. The mission is operated jointly by the European Space Agency (ESA) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). Besides Sentinel-2, Sentinel-3 is also a satellite developed by ESA under the Copernicus program, designed for environmental and oceanographic monitoring. The Sentinel-3 mission comprises two satellites, Sentinel-3A and Sentinel-3B, in a near-polar, sun-synchronous orbit with an inclination of 98.65°. The two operational satellites are equipped with the Ocean and Land Colour Instrument (OLCI), a multispectral sensor operating in 21 spectral bands, providing a revisit time of less than two days at the equator with a spatial resolution of 300 meters across all surfaces. Among the products, the instrument provides atmospheric by-products such as Integrated Water Vapour (IWV) columns, which represent the total amount of water vapor integrated over a column of the atmosphere, expressed in kg/m². This measurement uses the input bands Oa18 (885 nm) and Oa19 (900 nm) (ESA (b), 2024). For this study, an IWV OLCI Level-2 Land Full Resolution (LFR) image, taken on May 9, 2024, was acquired to represent the spatial distribution of IWV over the municipalities of Santa Maria and Porto Alegre. The diverse datasets analyzed in this study enabled a comprehensive assessment of the Neutrosphere from multiple perspectives. In situ measurements of atmospheric variables such as temperature, relative humidity, and precipitation were obtained from automatic surface meteorological stations. Atmospheric profiles from radiosonde launches provided vertical observations from the surface up to approximately 28 km. GNSS data offered estimates of the ZTD, which were subsequently converted into PWV, representing the integrated water vapor content throughout the Neutrosphere down to the station altitude. Additionally, optical remote sensing imagery contributed spatial insights into water vapor distribution from satellite observations. RESULTS AND ANALYSIS The results are organized into three main sections. The first section presents atmospheric variability based on climatological data collected from April 1 to May 6 over the period 2015–2024. The second section analyzes PWV estimates derived from GNSS and radiosonde data during the critical event window, from April 27 to May 7, 2024. Finally, the third section examines the extent of surface water coverage in the study areas and the IWV retrieved from optical satellite imagery acquired during the flood event. Atmospheric Variation To analyze atmospheric variation and the impact of extreme rainfall events, Fig. 2 presents the average of temperature (a), relative humidity (b), and precipitation (c) for the period from April 1 to May 6 from 2015 to 2024 for the stations located in Porto Alegre (purple bar) and Santa Maria (green bar). The light colors indicate El Niño (blue) and La Niña (pink) events. Between 2015 and 2024, the average temperature in Porto Alegre was 20.9°C, while in Santa Maria it was 19.6°C. The maximum temperature recorded was 23°C in Porto Alegre in 2018, one degree Celsius higher than that observed in Santa Maria during the same year. The temperature variation range over the period was 3.46°C in Porto Alegre and 3.97°C in Santa Maria. The difference in average temperatures between El Niño and La Niña periods was minimal, not exceeding 0.15°C at either station. Relative humidity values were consistently high across both regions, with Santa Maria reaching a maximum of 87.38% in 2024 and a minimum of 69.56% in 2020. Precipitation anomalies were particularly pronounced in 2024: Porto Alegre recorded 457 mm of accumulated rainfall, representing an increase of 304% relative to the average for previous El Niño years (2015 and 2016). In Santa Maria, precipitation reached 719.6 mm—approximately five times the average observed during earlier El Niño events. While elevated temperature and humidity are expected for southern Brazil, all meteorological parameters reached their peak values in 2024, with precipitation showing the most significant deviation. Figure 3 displays the accumulated precipitation data from 45 automatic meteorological stations belonging to the INMET network (INMET 2024 ) distributed across the state of Rio Grande do Sul for the months of April (first panel) and May (second panel) of 2024. The observed data (in green) are compared with the climatological averages (in blue) for the same months. The climatological values were derived from 19 nearby conventional INMET stations, using the 30-year reference period from 1991 to 2020. This comparison enables the evaluation of rainfall anomalies by contrasting the recorded values with the region’s expected climatological behavior. It is important to highlight that some stations did not report precipitation measurements due to data unavailability during the studied period (e.g., station A878 in May). A general analysis of accumulated precipitation from INMET stations (Fig. 3 ) reveals that rainfall in May 2024 was predominantly concentrated in the northern and central regions of Rio Grande do Sul, whereas April exhibited a more widespread spatial distribution. In terms of total volume, May registered a higher accumulated precipitation (16.14 mm) compared to April (12.19 mm), with an excess of 3.95 mm. When compared to the climatological normals, precipitation in both April and May 2024 significantly exceeded historical averages across nearly all monitored locations. Exceptions were observed in the municipalities of Encruzilhada do Sul (automatic station A893 and conventional station 83964) and Santa Vitória do Palmar (automatic station A899 and conventional station 83997) in April, as well as in Uruguaiana during May, where precipitation levels remained below or within the climatological expectations. Furthermore, Santa Maria (automatic station A803 and conventional station 83936) recorded the highest accumulated rainfall in April 2024 (520.4 mm), with approximately a 41% increase (369.3 mm) above what was expected by climatology. In May 2024, the city with the most rainfall was Soledade (737.8 mm - A837 ), followed by Serafina Corrêa (714.4 mm - A894), Canela (706.0 mm - A879), Bento Gonçalves (688.6 mm - A840), Cambara do Sul (618.2 mm - A897), Rio Pardo (561.4 mm - A813), Ibiruba (524.6 mm - A883) and Porto Alegre (524.4 mm - A801). PWV from direct measurements of the radiosonde and GNSS estimation Figure 4 displays the PWV estimated from radiosonde data, alongside PWV-GNSS values derived from the ZWD obtained through GNSS data processing using the Bernese GNSS Software, version 5.2. The conversion of ZWD to PWV was carried out using Equations ( 1 ), ( 2 ), and (3), as detailed in the methodology section. Additionally, the figure includes the accumulated precipitation recorded by INMET automatic weather stations located in the municipalities of Porto Alegre and Santa Maria, in the state of Rio Grande do Sul, during the period from April 27 to May 7, 2024. The PWV data reveals a clear relationship with the precipitation events recorded in both cities. In Porto Alegre, the PWV reached a maximum of approximately 60 mm on April 30, with a minimum value around 24 mm. Lower PWV values coincide with days of little to no precipitation (less than 5 mm), whereas higher PWV levels are associated with rainfall events. A similar pattern is observed in Santa Maria, where PWV peaked at around 66 mm and dropped to a minimum of 27 mm. In both locations, a correlation between PWV and precipitation is evident: PWV values tend to increase prior to rainfall events and decrease following them. Figure 5 shows the hourly values of PWV/GNSS, PWV/RDS and precipitation for the day with the highest precipitation during the entire period analyzed. According to Sapucci et al. ( 2019 ), peaks in PWV-GNSS values—referred to as PWV-GNSS jumps—have been observed to precede intense precipitation events within a time window of approximately 32 to 64 minutes. These abrupt increases in precipitable water vapor may serve as indicators of imminent severe rainfall, highlighting their potential as a valuable tool for nowcasting applications. In this study, similar patterns were identified, with sharp increases in PWV-GNSS measurements occurring shortly before the most intense rainfall episodes, particularly in the cities of Porto Alegre and Santa Maria. In Fig. 5 , for Porto Alegre (a), a notable increase in PWV-GNSS is observed at 5:00 a.m., approximately 60 minutes before the onset of a 25 mm precipitation event at 6:00 a.m.—characterizing a PWV-GNSS jump. During the precipitation period, the PWV-GNSS value drops by approximately 9 mm compared to the previous measurement, indicating the release of water vapor through rainfall. In Santa Maria (Fig. 5 (b)), the most significant PWV-GNSS jump is recorded at 3:00 a.m., immediately preceding a sharp increase in precipitation exceeding 25 mm. It is also noted that when precipitation increases more gradually (e.g., between 12:00 and 14:00), the PWV-GNSS rise is smoother. However, the accumulated PWV-GNSS over the preceding three hours (approximately 180 mm) effectively anticipates the intense rainfall recorded in the subsequent four hours (from 12:00 p.m. to 3:00 p.m.), with over 100 mm of accumulated precipitation. On April 30 in Porto Alegre, PWV-GNSS and PWV-RDS measurements were available only at 00 UTC. Precipitation recorded by the automatic weather station was 0 mm, while PWV-RDS and PWV-GNSS values were 54.37 mm and 48.86 mm, respectively. Beyond conventional meteorological applications, radiosonde data are essential for evaluating GNSS-derived atmospheric parameters. In particular, they are employed in the validation of Neutrospheric delay and PWV estimates. As GNSS signals are refracted while traversing the atmosphere—mainly due to variations in temperature, pressure, and humidity—accurate vertical profiles provided by radiosondes contribute to refining Neutrospheric delay models, enhancing the precision of GNSS-based atmospheric monitoring and weather forecasting (Bevis et al. 1992; Bock et al. 2005). Remote Sensing of the region affected by floods For calculating the extent of water coverage after the event, optical images from the Sentinel-2 sensor were collected as close as possible to the flooding event of April/May 2023 (Fig. 6 , a) and b)). The images indicating the situation after the flooding (Fig. 8, c) and d)) were acquired as close as possible to the days of heavy precipitation, May 6th, 2024, while being minimally affected by cloud cover. The figures representing the before and after of the event (Fig. 6 a to 6 d) are shown in false-color R (SWIR − 1.6 µm) G (NIR − 0.84 µm) B (Green − 0.56 µm) Sentinel-2 composite. For the calculation of the water-covered area, which indicates the original delineation of the water bodies in the two municipalities (Fig. 6 , e) and f)), data provided by MapBiomas Collection-8, 2022 (Mapbiomas 2024 ) were used. For the determination of areas covered by water after the heavy rainfall period studied, the MNDWI (Xu 2006 ), was calculated using images acquired on May 6, 2024, for both cities, Santa Maria and Porto Alegre, and a threshold was applied to separate water bodies from other features. The value resulting from the MNDWI calculation (Eq. 1 ) varies from − 1 to 1. A threshold value of zero was further applied to extract water features from the MNDWI images. That is, the land cover type was classified as water if MNDWI ≥ 0 and non-water if MNDWI < 0. As illustrated in Fig. 6 , the extent of water coverage increased significantly in the study areas following the extreme rainfall event of 2024. In the Santa Maria region, the flooded area expanded from 9,180 km² to 114,497 km², while in Porto Alegre, it increased from 21,143 km² to 344,191 km². These values underscore the magnitude of the flooding and its profound impact on the hydrological dynamics of the affected regions. For comparison with GNSS data, a Sentinel-3B OLCI Level-2 LFR image covering the study area was acquired. This image, captured by the sensor on May 9, 2024, provides spatial information on Integrated Water Vapor (IWV). Figure 7 displays the spatial distribution of IWV over the municipalities of Santa Maria and Porto Alegre. As seen in Fig. 7 , IWV values in Porto Alegre range from 9 to 50 kg/m², with higher concentrations observed in localized regions, indicating significant moisture availability likely associated with the occurrence of precipitation. In contrast, Santa Maria shows IWV values ranging from 7 to 12 kg/m², with a more homogeneous spatial distribution and lower moisture content compared to Porto Alegre. The average IWV estimated from Sentinel-3 for Porto Alegre was 27.75 kg/m², while in Santa Maria it was 10.25 kg/m². FINAL CONSIDERATIONS In this study, the objective was to understand the extreme event that occurred in the southern region of Brazil, specifically in the state of Rio Grande do Sul, in the aftermath of the disaster. The tragedy was triggered by the interaction of multiple climatological systems. It resulted from a combination of climatic and synoptic-scale factors. The region was under the influence of El Niño, a phenomenon that typically increases precipitation and storm frequency in southern Brazil. Additionally, El Niño conditions