The event was defined using station-specific 99th-percentile thresholds calculated for each season, rather than a single fixed rainfall amount. An event qualified when at least 20% of the 18 gauges simultaneously exceeded their local seasonal threshold. Across the full record, the mean extreme threshold was about 60 millimetres per day, but it ranged from 57 millimetres in winter to 97 millimetres in summer; some southern stations had summer thresholds exceeding 180 millimetres. Summer contained nine identified extremes, autumn seven, spring eight and winter four. This seasonal differentiation matters because the municipality’s warm-season baseline is far wetter than its winter baseline, making an annual threshold alone an incomplete description of local rainfall extremes.

Petrópolis displayed a pronounced rainfall gradient over an area of roughly 791 square kilometres, with an average elevation of 809 metres above sea level. Mean annual totals ranged from approximately 1,000 to more than 2,200 millimetres, with the wettest conditions consistently concentrated in the southern part of the municipality. Southern stations also carried the highest seasonal extreme thresholds: many exceeded 120 millimetres per day in summer, while some central and northern gauges had values around 60–70 millimetres. The difference between the southern Independência station and northern Itaipava station was approximately 125 millimetres in summer. The results indicate that steep slopes, valleys, elevation and exposure to moisture can produce major differences over short distances.

The seasonal cycle was even more pronounced. Summer supplied 48% of annual precipitation, while autumn and spring each contributed 23% and winter only 7%. January was especially wet, with a median near 225 millimetres in the northern sector and 260 millimetres in the south; July medians fell to approximately 18 and 45 millimetres, respectively. The monthly distributions also showed larger variability among southern gauges, with January upper whiskers reaching about 720 millimetres in that sector. The study linked the warm-season concentration to the mature phase of the South American Monsoon System, in which the South Atlantic Convergence Zone and frontal systems influence southeastern Brazil, while winter is comparatively dry.

Hourly records revealed a pronounced evening onset. Significant rainfall most frequently began between 17:00 and 21:00 UTC, with particularly high frequencies around 18:00–20:00 UTC, whereas initiation was least common from roughly 08:00 to 11:00 UTC. Mean event duration varied substantially among stations, from about 2.9 hours at Capim Roxo and 3.0 hours at Samambaia to 9.1 hours at Quitandinha and 8.8 hours at Independência. Because many events began late in the local day and lasted several hours, the researchers used an accumulation window running from 13:00 UTC to 12:00 UTC the following day. This window could keep a long-lived event intact instead of dividing its rainfall between two conventional calendar days and understating its apparent intensity.

The 17 March 2013 episode displayed both exceptional magnitude and broad spatial extent. Rain began around 18:00 UTC, or 15:00 local time, and intensified during the night, reaching its peak around 02:00 UTC on 18 March. Several gauges then recorded hourly totals above 50 millimetres per hour, with Quitandinha reaching 71.5 millimetres per hour and the LNCC station recording 64.25 millimetres per hour. Southern gauges including Quitandinha, Morin, LNCC and Coronel Veiga accumulated more than 400 millimetres over the event, while Posse, Cuiabá, Capim Roxo and Itaipava recorded less than 25 millimetres per day. The Independência station recorded no rain during the analyzed period despite lying in the most affected sector, a gap that raises questions about the representativeness of its record for this episode.

Global Forecast System analyses showed that the rainfall developed within a persistent synoptic environment rather than from one isolated atmospheric trigger. A low-pressure area and frontal system over the South Atlantic moved toward Brazil’s southeastern coast, while the low-pressure center remained nearly stationary around the rainfall peak. At 850 hPa, a south-southeasterly flow transported moist air from the Atlantic toward Rio de Janeiro. Specific humidity of 10–14 grams per kilogram was present before the event, rose above 12 grams per kilogram as intense rain began and remained high through the peak. Aloft, southeastern Brazil lay on the northern flank of a strong Atlantic jet, with core wind speeds exceeding 70 metres per second and locally approaching or surpassing 100 metres per second. Although no organized South Atlantic Convergence Zone episode was present, the offshore low, cold-front structure and persistent maritime moisture transport created favorable large-scale forcing.

Thermodynamic diagnostics indicated that moisture and vertical motion were as important to the event’s persistence as the changing instability. Over Petrópolis, convective available potential energy increased from 1,610 joules per kilogram at 12:00 UTC on 17 March to 2,465 joules per kilogram at 18:00, as the Lifted Index fell from −3.3 to −5.3 degrees Celsius. The K Index rose to 37 degrees Celsius and Total Totals to 44 degrees Celsius, both consistent in the analysis with a high thunderstorm potential. At the rainfall peak, CAPE had fallen to 1,090 joules per kilogram, but precipitable water reached 57.9 millimetres, its maximum value, while the K and Total Totals indices remained at 37 and 43 degrees Celsius. The decrease in CAPE therefore coincided with continued abundant moisture and active convection rather than an end to convective support.