intensified the Low-Level Jet (LLJ), which transports moisture from the Amazon Basin and contributes to the formation of mesoscale convective systems. Additionally, a cold front over Rio Grande do Sul was prevented from advancing northward due to an atmospheric blocking system over central Brazil. This synoptic configuration contributed to accumulated precipitation levels reaching 303% above the climatological average in Porto Alegre and 501% in Santa Maria. Such unprecedented rainfall led to a substantial increase in soil moisture, as reported by Silveira et al. (2024), which limited the infiltration capacity of the soil and, consequently, intensified flooding in the region. To investigate this event, we conducted a comprehensive analysis focused on the period from April 1 to May 6, 2024. Using both direct and indirect measurements, we examined the most affected areas of Rio Grande do Sul, particularly the cities of Porto Alegre and Santa Maria, mapping precipitable water vapor, precipitation, and flood extent. The recorded disasters were associated with accumulated rainfall exceeding 457 mm in Porto Alegre and 719.6 mm in Santa Maria between April 1 and May 6, as measured by 45 automatic pluviometers. These values represent a stark contrast to those recorded during previous El Niño periods in 2015 and 2016, which reached only 150.4 mm and 143.6 mm, respectively. This comparison underscores the exceptional nature of the 2024 event and highlights the anomalous magnitude of precipitation observed in the region. On April 30, 2024, in Porto Alegre (POAL), the highest PWV value was recorded at 05:00 UTC, reaching 60 mm. At that same hour, the pluviometer at SBPA station measured 3 mm of precipitation. In the following hour (06:00 UTC), PWV sharply dropped to 10 mm, while precipitation increased significantly to 26 mm—representing a ninefold increase. In Santa Maria, throughout April 30, PWV-GNSS values remained high, surpassing 58 mm and peaking at 66 mm at 03:00 UTC, with precipitation forecasts exceeding 20 mm. At 12:00 UTC, when radiosonde data became available, the PWV-GNSS measurement was 64.35 mm, only 0.65 mm lower than the PWV derived from the radiosonde (65.01 mm), with recorded precipitation of 9 mm. Water vapor measurements are inherently challenging to model, and these difficulties are amplified during extreme weather events. Throughout this investigation, several observations were missing due to operational limitations. To overcome these gaps, multiple data sources and methodologies were employed, allowing for broader and more accurate coverage. Each data source presents specific advantages and limitations in terms of temporal and spatial resolution, which were considered when interpreting and integrating the measurements. PWV/RDS allowed for the evaluation of climatological variable variations across different atmospheric layers; however, the measurements were limited to only two soundings per day (when available), restricting temporal resolution. In contrast, IWV data derived from Sentinel-3 imagery provided spatially detailed observations at a resolution of 300 meters, capturing atmospheric conditions at a specific moment, with a revisit time of approximately three minutes for each satellite pass. PWV/GNSS, although representing the total columnar water vapor above a station, emerges as a promising alternative due to its high temporal resolution—offering official hourly measurements and even sub-hourly sampling. Moreover, it ensures continuous data availability (24 hours/day) and can be applied in nowcasting, with the ability to detect significant variations in PWV up to 60 minutes or less before the occurrence of precipitation events. Declarations The authors would like to thank from: Coordination for higher Education Staff Development (Fundação Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) – CAPES (Grant numbers 33004129 and Finance Code 001, Grant Number 88887.817766/2023-00), National Council for Scientific and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico) - CNPq (process number 306112/2023-0 and 304773/2021-2), and São Paulo Research Foundation (Fundação de Amparo à Pesquisa do Estado de São Paulo) - FAPESP (Grant: 2023/14739-0). The authors have no relevant financial or non-financial interests to disclose. All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Tayná Aparecida Ferreira Gouveia, Helena Barbieri Azevedo, Afonso Marques Albuquerque, Maria Júlia de Souza Pompei, Aline Barrocá Marra, Viviane Aparecida dos Santos, Daniele Barroca Marra Alves and João Francisco Galera Monico. The first draft of the manuscript was written by Tayná Aparecida Ferreira Gouveia and all authors commented on previous versions of the manuscript. All authors read and approved of the final manuscript. Supervision and Corresponding author: Tayná Aparecida Ferreira Gouveia. References Araujo Rodrigues A, Moreira Siqueira T, Leitzke Caldeira Beskow T, Beskow S, Becker Nunes A (2023). Rainfall trend and variability in Rio Grande do Sul, Brazil. Revista Brasileira De Climatologia, 32(19), 177–207. https://doi.org/10.55761/abclima.v32i19.16179. BBC News. (2025). Landslides and massive flooding kill dozens in Brazil. BBC News. Retrieved April 13, 2025, from https://www.bbc.com/news/articles/c0w03627kq4o. Cavalcanti I, Ferreira NJ, Dias M A F, Justi M G A (2009) Tempo e Clima no Brasil. Oficina de Textos. São Paulo Coelho C A S, Uvo C B, Ambrizzi T (2002) Exploring the impacts of the tropical Pacific SST on the precipitation patterns over South America during ENSO periods. Theoretical and Applied Climatology, 71(3), 185–197. https://doi.org/10.1007/s007040200004. Du Z Q, Li WB, Zhou D B et al (2014) Analysis of Landsat-8 OLI imagery for land surface water mapping. Remote Sens. Lett. 5, 672–681. https://doi.org/10.1080/2150704X.2014.960606. Elgered G, Wickert J. Monitoring of the Neutral Atmosphere. In: TEUNISSEN, P. J. G, MONTENBRUCK, O. (Eds.). Springer Handbook of Global Navigation Satellite Systems. Springer HandbooksCham: Springer International Publishing, 2017. p. 1109–1138. ESA (a) (The European Space Agency), 2024. Sentinel-2. https://sentiwiki.copernicus.eu/web/sentinel-2 (22 July 2024). ESA (b) (The European Space Agency), 2024. Sentinel-3. https://sentiwiki.copernicus.eu/web/sentinel-3 (03 August 2024). Gouveia T, Monico J, Alves D, Sapucci, L F, Geremia-Nievinski F (2020) 50 years of synergy between Space Geodesy and Meteorology: from a GNSS positioning error to precipitation nowcasting applications. Brazilian Journal of Cartography, [S. l.], v. 72, p. 1509–1535 DOI: doi.org/10.14393/rbcv72nespecial50anos-56767. Gutman S I, Sahm S R, Benjamin S G et al (2004) Rapid Retrieval and Assimilation of Ground Based GPS Precipitable Water Observations at the NOAA Forecast Systems Laboratory: Impact on Weather Forecasts. Journal of the Meteorological Society of Japan. Ser. II, [s. l.], v. 82, n. 1B, p. 351–360, https://doi.org/10.2151/jmsj.2004.351. G1. (2024). Um mês de enchentes no RS: veja cronologia do desastre. G1. Retrieved April 13, 2025, from https://g1.globo.com/rs/rio-grande-do-sul/noticia/2024/05/29/um-mes-de-enchentes-no-rs-veja-cronologia-do-desastre.ghtml. Huang C, Chen Y, Wu J P (2014) Mapping spatio-temporal flood inundation dynamics at large river basin scale using time-series flow data and MODIS imagery. Int. J. Appl. Earth Obs. Geoinf. 2014, 26, 350–362. https://doi.org/10.1016/j.jag.2013.09.002. INMET. (2024). Banco de Dados Meteorológicos para Ensino e Pesquisa (BDMEP). Instituto Nacional de Meteorologia. Retrieved April 13, 2025, from https://bdmep.inmet.gov.br/. Laipelt L, de Andrade B C, Collischonn W, de Amorim Teixeira A, de Paiva R C D, Ruhoff A (2024) ANADEM: a digital terrain model for South America. Remote Sensing. DOI: https://doi.org/10.3390/rs16132321. Li W B, Du Z Q, Ling F et al (2013) A comparison of land surface water mapping using the normalized difference water index from TM, ETM plus and ALI. Remote Sensing 2013, 5, 5530–5549. https://doi.org/10.3390/rs5115530. MAPBIOMAS. (2024). Coleções MapBiomas – Coleção 8 da Série Anual de Mapas de Cobertura e Uso da Terra do Brasil (2022). Retrieved April 13, 2025, from https://brasil.mapbiomas.org/colecoes-mapbiomas/. Marengo J A, Liebmann B, Grimm A M et al (2012) Recent developments on the South American monsoon system. Int. J. Climatol., 32: 1-21.https://doi.org/10.1002/joc.2254 Mendes, V.D.B. (1999). Modeling the neutral-atmosphere propagation delay in radiometric space techniques (Ph.D. thesis). Dept. of Geodesy and Geomatics Engineering, Fredericton, N.B., Canada, 353 pp. Available from chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://gge.ext.unb.ca/Pubs/TR199.pdf. Nandi I, Srivastava P K, Shah K (2017) Floodplain Mapping through Support Vector Machine and Optical/Infrared Images from Landsat 8 OLI/TIRS Sensors: Case Study from Varanasi. Water Resour Manage 31, 1157–1171 https://doi.org/10.1007/s11269-017-1568-y. Silveira L N, Almeida Jr V H, Yamawaki M K et al (2024). Wide-swath satellite altimetry reveals the 2024 Porto Alegre extreme flood was intensified by backwater effect across choked river section. Authorea. https://doi.org/10.22541/au.171769020.08746753/v1 Nievinski F G, Santos M C (2010) Ray-tracing options to mitigate the neutral atmosphere delay in GPS. Geomatica, [s. l.], v. 64, p. 191–207. https://doi.org/10.5623/geomat-2010-0020 RBM-IBGE. (2024). RBMC – Rede Brasileira de Monitoramento Contínuo dos Sistemas GNSS. Instituto Brasileiro de Geografia e Estatística. Retrieved April 13, 2025, from https://www.ibge.gov.br/geociencias/informacoes-sobre-posicionamento-geodesico/rede-geodesica/16258-rede-brasileira-de-monitoramento-continuo-dos-sistemas-gnss-rbmc.html Reboita, M.S., Krusche, N., Ambrizzi, T., et al. (2015). Entendendo o tempo e o clima na América do Sul. Terrae Didatica, 8(1), 34–50. https://doi.org/10.20396/td.v8i1.8637425. Sapucci, L.F. (2014). Evaluation of modeling water-vapor-weighted mean tropospheric temperature for GNSS-integrated water vapor estimates in Brazil. Journal of Applied Meteorology and Climatology, 53(3), 715–730. https://doi.org/10.1175/JAMC-D-13-048.1. Sapucci, L.F., Machado, L.A.T., Souza, E.M.D., & Campos, T.B. (2019). Global positioning system precipitable water vapour (GPS-PWV) jumps before intense rain events: A potential application to nowcasting. Meteorological Applications, 26(1), 49–63. https://doi.org/10.1002/met.1735. Silva, G.A.M., & Ambrizzi, T. (2006). Inter-El Niño variability and its impact on the South American low-level jet east of the Andes during austral summer – two case studies. Advances in Geosciences, 6, 283–287. https://doi.org/10.5194/adgeo-6-283-2006. Silva, G.A.M., Ambrizzi, T., & Marengo, J.A. (2009). Observational evidences on the modulation of the South American Low Level Jet east of the Andes according to the ENSO variability. Annales Geophysicae, 27, 645–657. https://doi.org/10.5194/angeo-27-645-2009. Souza, C.A. de, & Reboita, M.S. (2021). Ferramenta para o monitoramento dos padrões de teleconexão na América do Sul. Terra e Didática, 17(00), e02109. https://doi.org/10.20396/td.v17i00.8663474. Tedeschi, R.G., & Collins, M. (2016). The influence of ENSO on South American precipitation during austral summer and autumn in observations and models. International Journal of Climatology, 36(2), 618–635. https://doi.org/10.1002/joc.4371. The New York Times. (2024, May 8). Images of a Brazilian city underwater: Torrential rains have caused one of Brazil’s worst floods in modern history, leaving more than 100 dead and nearly an entire state submerged. The New York Times. Retrieved April 13, 2025, from https://www.nytimes.com/2024/05/08/world/americas/brazil-flooding-photos.html. University of Wyoming. (2023). Atmospheric soundings. University of Wyoming. Retrieved December 2024, from http://www.weather.uwyo.edu/upperair/sounding.html. UNIFEI. (2024). Índice de teleconexões: AAO. Universidade Federal de Itajubá. Retrieved April 13, 2025, from https://meteorologia.unifei.edu.br/teleconexoes/indice?id=aao Valente, P.T., Viana, D.R., Aquino, F.E., & Simões, J.C. (2023). Classification of precipitation anomalies in Rio Grande do Sul in ENSO events in the 20th century. Sociedade & Natureza, 35(1). https://doi.org/10.14393/SN-v35-2023-66073. Vianello, R. L, & Alves, A. R. (2000). Meteorologia básica e aplicações. Editora UFV. Xu, H. (2006). Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6456625\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":458761185,\"identity\":\"db317f9a-35aa-4fb7-ab2e-bb54e756bdc1\",\"order_by\":0,\"name\":\"Tayná Aparecida Ferreira Gouveia\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYPACOQiVwGADJBkbD+BTywOhjGFa0kBaGkjQwsBwGEzi1WLPfsbswQ8GA7t+iRzDBw9qztutbT8MtKXGJhqnLTw55oY9DAbJM2fkGBskHLudvO1MIlDLsbTcBpwOyzGT4GH4k2xwIy1NIoHtdrLZAaAWxobDuLXwvzGT/AO0Bagl/UfCv3PJZucfEtAikWMmzQP0i8GN5GMMiW0H7MxuELLlxrMyaRkDgwTJnseHJRL7khPMbgBtScDjF/b+5G2SbyoM7PnZExs//vhmZ292Pv3hgw81Nji1MDBwGDAwGDAkwhRAGAk4lYPteQAi7WFcexzKRsEoGAWjYAQDAI7WXxuhzWv0AAAAAElFTkSuQmCC\",\"orcid\":\"https://orcid.org/0000-0003-1140-752X\",\"institution\":\"UNESP Campus de Presidente Prudente: Universidade Estadual Paulista Julio de Mesquita Filho - 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Campus de Presidente Prudente\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Daniele\",\"middleName\":\"Barroca Marra\",\"lastName\":\"Alves\",\"suffix\":\"\"},{\"id\":458761192,\"identity\":\"334b019e-6140-4c31-9c41-179894fda989\",\"order_by\":7,\"name\":\"João Francisco Galera Monico\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"UNESP Campus de Presidente Prudente: Universidade Estadual Paulista Julio de Mesquita Filho - Campus de Presidente Prudente\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"João\",\"middleName\":\"Francisco Galera\",\"lastName\":\"Monico\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-04-15 16:13:49\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6456625/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6456625/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1007/s11069-025-07748-5\",\"type\":\"published\",\"date\":\"2026-02-24T15:58:32+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":83208559,\"identity\":\"cd6cc0a2-4409-443e-bd5d-bf30d84b5b92\",\"added_by\":\"auto\",\"created_at\":\"2025-05-21 07:57:36\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":304289,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eLocation of the study areas - Brazil (a), the state of Rio Grande do Sul (b), Santa Maria and Porto Alegre municipalities (c) - showing the state's elevation, and the respective study areas according to ANADEM data.\\u003c/p\\u003e\\n\\u003cp\\u003eMaySource: Author (2024).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6456625/v1/7f605175d34a8ac1daf06ab2.png\"},{\"id\":83210071,\"identity\":\"6b9d5ffc-e9d6-46bc-85b8-c44308d04882\",\"added_by\":\"auto\",\"created_at\":\"2025-05-21 08:21:36\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":394049,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAverage of temperature (a) in ºC, relative humidity (b) in %, and precipitation (c) in mm for Porto Alegre (purple bar) and Santa Maria (green bar) in the last 10 years in the period from April 1 to May 6. Highlighted periods of El Niño (blue) and La Niña (pink) events.\\u003c/p\\u003e\\n\\u003cp\\u003eMaySource: Author (2024).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6456625/v1/55c7a88256d8391b85d9c2e2.png\"},{\"id\":83208729,\"identity\":\"2a9c69f2-cc66-4db3-939a-e30837aff1d6\",\"added_by\":\"auto\",\"created_at\":\"2025-05-21 08:05:36\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":342958,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMap of accumulated precipitation from INMET's automatic stations in the Rio Grande do Sul mesoregion, for the months of April and May 2024, and their respective climatologies.\\u003c/p\\u003e\\n\\u003cp\\u003eMaySource: Author (2024).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6456625/v1/05ec2e2716ce8e4fe48b52f1.png\"},{\"id\":83209535,\"identity\":\"205cb946-6988-414d-bff7-2fea2458c159\",\"added_by\":\"auto\",\"created_at\":\"2025-05-21 08:13:36\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":228460,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePWV and precipitation in Porto Alegre and Santa Maria from April 27 to 7 may for 24h (UTC).\\u003c/p\\u003e\\n\\u003cp\\u003eMaySource: Author (2024).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6456625/v1/217e2ed4796d5b5c4f631fdb.png\"},{\"id\":83208567,\"identity\":\"28e7bf1f-5e8f-4279-8129-4305d3b52ff2\",\"added_by\":\"auto\",\"created_at\":\"2025-05-21 07:57:36\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":268207,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePWV (PWV/GNSS and PWV/RDS) and precipitation in Porto Alegre and Santa Maria for 24h (UTC) of the day with the highest rainfall (April 30th).\\u003c/p\\u003e\\n\\u003cp\\u003eMaySource: Author (2024).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6456625/v1/4dc3d95dd2537395def5d99e.png\"},{\"id\":83208733,\"identity\":\"7bc338ea-c3a6-4715-aef4-175725eb3575\",\"added_by\":\"auto\",\"created_at\":\"2025-05-21 08:05:36\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":300174,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSanta Maria and Porto Alegre before and after heavy rains that occurred between late April and early May 2024. Figures a) and b) refer to Santa Maria and Porto Alegre, respectively, in false-color R(SWIR)G(NIR)B(Green) composition to highlight the standard water coverage of the study areas. Figures c) and d) show the water coverage after the flooding. Figures e) and f) represent the extent of water bodies mapped and provided by MapBiomas, Collection 8 (2022). Finally, figures g) and h) depict the water coverage after the flood, mapped using a spectral water index applied to Sentinel-2 optical images.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6456625/v1/41fac772d9230fdc28b9196b.png\"},{\"id\":83209538,\"identity\":\"ae5b2bc9-96bf-430b-a615-b68d00a7b078\",\"added_by\":\"auto\",\"created_at\":\"2025-05-21 08:13:36\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":155918,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSpatial distribution of Integrated Water Vapor (IWV) from the OLCI Level-2 Land Full Resolution product, obtained from the Sentinel-3 sensor on May 9, 2024, between 12:51 and 12:54 UTC, during a period of intense rainfall in the municipalities of Porto Alegre (a) and Santa Maria (b), state of Rio Grande do Sul (RS), Brazil\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6456625/v1/6862e529ea1a93ad0c7fe749.png\"},{\"id\":103765572,\"identity\":\"f5077531-88a8-43e5-accf-792cb22865aa\",\"added_by\":\"auto\",\"created_at\":\"2026-03-02 16:04:53\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2781095,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6456625/v1/82c8e24e-e3f5-44e2-9a66-6a42d122a4fe.pdf\"}],\"financialInterests\":\"\",\"formattedTitle\":\"Analyze of atmospheric variations using GNSS signal as atmospheric sensor (PWV-GNSS) in the extreme rainfall events in Rio Grande do Sul (Brazil) in 2024\",\"fulltext\":[{\"header\":\"INTRODUCTION\",\"content\":\"\\u003cp\\u003eThe precipitation regime in the southern region of Brazil, particularly in the state of Rio Grande do Sul (RS), is characterized by a relatively uniform distribution throughout the year. This pattern results from the influence of various meteorological systems that act across different seasons, continuously altering the region's weather conditions. These systems include frontal systems, cyclones, Mesoscale Convective Complexes (MCCs), Upper Tropospheric Cyclonic Vortices (UTCVs), sea breeze circulations, and instability lines (Reboita et al. \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn addition to these synoptic and mesoscale systems, interannual-scale phenomena also contribute to modulating the precipitation regime in RS. One of the most prominent is the El Ni\\u0026ntilde;o\\u0026ndash;Southern Oscillation (ENSO), a coupled ocean\\u0026ndash;atmosphere phenomenon in which changes in Sea Surface Temperature (SST) lead to variations in atmospheric pressure and wind patterns. These alterations influence large-scale atmospheric circulation and consequently affect the precipitation regime across several regions of the globe, including South America (Coelho et al. \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e; Marengo et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Tedeschi and Collins \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThis phenomenon exhibits two distinct phases: El Ni\\u0026ntilde;o, characterized by anomalous warming of the waters in the equatorial Pacific Ocean, and La Ni\\u0026ntilde;a, marked by cooler-than-average SSTs in the same region. Both phases significantly influence the precipitation regime in the state of Rio Grande do Sul, with El Ni\\u0026ntilde;o typically enhancing rainfall, while La Ni\\u0026ntilde;a is often associated with drought conditions.\\u003c/p\\u003e \\u003cp\\u003eBetween April and May 2024, the state of RS experienced unprecedented rainfall. According to the Brazilian National Institute of Meteorology (INMET), Santa Maria recorded an accumulated precipitation of 450.2 mm between April 29 and May 2, while Porto Alegre registered 297.2 mm during the same period. These values represent more than twice the climatological average expected for the entire month. The climatological normals for April and May in Santa Maria and Porto Alegre are 151.1 mm (114.4 mm) and 136.6 mm (112.8 mm), respectively, based on the 30-year average from 1991 to 2020. The 2024 rainfall event was classified as the most severe climate-related disaster in the state's history, marked not only by exceptionally high precipitation levels but also by widespread landslides and severe flooding.\\u003c/p\\u003e \\u003cp\\u003eAccording to Silveira et al. (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), the northern region of Porto Alegre experienced the highest flood levels, reaching up to 6.0 meters, particularly in the Jacu\\u0026iacute; River, one of the region\\u0026rsquo;s main waterways. These levels were exacerbated by the backwater effect caused by barriers. The observed water levels exceeded the design capacity of dikes constructed with an overflow threshold of 6.0 meters and a safety margin of 1.25 meters, which had been planned to follow one of the most significant floods in 1941 (maximum flood level of 4.75 meters). Because of the 2024 events, numerous fatalities and disappearances were reported, alongside widespread displacement of people and animals, and the emergence of disease outbreaks (G1 \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; BBC 2024; The New York Times \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eFrom a meteorological perspective, this extreme event was driven by a combination of factors acting at both climatic and synoptic scales. At the beginning of 2024, the region was under the influence of the El Ni\\u0026ntilde;o phenomenon. During El Ni\\u0026ntilde;o episodes, positive precipitation anomalies and more frequent storms are typically observed in southern Brazil (Cavalcanti et al. \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Ara\\u0026uacute;jo et al. 2023; Valente et al. \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), along with an intensification of the Low-Level Jet (LLJ) (Silva and Ambrizzi \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e; Silva et al. \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). The LLJ plays a key role in transporting moisture from the Amazon region to southern Brazil. Additionally, a cold front was present over the state of Rio Grande do Sul; however, its northward progression was blocked by an atmospheric blocking system over central Brazil, which prevented the front from advancing to lower latitudes.\\u003c/p\\u003e \\u003cp\\u003eSatellites positioning using Global Navigation Satellite Systems (GNSS) are a well-established technique for monitoring precipitable water vapor (PWV) (Gutman et al. 2003; Sapucci et al. 2014; Sapucci et al. \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). This approach exemplifies the synergy between Geodesy and Meteorology. Meteorology provides atmospheric profiles and forecasts of variable concentrations that are essential for modeling and minimizing the signal delay caused by atmospheric refractivity, thereby improving the accuracy of real-time GNSS positioning. Conversely, Geodesy, through GNSS technology, enables high temporal resolution (on the order of minutes) estimates of atmospheric parameters such as PWV (Gouveia et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003eIn meteorology, the atmosphere is stratified based on temperature gradients into four primary layers, separated by three boundary surfaces. Meteorological phenomena occur primarily in the troposphere, the lowest layer, which extends from the Earth's surface up to approximately 18 km (Wallace \\u0026amp; Hobbs 2005). In geodesy, GNSS satellite signals (radio waves) are affected as they propagate through the atmosphere to ground-based receivers. The Neutrosphere, which extends vertically from the surface to about 50 km altitude, is the atmospheric portion responsible for inducing errors, known as zenith total delay (ZTD), in GNSS signals due to the spatial and temporal variability of atmospheric parameters (Mendes \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e; Nievinski 2010; Gouveia et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). The ZTD can be converted into PWV estimates, as detailed in the section PWV and GNSS Data\\u003c/p\\u003e \\u003cp\\u003eIn this way, we quantified the extreme rainfall and the variation of the Neutrosphere during the event, and we took measurements that express this variation from different equipment and techniques. Among them are conventional meteorological stations of surface and high-altitude atmospheric soundings (radiosondes), remote sensing images, and GNSS data.\\u003c/p\\u003e \\u003cp\\u003eTo quantify the extreme rainfall and the associated variations in the Neutrosphere during the event, we employed measurements derived from multiple instruments and methodologies. These included conventional surface meteorological stations, upper-atmosphere soundings (radiosondes), remote sensing imagery, and GNSS data.