Low-level saturation and ascending motion further organized the event. Relative humidity over Petrópolis reached approximately 100% when intense rainfall began and remained near saturation at the peak. The Galvez–Davison Index exceeded 50 across large parts of Rio de Janeiro, São Paulo and Minas Gerais during intensification, with local cores above 60. At the peak, omega values over the municipality were below −1 pascal per second at 850 hPa, while adjacent areas reached values below −3 pascal per second, indicating stronger ascent in the surrounding region. By 06:00 UTC on 18 March, CAPE had nearly vanished from the study area and vertical-motion maxima had become more scattered, corresponding with the weakening of the rain. The study interprets the episode as the interaction of persistent moisture, favorable upper-level circulation, low-level saturation, frontal forcing and local orographic modulation, rather than as the product of a single mechanism.

The study provides a quantitative local baseline for hydrometeorological monitoring, but its design also establishes important limits. It covers one municipality and 13 years, so it does not establish long-term changes in the frequency of extremes, and a percentile criterion identifies unusually wet days within the observed record rather than a fixed probability of future recurrence. MERGE was used to replace missing gauge hours rather than substitute for the station network; its mean Pearson correlation with gauge records was 0.6. Atmospheric mechanisms were diagnosed from GFS analyses at 0.5-degree resolution, and the derived soundings and fields cannot resolve Petrópolis’s steep terrain, boundary-layer processes or cloud microphysics at neighborhood scale. The authors therefore identify radar observations, satellite analysis and high-resolution numerical modeling as priorities for future work, while the 2013 case demonstrates how gauge-based event definitions and layered atmospheric diagnostics can reveal the distinctive hazards of extreme rainfall in complex tropical terrain.

Subject of Research: Observational and atmospheric-diagnostic analysis of extreme rainfall in Petrópolis, Brazil

Article Title: Extreme rainfall events in a tropical complex-terrain environment: Identification and dynamical–thermodynamic evaluation in Petropolis, Rio de Janeiro (Brazil)

Article References: Miranda, B. P., da Silva Bastos Guimarães, N., & Polifke, F. (2026). Extreme rainfall events in a tropical complex-terrain environment: Identification and dynamical–thermodynamic evaluation in Petropolis, Rio de Janeiro (Brazil). Theoretical and Applied Climatology, 157(11), Article 730. https://doi.org/10.1007/s00704-026-06660-7

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06660-7

Keywords: extreme rainfall, Petrópolis, Brazil, rain gauges, topographic effects, atmospheric moisture, mesoscale convection, precipitation extremes, South Atlantic, weather dynamics, Extreme, rainfall

The study, conducted by Hüdaverdi Gürkan of the University of Florida, the Turkish State Meteorological Service, and Adana Alparslan Turkes Science and Technology University, together with Gerrit Hoogenboom of the University of Florida’s Global Food Systems Institute, set out to answer a deceptively simple question: when NASA POWER says it rained, or reports yesterday’s maximum temperature, how closely does that match what actually happened on the ground? The answer, the researchers found, depends heavily on which variable you care about and where the point of interest sits on the map. For temperature and solar radiation, the news is overwhelmingly positive. For humidity, wind, and precipitation, geography matters enormously, and the dataset’s reliability can degrade sharply along coastlines and in mountainous terrain.

The statistical evidence for temperature is striking. Maximum daily temperature records from NASA POWER explained between 87 and 98 percent of the variance in ground observations, with root mean square errors ranging from 1.50 to 6.89 degrees Celsius. Minimum temperatures performed nearly as well, with R-squared values between 0.78 and 0.97 and errors from 1.23 to 8.37 degrees Celsius. Solar radiation estimates tracked ground measurements with R-squared values from 0.76 to 0.97 and root mean square errors between 1.73 and 3.84 megajoules per square meter. Across all temperature and radiation variables, the study reports overall R-squared values between 0.76 and 0.98, a level of agreement that the authors say establishes NASA POWER as a dependable foundation for climate modeling and agricultural decision support in most settings.