\\u003c/p\\u003e \\u003cp\\u003eThis study aims to analyze the response of neutral atmospheric variations and zenithal delay (ZTD) during the extreme rainfall events that occurred in Rio Grande do Sul (Brazil) in April and May 2024. The results and analyses based on different data sources are presented in Sections 3 to 7. Section 3 addresses the variability of atmospheric parameters using a 10-year time series; Section 4 presents the ZTD derived from radiosonde data; and Section 5 focuses on remote sensing observations. Finally, Sections 6 and 7 provide a discussion of the results and the final considerations, respectively.\\u003c/p\\u003e\"},{\"header\":\"DATA AND METHODOLOGY\",\"content\":\"\\u003cp\\u003eThe dataset used in this study integrates multiple data sources, reflecting both the complexity of modeling variations in the neutral atmosphere, particularly during extreme precipitation events\\u0026mdash;and the limitations imposed by data gaps resulting from the impacts of the disaster. The primary analysis focused on the period from April 6 to May 6, 2024, encompassing conditions before and after the extreme precipitation event that occurred between April 26 and May 5. For the climatological context, a 10-year time series (2015\\u0026ndash;2024) was considered for the same temporal window (April 6 to May 6).\\u003c/p\\u003e\\n\\u003cp\\u003eThe integration of multiple data sources enabled the analysis of the Neutrosphere across different altitudinal layers and ensured continuity in the dataset when information was missing from any individual source. The data and methodology are organized into four sections: (i) study areas; (ii) surface meteorological stations (INMET 2024) and upper-air observations from radiosondes (with measurements from the surface up to approximately 28 km) (University of Wyoming 2024), as detailed in the Meteorological Data section; (iii) PWV/GNSS data, which provide ZTD estimates from the top of the Neutrosphere (~50 km) to the surface; and (iv) optical imagery from remote sensing satellites, with observations captured at an altitude of approximately 790 km.\\u003c/p\\u003e\\n\\u003ch2\\u003e\\u003cstrong\\u003eStudy areas\\u0026nbsp;\\u003c/strong\\u003e\\u003c/h2\\u003e\\n\\u003cp\\u003eIn Figure 1, the selected study areas are presented, covering the two cities most affected by the tragedy: Brazil (a), in the state of Rio Grande do Sul (b) Porto Alegre and Santa Maria (c). The average altitudes of the cities of Porto Alegre and Santa Maria (Figure 1(c)) are 10 meters and 115 meters above sea level, respectively.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eFigure 1 presents the selected study areas, highlighting the two cities most severely affected by the disaster: Brazil (Figure 1a), in the state of Rio Grande do Sul (Figure 1b), Porto Alegre and Santa Maria (Figure 1c) municipalities. The mean altitudes of Porto Alegre and Santa Maria are approximately 10 meters and 115 meters above sea level, respectively. The elevation data shown in Figure 1 were obtained from the ANADEM (National Water Agency Digital Elevation Model) (Laipelt et al. 2024). ANADEM is a 30-meter resolution digital terrain model (DTM) that corrects vegetation bias present in the Copernicus GLO-30 digital elevation model (DEM). It encompasses the entirety of South America and was developed by the Hydraulic Research Institute (Instituto de Pesquisas Hidr\\u0026aacute;ulicas, IPH) at the Federal University of Rio Grande do Sul (Universidade Federal do Rio Grande do Sul, UFRGS), in partnership with the National Water and Basic Sanitation Agency (Ag\\u0026ecirc;ncia Nacional de \\u0026Aacute;guas e Saneamento B\\u0026aacute;sico, ANA). The data can be accessed at http://ufrgs.br/hge/anadem.\\u003c/p\\u003e\\n\\u003ch2\\u003e\\u003cstrong\\u003eSurface and altitude meteorological data\\u003c/strong\\u003e\\u003c/h2\\u003e\\n\\u003cp\\u003eMeteorological data were obtained from automatic surface stations and radiosondes. Surface measurements were sourced from the Brazilian National Institute of Meteorology (INMET 2024), specifically from the automatic stations located in Porto Alegre (A801) and Santa Maria (A803). Hourly data on temperature, relative humidity, and precipitation were analyzed for the period from April 1 to May 6 over the past 10 years. Additionally, precipitation data from more than 43 INMET stations across Rio Grande do Sul were utilized for the months of April and May 2024. The identification codes and geographic coordinates of the 45 stations used in the analysis are summarized in Table 1 and spatially represented in Figure 3.\\u003c/p\\u003e\\n\\u003cp\\u003eTable 1: INMET station codes and coordinates (latitude, longitude, altitude).\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"513\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eCity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eCode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003eLatitude\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003eLongitude\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003eAltitude\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eSanta Vit\\u0026oacute;ria Do Palmar\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA899\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-33,74\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-53,37\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e7,41\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eJaguar\\u0026atilde;o\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA836\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-32,53\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-53,38\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e31,48\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eRio Grande\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA802\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-32,08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-52,17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e4,92\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eCap\\u0026atilde;o Do Le\\u0026atilde;o (Pelotas)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA887\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-31,80\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-52,41\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e13,00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eCangucu\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA811\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-31,40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-52,70\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e446,81\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eBag\\u0026eacute;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA827\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-31,35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-54,01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e226,19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eMostardas\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA878\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-31,25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-50,91\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e3,82\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eDom Pedrito\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA881\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-31,00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-54,62\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e150,00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eCamaqu\\u0026atilde;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA838\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-30,81\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-51,83\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e92,30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eSantana Do Livramento\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA804\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-30,75\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-55,40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e196,00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eCacapava Do Sul\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA812\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-30,55\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-53,47\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e420,82\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eEncruzilhada Do Sul\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA893\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-30,54\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-52,52\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e427,75\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eQuara\\u0026iacute;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA831\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-30,37\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-56,44\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e113,05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eS\\u0026atilde;o Gabriel\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA832\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-30,34\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-54,31\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e114,89\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003ePorto Alegre- Bel\\u0026eacute;m Novo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eB807\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-30,19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-51,18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e3,30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003ePorto Alegre - Jardim Bot\\u0026acirc;nico\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA801\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-30,05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-51,17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e41,18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eTramanda\\u0026iacute;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA834\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-30,01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-50,14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e4,56\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eRio Pardo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA813\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-29,87\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-52,38\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e106,99\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eUruguaiana\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA809\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-29,84\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-57,08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e74,29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eSanta Maria\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA803\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-29,72\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-53,72\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e103,10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eAlegrete\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA826\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-29,71\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-55,53\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e120,88\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eS\\u0026atilde;o Vicente Do Sul\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA889\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-29,70\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-54,69\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e134,00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eCampo Bom\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA884\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-29,67\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-51,06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e23,35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eTeut\\u0026ocirc;nia\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA882\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-29,45\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-51,82\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e81,00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eCanela\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA879\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-29,37\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-50,83\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e830,93\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eTorres\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA808\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-29,35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-49,73\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e8,44\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eSantiago\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA833\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-29,19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-54,89\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e390,03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eBento Gon\\u0026ccedil;alves\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA840\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-29,16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-51,53\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e623,27\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eTupanciret\\u0026atilde;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA886\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-29,09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-53,83\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e462,00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eCambar\\u0026aacute; do Sul\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA897\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-29,05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-50,15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e1017,00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eSoledade\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA837\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-28,86\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-52,54\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e660,44\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eS\\u0026atilde;o\\u0026nbsp;Jos\\u0026eacute; Dos Ausentes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA829\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-28,75\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-50,06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e1228,59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eSerafina Corr\\u0026ecirc;a\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA894\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-28,70\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-51,87\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e485,29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eIbirub\\u0026aacute;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA883\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-28,65\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-53,11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e455,27\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eS\\u0026atilde;o Borja\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA830\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-28,65\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-56,02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e81,08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eCruz Alta\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA853\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-28,60\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-53,67\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e426,69\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eVacaria\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA880\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-28,51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-50,88\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e969,89\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eS\\u0026atilde;o Luiz Gonzaga\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA852\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-28,42\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-54,96\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e245,50\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003ePasso Fundo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA839\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-28,23\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-52,40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e680,67\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eLagoa Vermelha\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA844\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-28,22\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-51,51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e833,83\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003ePalmeira Das Miss\\u0026otilde;es\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA856\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-27,92\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-53,32\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e614,11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eSanta Rosa\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA810\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-27,89\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-54,48\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e272,84\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eSanto Augusto\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA805\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-27,85\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-53,79\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e489,67\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eErechim\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA828\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-27,66\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-52,31\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e777,08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eFrederico Westphalen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003eA854\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e-27,40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 86px;\\\"\\u003e\\n \\u003cp\\u003e-53,43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003e489,42\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u0026nbsp;The radiosonde data were obtained from University of Wyoming (University of Wyoming 2024) (http://www.weather.uwyo.edu/upperair/sounding.html). A radiosonde is a meteorological instrument carried by a balloon to collect atmospheric data at different altitudes. Radiosonde profiles collect measurements from the balloon\\u0026apos;s launch point at the Earth\\u0026apos;s surface up to its burst altitude, approximately 28 km. During its ascent, it records variables such as temperature (T, in \\u0026deg;C), atmospheric pressure (P, in hPa), relative humidity (RH - in %), and mixing ratio (MIXR - in g/kg) providing a detailed vertical profile of atmospheric conditions. These high-resolution observations are essential for weather forecasting, climate monitoring, and atmospheric research (Durre et al. 2018; Seidel et al. 2011). In this study, three of these variables\\u0026mdash;temperature, pressure, and relative humidity\\u0026mdash;were utilized for further analysis.\\u003c/p\\u003e\\n\\u003cp\\u003eAlthough radiosondes are highly effective tools for investigating extreme weather events, their deployment can be hindered under adverse conditions. During the extreme events in Rio Grande do Sul, particularly in Porto Alegre and Santa Maria, intense rainfall and widespread flooding significantly disrupted radiosonde operations. As a result, atmospheric profiling and the derivation of products such as radiosonde-based PWV were limited due to a substantial number of missed launches and data gaps.\\u003c/p\\u003e\\n\\u003cp\\u003eTable 2 presents the geometric (Geo_Dist, in km) and altimetric (Alt_Dist, in m) distances between the meteorological stations in Porto Alegre and Santa Maria. These values enabled the analysis of atmospheric variable variations based on surface data collected at different altitudes. Notably, in Porto Alegre, the maximum geometric distance between the INMET and radiosonde stations was 6 km, with an altimetric difference of approximately 38 m.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 2\\u003c/strong\\u003e: Geometric distance (km) (Geo_Dist) and absolute altitude difference (m) (Alt_Dist) between surface meteorological stations (INMET) and altitude stations (radiosondes) in Porto Alegre and Santa Maria and their identification codes (Code).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"551\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"3\\\" style=\\\"width: 92px;\\\"\\u003e\\n \\u003cp\\u003eCity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 107px;\\\"\\u003e\\n \\u003cp\\u003eINMET station\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eRadiosondes station\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" style=\\\"width: 110px;\\\"\\u003e\\n \\u003cp\\u003eGeo_Dist (Km)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" style=\\\"width: 101px;\\\"\\u003e\\n \\u003cp\\u003eAlt_Dist (m)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 107px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 107px;\\\"\\u003e\\n \\u003cp\\u003eCode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eCode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 92px;\\\"\\u003e\\n \\u003cp\\u003eSanta Maria\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 107px;\\\"\\u003e\\n \\u003cp\\u003eA803\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eSBSM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 110px;\\\"\\u003e\\n \\u003cp\\u003e2.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 101px;\\\"\\u003e\\n \\u003cp\\u003e18.10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 92px;\\\"\\u003e\\n \\u003cp\\u003ePorto Alegre\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 107px;\\\"\\u003e\\n \\u003cp\\u003eA801\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eSBPA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 110px;\\\"\\u003e\\n \\u003cp\\u003e5.98\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 101px;\\\"\\u003e\\n \\u003cp\\u003e38.18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePWV and GNSS Data\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe ZTD estimated from GNSS (ZTD/GNSS) quantifies Neutrospheric delay experienced by signals received at a GNSS station with known geographic coordinates. This technique offers high temporal resolution, ranging from one to five minutes, and continuous temporal coverage (24 hours a day, 365 days a year). Although GNSS satellites orbit at altitudes exceeding 20,000 km above the Earth\\u0026apos;s surface. The ZTD is predominantly induced by the signal\\u0026rsquo;s propagation through the Neutrosphere, which extends up to approximately 50 km. For further details on ZTD/GNSS estimation, refer to Nievinski et al. (2010), Elgered; Wickert (2017b), and Gouveia et al. (2020).\\u003c/p\\u003e\\n\\u003cp\\u003eThere are networks of regional and local GNSS stations worldwide that provide this data free of charge. The Santa Maria and Porto Alegre stations belong to the Brazilian Network for Continuous Monitoring of GNSS Systems (RBMC) (RBMC, 2024).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cimg 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\\\"\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePWV represents the total amount of water vapor contained within a vertical column of the atmosphere, expressed in millimeters of equivalent liquid water. This parameter provides a physically intuitive measure of atmospheric moisture content and can be directly compared with precipitation estimates derived from other observational sources, such as those discussed in previous sections. PWV (determined in millimeters) can be determined from IWV by applying a scale factor, the density of liquid water (\\u003cimg width=\\\"15\\\" height=\\\"19\\\" src=\\\"data:image/png;base64,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\\\" alt=\\\"image\\\"\\u003e) (1 kg/m\\u003csup\\u003e3\\u003c/sup\\u003e) (equation 3). If the IWV is determined from the IWV/GNSS, the PWV from equation 3 is called the PWV/GNSS (Bevis et al. 1992; Elgered \\u0026amp; Wickert 2017; Gouveia et al. 2020):\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cimg src=\\\"data:image/png;base64,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\\\"\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe IWV and consequently the PWV estimated from GNSS measurements in relative positioning were determined from the Bernese scientific software (Bern 2015). The main files used to obtain the ZTD in Bernese are: observation data, mapping function data (VMF1), precise ephemeris files, Earth rotation parameters, ionosphere, and coordinate files (Bern 2015).\\u003c/p\\u003e\\n\\u003cp\\u003eTable 3 shows the horizontal and vertical distances between the RBMC stations selected in the cities of Santa Maria (SMAR) and Porto Alegre (POAL) and the nearest INMET.\\u003c/p\\u003e\\n\\u003cp\\u003eTable 3: Geometric distance (km) (Geo_Dist) and absolute altitude difference (m) (Alt_Dist) between INMET stations and GNSS in Porto Alegre and Santa Maria and their identification codes (Code).