The implications for crop modeling are significant. Crop simulation models, which are used worldwide to forecast yields, evaluate irrigation strategies, and assess climate change impacts, are notoriously sensitive to input weather data. Small biases in daily minimum temperature can alter calculations of growing degree days and chilling requirements, while errors in solar radiation propagate directly into photosynthesis and evapotranspiration estimates. Previous work by Hoogenboom and colleagues, including earlier evaluations of NASA satellite-derived temperature and radiation data over the continental United States, laid the groundwork for using such datasets in agricultural applications. The new study extends that legacy by testing the modern POWER product across a far broader range of topographic and climatic settings, from the humid subtropics of Florida to the complex mountain terrain of Türkiye.

That topographic diversity is precisely where the study’s cautionary findings emerge. The researchers found that the dataset’s performance for relative humidity, wind speed, and precipitation is strongly controlled by local geography. In coastal areas, land-sea breeze circulations and marine atmospheric layers introduce discrepancies that the gridded satellite and reanalysis data struggle to capture. NASA POWER produces values on a grid, essentially averaging conditions over cells of finite size, and these spatial averages smooth over the sharp gradients that occur where land meets ocean. A weather station a few hundred meters from the shoreline can experience humidity and wind regimes that differ substantially from the grid-cell average, producing errors that would surprise a user relying on the dataset without checking it first.

High-relief terrain presents a parallel problem that atmospheric scientists call topographic smoothing. When elevation changes dramatically within a single grid cell, as it does across the mountain ranges of Anatolia or the slopes of Washington State’s Cascades, a single gridded value cannot represent the true spread of conditions from valley floor to ridge crest. The dataset’s estimates regress toward the mean of the cell, underestimating the extremes that stations actually record. This phenomenon, well documented in studies of gridded precipitation in complex terrain, means that hydrologists and agronomists working in mountainous regions should treat NASA POWER outputs for precipitation, humidity, and wind with particular skepticism, or apply bias correction before feeding them into models.

The practical value of the study lies in the regional performance thresholds it establishes. Rather than issuing a blanket verdict on the dataset, the authors provide what amounts to a decision guide: researchers can look up their variable of interest and their terrain type, and determine in advance whether raw NASA POWER data is adequate or whether bias correction is necessary. This distinction matters because bias correction is not free. It requires access to local observations, adds processing steps, and introduces its own uncertainties. Knowing that temperature and radiation can typically be used as-is, while coastal precipitation may demand correction, saves both computational effort and scientific risk. In an era when open-access geospatial datasets are increasingly substituted for sparse station networks, such interoperability benchmarks become essential infrastructure for environmental monitoring.

The validation design itself reflects a growing awareness that dataset quality assessments must span decades, not just single seasons. By drawing on records of 15 to 43 years from networks including the Florida Automated Weather Network, the Georgia Automated Environmental Monitoring Network, Washington State University’s AgWeatherNet, and the Turkish State Meteorological Service, the study captures the full range of interannual variability, El Niño and La Niña cycles, heat waves, and drought years that any operational dataset must handle. A dataset that performs well only in average years is of limited use for the extreme events that matter most to farmers and water managers. The multi-decadal approach also guards against the possibility of agreement arising by coincidence in a short window of unusually stable weather.

The research also situates NASA POWER within a crowded field of gridded climate products, including ERA5, MERRA-2, CRU TS, and CHIRPS, each with its own strengths and known weaknesses. What distinguishes POWER is its accessibility: it is free, requires no registration, and delivers daily agroclimatic variables in formats that plug directly into widely used crop models. The authors’ benchmark gives the global community of agricultural modelers, many of whom work in data-sparse regions of Africa, Central Asia, and South America, a defensible basis for choosing POWER, and a clear-eyed understanding of where it will need help. As climate change intensifies the demand for reliable weather information in agriculture, studies like this one transform a free dataset from a convenient substitute into a rigorously characterized scientific instrument.

Subject of Research: Multi-decadal validation of the NASA POWER gridded meteorological dataset against ground weather stations in the United States and Türkiye for climatology and agricultural modeling applications

Article Title: Validating NASA POWER meteorological data for applied climatology and agricultural modeling: a multi-decadal assessment across diverse topographies in the US and Türkiye

Article References: Gürkan, H., & Hoogenboom, G. (2026). Validating NASA POWER meteorological data for applied climatology and agricultural modeling: a multi-decadal assessment across diverse topographies in the US and Türkiye. Theoretical and Applied Climatology, 157(10), Article 621. https://doi.org/10.1007/s00704-026-06532-0

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06532-0

Keywords: NASA POWER, gridded meteorological data, weather station validation, applied climatology, agricultural modeling, crop simulation, solar radiation, temperature data, precipitation accuracy, topographic effects, bias correction, Türkiye