\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"516\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 92px;\\\"\\u003e\\n \\u003cp\\u003eCity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 107px;\\\"\\u003e\\n \\u003cp\\u003eINMET station\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003eGNSS Station\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003eGeo_Dist (Km)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003eAlt_Dist (m)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 107px;\\\"\\u003e\\n \\u003cp\\u003eCode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003eCode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 92px;\\\"\\u003e\\n \\u003cp\\u003eSanta Maria\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 107px;\\\"\\u003e\\n \\u003cp\\u003eA803\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003eSMAR\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e5.75\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 92px;\\\"\\u003e\\n \\u003cp\\u003ePorto Alegre\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 107px;\\\"\\u003e\\n \\u003cp\\u003eA801\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003ePOAL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e0.77\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e1.47\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe PWV obtained from radiosonde data is considered a reference for evaluating PWV-GNSS due to its ability to collect in situ atmospheric information. Radiosonde data will be processed using the automatic Neutrosphere Processing Tool (NPTool), to calculate the zenith delay and PWV (Lima et al. 2019; Albuquerque et al. 2024).\\u003c/p\\u003e\\n\\u003ch2\\u003e\\u003cstrong\\u003eOptical images from remote sensing satellites\\u003c/strong\\u003e\\u003c/h2\\u003e\\n\\u003cp\\u003eThe optical imagery used in this study was acquired from the Sentinel-2 mission, part of the European Space Agency\\u0026rsquo;s (ESA) Copernicus Program. This mission comprises a constellation of two satellites, Sentinel-2A (S2A) and Sentinel-2B (S2B), operating in tandem and positioned 180\\u0026deg; apart in a Sun-synchronous orbit at an average altitude of 786 km. Together, they provide global coverage with a revisit frequency of five days.\\u003c/p\\u003e\\n\\u003cp\\u003eSentinel-2 imagery is captured by the MultiSpectral Instrument (MSI), which offers 13 spectral bands at varying spatial resolutions. The data is openly and freely accessible, supporting a wide range of institutional and research applications. Further technical details on the sensor\\u0026apos;s specifications are available in ESA (2024a).\\u003c/p\\u003e\\n\\u003cp\\u003eThe data were acquired as close as possible to the April\\u0026ndash;May 2024 flood event, ensuring cloud-free conditions and minimal influence from prior flooding. This approach aimed to accurately capture the original extent of water bodies and their spatial definitions.\\u003c/p\\u003e\\n\\u003cp\\u003eTo calculate the water-covered area, representing the original delineation of water bodies, data from MapBiomas Collection 8 (2022) were utilized (Mapbiomas 2024). The MapBiomas Project, an initiative of the Climate Observatory, is developed through a collaborative network involving universities, Non-Governmental Organizations (NGOs), and technology companies. Its primary objective is to produce annual maps of land cover and land use in Brazil and to monitor changes in the national territory over time.\\u003c/p\\u003e\\n\\u003cp\\u003eTo determine the extent of areas covered by water following the intense rainfall period, the Modified Normalized Difference Water Index (MNDWI), as proposed by Xu (2006), was computed using Sentinel-2 imagery acquired on May 6, 2024, for both Santa Maria and Porto Alegre. A thresholding technique was then applied to distinguish water bodies from other land cover types. The MNDWI is calculated according to equation 4:\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003cimg 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\\\"\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMNDWI allows highlighting water features and minimizing the rest of the targets, considering that the green band is located in the region of high water reflectance and the SWIR band in the region of high absorption of water contents.\\u003c/p\\u003e\\n\\u003cp\\u003eMany previous research works have demonstrated that MNDWI is a suitable spectral index to enhance water information and extract water bodies, such as the identification of watercourses (Li et al. 2013; Du et al. 2014), mapping of flooded areas (Huang et al. 2014; Nandi et al. 2017), among other applications. The value resulting from the MNDWI calculation (Equation 1) varies from -1 to 1. A threshold value of zero was further applied to extract water features from the MNDWI images. That is, the land cover type was classified as water if MNDWI \\u0026ge; 0 and non-water if MNDWI \\u0026lt; 0.\\u003c/p\\u003e\\n\\u003cp\\u003eSentinel-3 is a European Earth Observation satellite mission developed to support a wide range of applications within the Copernicus program, including ocean, land, atmospheric, emergency, security, and cryosphere monitoring. The mission is operated jointly by the European Space Agency (ESA) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT).\\u003c/p\\u003e\\n\\u003cp\\u003eBesides Sentinel-2, Sentinel-3 is also a satellite developed by ESA under the Copernicus program, designed for environmental and oceanographic monitoring. The Sentinel-3 mission comprises two satellites, Sentinel-3A and Sentinel-3B, in a near-polar, sun-synchronous orbit with an inclination of 98.65\\u0026deg;.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe two operational satellites are equipped with the Ocean and Land Colour Instrument (OLCI), a multispectral sensor operating in 21 spectral bands, providing a revisit time of less than two days at the equator with a spatial resolution of 300 meters across all surfaces.\\u003c/p\\u003e\\n\\u003cp\\u003eAmong the products, the instrument provides atmospheric by-products such as Integrated Water Vapour (IWV) columns, which represent the total amount of water vapor integrated over a column of the atmosphere, expressed in kg/m\\u0026sup2;. This measurement uses the input bands Oa18 (885 nm) and Oa19 (900 nm) (ESA (b), 2024).\\u003c/p\\u003e\\n\\u003cp\\u003eFor this study, an IWV OLCI Level-2 Land Full Resolution (LFR) image, taken on May 9, 2024, was acquired to represent the spatial distribution of IWV over the municipalities of Santa Maria and Porto Alegre.\\u003c/p\\u003e\\n\\u003cp\\u003eThe diverse datasets analyzed in this study enabled a comprehensive assessment of the Neutrosphere from multiple perspectives. In situ measurements of atmospheric variables such as temperature, relative humidity, and precipitation were obtained from automatic surface meteorological stations. Atmospheric profiles from radiosonde launches provided vertical observations from the surface up to approximately 28 km. GNSS data offered estimates of the ZTD, which were subsequently converted into PWV, representing the integrated water vapor content throughout the Neutrosphere down to the station altitude. Additionally, optical remote sensing imagery contributed spatial insights into water vapor distribution from satellite observations.\\u003c/p\\u003e\"},{\"header\":\"RESULTS AND ANALYSIS\",\"content\":\"\\u003cp\\u003eThe results are organized into three main sections. The first section presents atmospheric variability based on climatological data collected from April 1 to May 6 over the period 2015\\u0026ndash;2024. The second section analyzes PWV estimates derived from GNSS and radiosonde data during the critical event window, from April 27 to May 7, 2024. Finally, the third section examines the extent of surface water coverage in the study areas and the IWV retrieved from optical satellite imagery acquired during the flood event.\\u003c/p\\u003e\\n\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eAtmospheric Variation\\u003c/h2\\u003e\\n \\u003cp\\u003eTo analyze atmospheric variation and the impact of extreme rainfall events, Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e presents the average of temperature (a), relative humidity (b), and precipitation (c) for the period from April 1 to May 6 from 2015 to 2024 for the stations located in Porto Alegre (purple bar) and Santa Maria (green bar). The light colors indicate El Ni\\u0026ntilde;o (blue) and La Ni\\u0026ntilde;a (pink) events.\\u003c/p\\u003e\\n \\u003cp\\u003eBetween 2015 and 2024, the average temperature in Porto Alegre was 20.9\\u0026deg;C, while in Santa Maria it was 19.6\\u0026deg;C. The maximum temperature recorded was 23\\u0026deg;C in Porto Alegre in 2018, one degree Celsius higher than that observed in Santa Maria during the same year. The temperature variation range over the period was 3.46\\u0026deg;C in Porto Alegre and 3.97\\u0026deg;C in Santa Maria. The difference in average temperatures between El Ni\\u0026ntilde;o and La Ni\\u0026ntilde;a periods was minimal, not exceeding 0.15\\u0026deg;C at either station. Relative humidity values were consistently high across both regions, with Santa Maria reaching a maximum of 87.38% in 2024 and a minimum of 69.56% in 2020. Precipitation anomalies were particularly pronounced in 2024: Porto Alegre recorded 457 mm of accumulated rainfall, representing an increase of 304% relative to the average for previous El Ni\\u0026ntilde;o years (2015 and 2016). In Santa Maria, precipitation reached 719.6 mm\\u0026mdash;approximately five times the average observed during earlier El Ni\\u0026ntilde;o events. While elevated temperature and humidity are expected for southern Brazil, all meteorological parameters reached their peak values in 2024, with precipitation showing the most significant deviation.\\u003c/p\\u003e\\n \\u003cp\\u003eFigure \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e displays the accumulated precipitation data from 45 automatic meteorological stations belonging to the INMET network (INMET \\u003cspan class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e) distributed across the state of Rio Grande do Sul for the months of April (first panel) and May (second panel) of 2024. The observed data (in green) are compared with the climatological averages (in blue) for the same months. The climatological values were derived from 19 nearby conventional INMET stations, using the 30-year reference period from 1991 to 2020. This comparison enables the evaluation of rainfall anomalies by contrasting the recorded values with the region\\u0026rsquo;s expected climatological behavior. It is important to highlight that some stations did not report precipitation measurements due to data unavailability during the studied period (e.g., station A878 in May).\\u003c/p\\u003e\\n \\u003cp\\u003eA general analysis of accumulated precipitation from INMET stations (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e) reveals that rainfall in May 2024 was predominantly concentrated in the northern and central regions of Rio Grande do Sul, whereas April exhibited a more widespread spatial distribution. In terms of total volume, May registered a higher accumulated precipitation (16.14 mm) compared to April (12.19 mm), with an excess of 3.95 mm. When compared to the climatological normals, precipitation in both April and May 2024 significantly exceeded historical averages across nearly all monitored locations. Exceptions were observed in the municipalities of Encruzilhada do Sul (automatic station A893 and conventional station 83964) and Santa Vit\\u0026oacute;ria do Palmar (automatic station A899 and conventional station 83997) in April, as well as in Uruguaiana during May, where precipitation levels remained below or within the climatological expectations.\\u003c/p\\u003e\\n \\u003cp\\u003eFurthermore, Santa Maria (automatic station A803 and conventional station 83936) recorded the highest accumulated rainfall in April 2024 (520.4 mm), with approximately a 41% increase (369.3 mm) above what was expected by climatology. In May 2024, the city with the most rainfall was Soledade (737.8 mm - A837 ), followed by Serafina Corr\\u0026ecirc;a (714.4 mm - A894), Canela (706.0 mm - A879), Bento Gon\\u0026ccedil;alves (688.6 mm - A840), Cambara do Sul (618.2 mm - A897), Rio Pardo (561.4 mm - A813), Ibiruba (524.6 mm - A883) and Porto Alegre (524.4 mm - A801).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003ch3\\u003ePWV from direct measurements of the radiosonde and GNSS estimation\\u003c/h3\\u003e\\n\\u003cp\\u003eFigure \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e displays the PWV estimated from radiosonde data, alongside PWV-GNSS values derived from the ZWD obtained through GNSS data processing using the Bernese GNSS Software, version 5.2. The conversion of ZWD to PWV was carried out using Equations (\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e), (\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), and (3), as detailed in the methodology section. Additionally, the figure includes the accumulated precipitation recorded by INMET automatic weather stations located in the municipalities of Porto Alegre and Santa Maria, in the state of Rio Grande do Sul, during the period from April 27 to May 7, 2024.\\u003c/p\\u003e\\n\\u003cp\\u003eThe PWV data reveals a clear relationship with the precipitation events recorded in both cities. In Porto Alegre, the PWV reached a maximum of approximately 60 mm on April 30, with a minimum value around 24 mm. Lower PWV values coincide with days of little to no precipitation (less than 5 mm), whereas higher PWV levels are associated with rainfall events. A similar pattern is observed in Santa Maria, where PWV peaked at around 66 mm and dropped to a minimum of 27 mm. In both locations, a correlation between PWV and precipitation is evident: PWV values tend to increase prior to rainfall events and decrease following them.\\u003c/p\\u003e\\n\\u003cp\\u003eFigure \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e shows the hourly values of PWV/GNSS, PWV/RDS and precipitation for the day with the highest precipitation during the entire period analyzed.\\u003c/p\\u003e\\n\\u003cp\\u003eAccording to Sapucci et al. (\\u003cspan class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), peaks in PWV-GNSS values\\u0026mdash;referred to as PWV-GNSS jumps\\u0026mdash;have been observed to precede intense precipitation events within a time window of approximately 32 to 64 minutes. These abrupt increases in precipitable water vapor may serve as indicators of imminent severe rainfall, highlighting their potential as a valuable tool for nowcasting applications. In this study, similar patterns were identified, with sharp increases in PWV-GNSS measurements occurring shortly before the most intense rainfall episodes, particularly in the cities of Porto Alegre and Santa Maria.\\u003c/p\\u003e\\n\\u003cp\\u003eIn Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e, for Porto Alegre (a), a notable increase in PWV-GNSS is observed at 5:00 a.m., approximately 60 minutes before the onset of a 25 mm precipitation event at 6:00 a.m.\\u0026mdash;characterizing a PWV-GNSS jump. During the precipitation period, the PWV-GNSS value drops by approximately 9 mm compared to the previous measurement, indicating the release of water vapor through rainfall. In Santa Maria (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e(b)), the most significant PWV-GNSS jump is recorded at 3:00 a.m., immediately preceding a sharp increase in precipitation exceeding 25 mm. It is also noted that when precipitation increases more gradually (e.g., between 12:00 and 14:00), the PWV-GNSS rise is smoother. However, the accumulated PWV-GNSS over the preceding three hours (approximately 180 mm) effectively anticipates the intense rainfall recorded in the subsequent four hours (from 12:00 p.m. to 3:00 p.m.), with over 100 mm of accumulated precipitation.\\u003c/p\\u003e\\n\\u003cp\\u003eOn April 30 in Porto Alegre, PWV-GNSS and PWV-RDS measurements were available only at 00 UTC. Precipitation recorded by the automatic weather station was 0 mm, while PWV-RDS and PWV-GNSS values were 54.37 mm and 48.86 mm, respectively.\\u003c/p\\u003e\\n\\u003cp\\u003eBeyond conventional meteorological applications, radiosonde data are essential for evaluating GNSS-derived atmospheric parameters. In particular, they are employed in the validation of Neutrospheric delay and PWV estimates. As GNSS signals are refracted while traversing the atmosphere\\u0026mdash;mainly due to variations in temperature, pressure, and humidity\\u0026mdash;accurate vertical profiles provided by radiosondes contribute to refining Neutrospheric delay models, enhancing the precision of GNSS-based atmospheric monitoring and weather forecasting (Bevis et al. 1992; Bock et al. 2005).\\u003c/p\\u003e\\n\\u003ch3\\u003eRemote Sensing of the region affected by floods\\u003c/h3\\u003e\\n\\u003cp\\u003eFor calculating the extent of water coverage after the event, optical images from the Sentinel-2 sensor were collected as close as possible to the flooding event of April/May 2023 (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e, a) and b)). The images indicating the situation after the flooding (Fig. 8, c) and d)) were acquired as close as possible to the days of heavy precipitation, May 6th, 2024, while being minimally affected by cloud cover. The figures representing the before and after of the event (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003ea to \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003ed) are shown in false-color R (SWIR \\u0026minus;\\u0026thinsp;1.6 \\u0026micro;m) G (NIR \\u0026minus;\\u0026thinsp;0.84 \\u0026micro;m) B (Green \\u0026minus;\\u0026thinsp;0.56 \\u0026micro;m) Sentinel-2 composite.\\u003c/p\\u003e\\n\\u003cp\\u003eFor the calculation of the water-covered area, which indicates the original delineation of the water bodies in the two municipalities (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e, e) and f)), data provided by MapBiomas Collection-8, 2022 (Mapbiomas \\u003cspan class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e) were used.\\u003c/p\\u003e\\n\\u003cp\\u003eFor the determination of areas covered by water after the heavy rainfall period studied, the MNDWI (Xu \\u003cspan class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e), was calculated using images acquired on May 6, 2024, for both cities, Santa Maria and Porto Alegre, and a threshold was applied to separate water bodies from other features.\\u003c/p\\u003e\\n\\u003cp\\u003eThe value resulting from the MNDWI calculation (Eq. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e) varies from \\u0026minus;\\u0026thinsp;1 to 1. A threshold value of zero was further applied to extract water features from the MNDWI images. That is, the land cover type was classified as water if MNDWI\\u0026thinsp;\\u0026ge;\\u0026thinsp;0 and non-water if MNDWI\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.\\u003c/p\\u003e\\n\\u003cp\\u003eAs illustrated in Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e, the extent of water coverage increased significantly in the study areas following the extreme rainfall event of 2024. In the Santa Maria region, the flooded area expanded from 9,180 km\\u0026sup2; to 114,497 km\\u0026sup2;, while in Porto Alegre, it increased from 21,143 km\\u0026sup2; to 344,191 km\\u0026sup2;. These values underscore the magnitude of the flooding and its profound impact on the hydrological dynamics of the affected regions.\\u003c/p\\u003e\\n\\u003cp\\u003eFor comparison with GNSS data, a Sentinel-3B OLCI Level-2 LFR image covering the study area was acquired. This image, captured by the sensor on May 9, 2024, provides spatial information on Integrated Water Vapor (IWV). Figure \\u003cspan class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e displays the spatial distribution of IWV over the municipalities of Santa Maria and Porto Alegre.\\u003c/p\\u003e\\n\\u003cp\\u003eAs seen in Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e, IWV values in Porto Alegre range from 9 to 50 kg/m\\u0026sup2;, with higher concentrations observed in localized regions, indicating significant moisture availability likely associated with the occurrence of precipitation. In contrast, Santa Maria shows IWV values ranging from 7 to 12 kg/m\\u0026sup2;, with a more homogeneous spatial distribution and lower moisture content compared to Porto Alegre. The average IWV estimated from Sentinel-3 for Porto Alegre was 27.75 kg/m\\u0026sup2;, while in Santa Maria it was 10.25 kg/m\\u0026sup2;.\\u003c/p\\u003e\"},{\"header\":\"FINAL CONSIDERATIONS\",\"content\":\"\\u003cp\\u003eIn this study, the objective was to understand the extreme event that occurred in the southern region of Brazil, specifically in the state of Rio Grande do Sul, in the aftermath of the disaster.\\u003c/p\\u003e\\n\\u003cp\\u003eThe tragedy was triggered by the interaction of multiple climatological systems. It resulted from a combination of climatic and synoptic-scale factors. The region was under the influence of El Niño, a phenomenon that typically increases precipitation and storm frequency in southern Brazil. Additionally, El Niño conditions intensified the Low-Level Jet (LLJ), which transports moisture from the Amazon Basin and contributes to the formation of mesoscale convective systems.\\u003c/p\\u003e\\n\\u003cp\\u003eAdditionally, a cold front over Rio Grande do Sul was prevented from advancing northward due to an atmospheric blocking system over central Brazil. This synoptic configuration contributed to accumulated precipitation levels reaching 303% above the climatological average in Porto Alegre and 501% in Santa Maria. Such unprecedented rainfall led to a substantial increase in soil moisture, as reported by Silveira et al. (2024), which limited the infiltration capacity of the soil and, consequently, intensified flooding in the region.\\u003c/p\\u003e\\n\\u003cp\\u003eTo investigate this event, we conducted a comprehensive analysis focused on the period from April 1 to May 6, 2024. Using both direct and indirect measurements, we examined the most affected areas of Rio Grande do Sul, particularly the cities of Porto Alegre and Santa Maria, mapping precipitable water vapor, precipitation, and flood extent.\\u003c/p\\u003e\\n\\u003cp\\u003eThe recorded disasters were associated with accumulated rainfall exceeding 457 mm in Porto Alegre and 719.6 mm in Santa Maria between April 1 and May 6, as measured by 45 automatic pluviometers. These values represent a stark contrast to those recorded during previous El Niño periods in 2015 and 2016, which reached only 150.4 mm and 143.6 mm, respectively. This comparison underscores the exceptional nature of the 2024 event and highlights the anomalous magnitude of precipitation observed in the region. On April 30, 2024, in Porto Alegre (POAL), the highest PWV value was recorded at 05:00 UTC, reaching 60 mm. At that same hour, the pluviometer at SBPA station measured 3 mm of precipitation. In the following hour (06:00 UTC), PWV sharply dropped to 10 mm, while precipitation increased significantly to 26 mm—representing a ninefold increase. In Santa Maria, throughout April 30, PWV-GNSS values remained high, surpassing 58 mm and peaking at 66 mm at 03:00 UTC, with precipitation forecasts exceeding 20 mm. At 12:00 UTC, when radiosonde data became available, the PWV-GNSS measurement was 64.35 mm, only 0.65 mm lower than the PWV derived from the radiosonde (65.01 mm), with recorded precipitation of 9 mm.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eWater vapor measurements are inherently challenging to model, and these difficulties are amplified during extreme weather events. Throughout this investigation, several observations were missing due to operational limitations. To overcome these gaps, multiple data sources and methodologies were employed, allowing for broader and more accurate coverage. Each data source presents specific advantages and limitations in terms of temporal and spatial resolution, which were considered when interpreting and integrating the measurements.\\u003c/p\\u003e\\n\\u003cp\\u003ePWV/RDS allowed for the evaluation of climatological variable variations across different atmospheric layers; however, the measurements were limited to only two soundings per day (when available), restricting temporal resolution. In contrast, IWV data derived from Sentinel-3 imagery provided spatially detailed observations at a resolution of 300 meters, capturing atmospheric conditions at a specific moment, with a revisit time of approximately three minutes for each satellite pass. PWV/GNSS, although representing the total columnar water vapor above a station, emerges as a promising alternative due to its high temporal resolution—offering official hourly measurements and even sub-hourly sampling. Moreover, it ensures continuous data availability (24 hours/day) and can be applied in nowcasting, with the ability to detect significant variations in PWV up to 60 minutes or less before the occurrence of precipitation events.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eThe authors would like to thank from: Coordination for higher Education Staff Development (Fundação Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) – CAPES (Grant numbers \\u0026nbsp;33004129 and Finance Code 001, Grant Number 88887.817766/2023-00), National Council for Scientific and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico) - CNPq \\u0026nbsp;(process number 306112/2023-0 and \\u0026nbsp;304773/2021-2), and São Paulo Research Foundation (Fundação de Amparo à Pesquisa do Estado de São Paulo) - FAPESP (Grant: 2023/14739-0).\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors have no relevant financial or non-financial interests to disclose.\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Tayná Aparecida Ferreira Gouveia, Helena Barbieri Azevedo, Afonso Marques Albuquerque, Maria Júlia de Souza Pompei, Aline Barrocá Marra, Viviane Aparecida dos Santos, Daniele Barroca Marra Alves and João Francisco Galera Monico. The first draft of the manuscript was written by Tayná Aparecida Ferreira Gouveia and all authors commented on previous versions of the manuscript. All authors read and approved of the final manuscript. Supervision and Corresponding author: Tayná Aparecida Ferreira Gouveia.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eAraujo Rodrigues A, Moreira Siqueira T, Leitzke Caldeira Beskow T, Beskow S, Becker Nunes A (2023). Rainfall trend and variability in Rio Grande do Sul, Brazil. Revista Brasileira De Climatologia, 32(19), 177\\u0026ndash;207. https://doi.org/10.55761/abclima.v32i19.16179.\\u003c/li\\u003e\\n \\u003cli\\u003eBBC News. (2025). Landslides and massive flooding kill dozens in Brazil. BBC News. Retrieved April 13, 2025, from https://www.bbc.com/news/articles/c0w03627kq4o.\\u003c/li\\u003e\\n \\u003cli\\u003eCavalcanti I, Ferreira NJ, Dias M A F, Justi M G A (2009) Tempo e Clima no Brasil. Oficina de Textos. S\\u0026atilde;o Paulo\\u003c/li\\u003e\\n \\u003cli\\u003eCoelho C A S, Uvo C B, Ambrizzi T (2002) Exploring the impacts of the tropical Pacific SST on the precipitation patterns over South America during ENSO periods. Theoretical and Applied Climatology, 71(3), 185\\u0026ndash;197. https://doi.org/10.1007/s007040200004.\\u003c/li\\u003e\\n \\u003cli\\u003eDu Z Q, Li WB, Zhou D B et al (2014) Analysis of Landsat-8 OLI imagery for land surface water mapping. Remote Sens. Lett. 5, 672\\u0026ndash;681. https://doi.org/10.1080/2150704X.2014.960606.\\u003c/li\\u003e\\n \\u003cli\\u003eElgered G, Wickert J. Monitoring of the Neutral Atmosphere. In: TEUNISSEN, P. J. G, MONTENBRUCK, O. (Eds.). Springer Handbook of Global Navigation Satellite Systems. Springer HandbooksCham: Springer International Publishing, 2017. p. 1109\\u0026ndash;1138.\\u003c/li\\u003e\\n \\u003cli\\u003eESA (a) (The European Space Agency), 2024. Sentinel-2. https://sentiwiki.copernicus.eu/web/sentinel-2 (22 July 2024).\\u003c/li\\u003e\\n \\u003cli\\u003eESA (b) (The European Space Agency), 2024. Sentinel-3. https://sentiwiki.copernicus.eu/web/sentinel-3 (03 August 2024).\\u003c/li\\u003e\\n \\u003cli\\u003eGouveia T, Monico J, Alves D, Sapucci, L F, Geremia-Nievinski F (2020) 50 years of synergy between Space Geodesy and Meteorology: from a GNSS positioning error to precipitation nowcasting applications. Brazilian Journal of Cartography, [S. l.], v. 72, p. 1509\\u0026ndash;1535 DOI: doi.org/10.14393/rbcv72nespecial50anos-56767.\\u003c/li\\u003e\\n \\u003cli\\u003eGutman S I, Sahm S R, Benjamin S G et al (2004) Rapid Retrieval and Assimilation of Ground Based GPS Precipitable Water Observations at the NOAA Forecast Systems Laboratory: Impact on Weather Forecasts. Journal of the Meteorological Society of Japan. Ser. II, [s. l.], v. 82, n. 1B, p. 351\\u0026ndash;360, https://doi.org/10.2151/jmsj.2004.351.\\u003c/li\\u003e\\n \\u003cli\\u003eG1. (2024). Um m\\u0026ecirc;s de enchentes no RS: veja cronologia do desastre. G1. Retrieved April 13, 2025, from https://g1.globo.com/rs/rio-grande-do-sul/noticia/2024/05/29/um-mes-de-enchentes-no-rs-veja-cronologia-do-desastre.ghtml.\\u003c/li\\u003e\\n \\u003cli\\u003eHuang C, Chen Y, Wu J P (2014) Mapping spatio-temporal flood inundation dynamics at large river basin scale using time-series flow data and MODIS imagery. Int. J. Appl. Earth Obs. Geoinf. 2014, 26, 350\\u0026ndash;362. https://doi.org/10.1016/j.jag.2013.09.002.\\u003c/li\\u003e\\n \\u003cli\\u003eINMET. (2024). Banco de Dados Meteorol\\u0026oacute;gicos para Ensino e Pesquisa (BDMEP). Instituto Nacional de Meteorologia. Retrieved April 13, 2025, from https://bdmep.inmet.gov.br/.\\u003c/li\\u003e\\n \\u003cli\\u003eLaipelt L, de Andrade B C, Collischonn W, de Amorim Teixeira A, de Paiva R C D, Ruhoff A (2024) ANADEM: a digital terrain model for South America. Remote Sensing. DOI: https://doi.org/10.3390/rs16132321.\\u003c/li\\u003e\\n \\u003cli\\u003eLi W B, Du Z Q, Ling F et al (2013) A comparison of land surface water mapping using the normalized difference water index from TM, ETM plus and ALI. Remote Sensing 2013, 5, 5530\\u0026ndash;5549. https://doi.org/10.3390/rs5115530.\\u003c/li\\u003e\\n \\u003cli\\u003eMAPBIOMAS. (2024). Cole\\u0026ccedil;\\u0026otilde;es MapBiomas \\u0026ndash; Cole\\u0026ccedil;\\u0026atilde;o 8 da S\\u0026eacute;rie Anual de Mapas de Cobertura e Uso da Terra do Brasil (2022). Retrieved April 13, 2025, from https://brasil.mapbiomas.org/colecoes-mapbiomas/.\\u003c/li\\u003e\\n \\u003cli\\u003eMarengo J A, Liebmann B, Grimm A M et al (2012) Recent developments on the South American monsoon system. Int. J. Climatol., 32: 1-21.https://doi.org/10.1002/joc.2254\\u003c/li\\u003e\\n \\u003cli\\u003eMendes, V.D.B. (1999). Modeling the neutral-atmosphere propagation delay in radiometric space techniques (Ph.D. thesis). Dept. of Geodesy and Geomatics Engineering, Fredericton, N.B., Canada, 353 pp. Available from chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://gge.ext.unb.ca/Pubs/TR199.pdf.\\u003c/li\\u003e\\n \\u003cli\\u003eNandi I, Srivastava P K, Shah K (2017) Floodplain Mapping through Support Vector Machine and Optical/Infrared Images from Landsat 8 OLI/TIRS Sensors: Case Study from Varanasi. Water Resour Manage 31, 1157\\u0026ndash;1171 https://doi.org/10.1007/s11269-017-1568-y.\\u003c/li\\u003e\\n \\u003cli\\u003eSilveira L N, Almeida Jr V H, Yamawaki M K et al (2024). Wide-swath satellite altimetry reveals the 2024 Porto Alegre extreme flood was intensified by backwater effect across choked river section. Authorea. https://doi.org/10.22541/au.171769020.08746753/v1\\u003c/li\\u003e\\n \\u003cli\\u003eNievinski F G, Santos M C (2010) Ray-tracing options to mitigate the neutral atmosphere delay in GPS. Geomatica, [s. l.], v. 64, p. 191\\u0026ndash;207. https://doi.org/10.5623/geomat-2010-0020\\u003c/li\\u003e\\n \\u003cli\\u003eRBM-IBGE. (2024). RBMC \\u0026ndash; Rede Brasileira de Monitoramento Cont\\u0026iacute;nuo dos Sistemas GNSS. Instituto Brasileiro de Geografia e Estat\\u0026iacute;stica. Retrieved April 13, 2025, from https://www.ibge.gov.br/geociencias/informacoes-sobre-posicionamento-geodesico/rede-geodesica/16258-rede-brasileira-de-monitoramento-continuo-dos-sistemas-gnss-rbmc.html\\u003c/li\\u003e\\n \\u003cli\\u003eReboita, M.S., Krusche, N., Ambrizzi, T., et al. (2015). Entendendo o tempo e o clima na Am\\u0026eacute;rica do Sul. Terrae Didatica, 8(1), 34\\u0026ndash;50. https://doi.org/10.20396/td.v8i1.8637425.\\u003c/li\\u003e\\n \\u003cli\\u003eSapucci, L.F. (2014). Evaluation of modeling water-vapor-weighted mean tropospheric temperature for GNSS-integrated water vapor estimates in Brazil. Journal of Applied Meteorology and Climatology, 53(3), 715\\u0026ndash;730. https://doi.org/10.1175/JAMC-D-13-048.1.\\u003c/li\\u003e\\n \\u003cli\\u003eSapucci, L.F., Machado, L.A.T., Souza, E.M.D., \\u0026amp; Campos, T.B. (2019). Global positioning system precipitable water vapour (GPS-PWV) jumps before intense rain events: A potential application to nowcasting. Meteorological Applications, 26(1), 49\\u0026ndash;63. https://doi.org/10.1002/met.1735.\\u003c/li\\u003e\\n \\u003cli\\u003eSilva, G.A.M., \\u0026amp; Ambrizzi, T. (2006). Inter-El Ni\\u0026ntilde;o variability and its impact on the South American low-level jet east of the Andes during austral summer \\u0026ndash; two case studies. Advances in Geosciences, 6, 283\\u0026ndash;287. https://doi.org/10.5194/adgeo-6-283-2006.\\u003c/li\\u003e\\n \\u003cli\\u003eSilva, G.A.M., Ambrizzi, T., \\u0026amp; Marengo, J.A. (2009). Observational evidences on the modulation of the South American Low Level Jet east of the Andes according to the ENSO variability. Annales Geophysicae, 27, 645\\u0026ndash;657. https://doi.org/10.5194/angeo-27-645-2009.\\u003c/li\\u003e\\n \\u003cli\\u003eSouza, C.A. de, \\u0026amp; Reboita, M.S. (2021). Ferramenta para o monitoramento dos padr\\u0026otilde;es de teleconex\\u0026atilde;o na Am\\u0026eacute;rica do Sul. Terra e Did\\u0026aacute;tica, 17(00), e02109. https://doi.org/10.20396/td.v17i00.8663474.\\u003c/li\\u003e\\n \\u003cli\\u003eTedeschi, R.G., \\u0026amp; Collins, M. (2016). The influence of ENSO on South American precipitation during austral summer and autumn in observations and models. International Journal of Climatology, 36(2), 618\\u0026ndash;635. https://doi.org/10.1002/joc.4371.\\u003c/li\\u003e\\n \\u003cli\\u003eThe New York Times. (2024, May 8). Images of a Brazilian city underwater: Torrential rains have caused one of Brazil\\u0026rsquo;s worst floods in modern history, leaving more than 100 dead and nearly an entire state submerged. The New York Times. Retrieved April 13, 2025, from https://www.nytimes.com/2024/05/08/world/americas/brazil-flooding-photos.html.\\u003c/li\\u003e\\n \\u003cli\\u003eUniversity of Wyoming. (2023). Atmospheric soundings. University of Wyoming. Retrieved December 2024, from http://www.weather.uwyo.edu/upperair/sounding.html.\\u003c/li\\u003e\\n \\u003cli\\u003eUNIFEI. (2024). \\u0026Iacute;ndice de teleconex\\u0026otilde;es: AAO. Universidade Federal de Itajub\\u0026aacute;. Retrieved April 13, 2025, from https://meteorologia.unifei.edu.br/teleconexoes/indice?id=aao\\u003c/li\\u003e\\n \\u003cli\\u003eValente, P.T., Viana, D.R., Aquino, F.E., \\u0026amp; Sim\\u0026otilde;es, J.C. (2023). Classification of precipitation anomalies in Rio Grande do Sul in ENSO events in the 20th century. Sociedade \\u0026amp; Natureza, 35(1). https://doi.org/10.14393/SN-v35-2023-66073.\\u003c/li\\u003e\\n \\u003cli\\u003eVianello, R. L, \\u0026amp; Alves, A. R. (2000). Meteorologia b\\u0026aacute;sica e aplica\\u0026ccedil;\\u0026otilde;es. Editora UFV.\\u003c/li\\u003e\\n \\u003cli\\u003eXu, H. (2006). Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025\\u0026ndash;3033.\\u003c/li\\u003e\\n\\u003c/ol\\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\":true,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"natural-hazards\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"nhaz\",\"sideBox\":\"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)\",\"snPcode\":\"11069\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11069/3\",\"title\":\"Natural Hazards\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Extreme Weather Events, GNSS Meteorology, Precipitable Water Vapor (PWV), Flood Monitoring, Rio Grande do Sul Disaster 2024\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6456625/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6456625/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eAt the end of April and beginning of May 2024, the state of Rio Grande do Sul, Brazil, experienced an unprecedented climatic disaster. A combination of meteorological factors\\u0026mdash;including extreme accumulated precipitation\\u0026mdash;and the region\\u0026rsquo;s topography led to a rapid and severe rise in river levels. Therefore, numerous cities were inundated, leaving both people and animals homeless, and resulting in several fatalities. Given the magnitude of these events, this study aims to understand their underlying causes and explore strategies for identifying them both retrospectively and in near-real-time (nowcasting). To this end, we analyzed direct and indirect atmospheric measurements over the most affected areas in Rio Grande do Sul, particularly the cities of Porto Alegre and Santa Maria. Data were obtained from multiple sources, encompassing observations at the surface and throughout the atmospheric column. We mapped precipitable water vapor (PWV), precipitation, and flood extents. The recorded disasters were associated with accumulated rainfall levels that were over three times higher in Porto Alegre (457 mm) and five times higher in Santa Maria (719 mm) compared to previous El Ni\\u0026ntilde;o events (2015 and 2016). Our findings demonstrate that GNSS-derived PWV emerges as a promising atmospheric sensor for quantifying water vapor, with the additional advantage of enabling nowcasting applications approximately 60 minutes (or less) ahead of extreme weather events.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Analyze of atmospheric variations using GNSS signal as atmospheric sensor (PWV-GNSS) in the extreme rainfall events in Rio Grande do Sul (Brazil) in 2024\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-05-21 07:57:31\",\"doi\":\"10.21203/rs.3.rs-6456625/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"reviewerAgreed\",\"content\":\"\",\"date\":\"2025-05-21T12:10:17+00:00\",\"index\":0,\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-05-19T13:30:52+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"Natural Hazards\",\"date\":\"2025-05-19T12:56:16+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-04-29T12:26:05+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Natural Hazards\",\"date\":\"2025-04-26T09:15:49+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"natural-hazards\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"nhaz\",\"sideBox\":\"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)\",\"snPcode\":\"11069\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11069/3\",\"title\":\"Natural Hazards\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"8a6091b3-07c9-465f-85ad-b6f1eb84c561\",\"owner\":[],\"postedDate\":\"May 21st, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-03-02T16:01:42+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-6456625\",\"link\":\"https://doi.org/10.1007/s11069-025-07748-5\",\"journal\":{\"identity\":\"natural-hazards\",\"isVorOnly\":false,\"title\":\"Natural Hazards\"},\"publishedOn\":\"2026-02-24 15:58:32\",\"publishedOnDateReadable\":\"February 24th, 2026\"},\"versionCreatedAt\":\"2025-05-21 07:57:31\",\"video\":\"\",\"vorDoi\":\"10.1007/s11069-025-07748-5\",\"vorDoiUrl\":\"https://doi.org/10.1007/s11069-025-07748-5\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6456625\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6456625\",\"identity\":\"rs-6456625\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}