Este trabalho apresenta uma contribuição aos aspectos da climatologia dinâmica da Amazônia oriental, baseado na análise observacional das séries históricas de 40 anos (1975 a 2014) dos dados de precipitação para 30 estações meteorológicas dos estados do Pará, Maranhão e Tocantins. Os resultados mostraram que os padrões climatológicos de precipitação nos regimes chuvoso (janeiro a maio) e seco (julho a novembro) apresentam variabilidade espacial pronunciada, com características climáticas diferenciadas (intensidade e disposição geográfica dos máximos e mínimos pluviométricos) entre os dois regimes sazonais. Os dados de precipitação do satélite TRMM indicaram que a ZCIT e a ZCAS são os principais sistemas meteorológicos do regime chuvoso, enquanto que os aglomerados convectivos, linhas de instabilidade e influências das bandas frontais posicionadas no nordeste brasileiro são os principais sistemas que explicam a distribuição pluviométrica do regime seco. A análise de tendências usando o teste de Mann-Kendall para o regime chuvoso demonstrou sinais de aumento sistemático de precipitação particularmente no oeste, centro e nordeste do Pará e norte do Tocantins. Para o regime seco, há sinais de tendências positivas significantes no oeste/sudeste do Pará, contudo ressaltam-se os padrões de neutralidade (sem tendências) em grande parte do Maranhão e alguns setores do norte/nordeste do Pará.

Monitoring rainfall in the Brazilian Legal Amazon (BLA), which comprises most of the largest tropical rainforest and largest river basin on the planet, is extremely important but challenging. The size of the area and land cover alone impose difficulties on the operation of a rain gauge network. Given this, we aimed to evaluate the performance of nine databases that estimate rainfall in the BLA, four from gridded analyses based on pluviometry (Xavier, CPC, GPCC and CRU), four based on remote sensing (CHIRPS, IMERG, CMORPH and PERSIANN-CDR), and one from reanalysis (ERA5Land). We found that all the bases are efficient in characterizing the average annual cycle of accumulated precipitation in the BLA, but with a predominantly negative bias. Parameters such as Pearson’s correlation (r), root-mean-square error (RMSE) and Taylor diagrams (SDE), applied in a spatial analysis for the entire BLA as well as for six pluviometrically homogeneous regions, showed that, based on a skill ranking, the data from Xavier’s grid analysis, CHIRPS, GPCC and ERA5Land best represent precipitation in the BLA at monthly, seasonal and annual levels. The PERSIANN-CDR data showed intermediate performance, while the IMERG, CMORPH, CRU and CPC data showed the lowest correlations and highest errors, characteristics also captured in the Taylor diagrams. It is hoped that this demonstration of hierarchy based on skill will subsidize climate studies in this region of great relevance in terms of biodiversity, water resources and as an important climate regulator.

The Amazon River Delta-Estuary region presents a scenario of high annual precipitation totals, governed by large- and meso-scale hydrometeorological events. Intense precipitation can lead to significant erosive susceptibility in the region, depending on hydrometeorological influences and different types of land use and cover. This study aimed to assess rainfall erosivity risk in a time series spanning from 1985 to 2022, with the hypothesis that hydrometeorological events, as well as land use and cover, influence this factor. The dynamics of erosivity were evaluated from the perspective of the influence of hydrometeorological events and land use and cover types. Rainfall erosivity (R) ranged from 10078.7 to 13975.16 MJ·mm−1·ha−1·h−1·year−1, classified as very high erosivity in all years of the study period. The erosive potential showed climatic variability according to the positive and negative phases of the Atlantic Meridional Mode (AMM), and the warm (El Niño) and cold (La Niña) phases of the El Niño-Southern Oscillation (ENSO). The R-factor is positively related to natural non-forest formation, agriculture, non-vegetated area, and wetland ecosystems; being influenced by hydrometeorological events and spatially by different types of land use and cover, confirming the hypothesis raised.

Mining is an important branch of the economy in Brazil. Particularly in the eastern Amazon, a large part of the mineral chain spans regions with heterogeneous landscapes characterized by ridges colonized by native rainforests and canga vegetation, where mining activities have developed, rolling hills with extensive pastures and croplands, and restricted urban areas. Regarding operational activities, the mining industry takes place in open-pit, railway and port settings, all of which are naturally exposed to the impacts of adverse weather conditions. This article addressed the importance of meteorological studies in minimizing and managing the rebounding of atmospheric processes in the mining chain from the mines to the ports. The main methods used in the analyzed studies included regional numerical models and statistical modeling adapted for the target area. The results presented herein refer to the development of climate projections focusing on precipitation extremes, seasonal precipitation forecasts, a short-term meteorological forecasting system, a lightning alert system for mining areas and the creation of a hydrometeorological monitoring network. Such results were presented to stakeholders, who can use information derived from scientific research, especially when planning their respective activities on different time scales, including annual, monthly, daily and hourly timescales, from the perspective of process optimization, human safety, community well-being, biodiversity conservation and water resource management.

This research aimed to analyze the relationship between deforestation (DFT) and climatic variables during the rainy (CHU+) and less-rainy (CHU−) seasons in the Tapajós River basin. Data were sourced from multiple institutions, including the Climatic Research Unit (CRU), Center for Weather Forecasts and Climate Studies (CPTEC), PRODES Program (Monitoring of Brazilian Amazon Deforestation Project), National Water Agency (ANA) and National Centers for Environmental Prediction/National Oceanic and Atmospheric Administration (NCEP/NOAA). The study assessed anomalies (ANOM) in maximum temperature (TMAX), minimum temperature (TMIN) and precipitation (PREC) over three years without the occurrence of the El Niño–Southern Oscillation (ENSO) atmospheric–oceanic phenomenon. It also examined areas with higher DFT density using the Kernel methodology and analyzed the correlation between DFT and climatic variables. Additionally, it assessed trends using the Mann–Kendall technique for both climatic and environmental data. The results revealed significant ANOM in TEMP and PREC. In PREC, the highest values of ANOM were negative in CHU+. Regarding temperature, the most significant values were positive ANOM in the south, southwest and northwestern regions of the basin. Concerning DFT density, data showed that the highest concentration was of medium density, primarily along the highways. The most significant correlations were found between DFT and TEMP during the CHU− season in the Middle and Lower Tapajós sub-basins, regions where the forest still exhibits more preserved characteristics. Furthermore, the study identified a positive trend in TEMP and a negative trend in PREC.

O objetivo deste estudo foi avaliar o desempenho de estimadores de precipitação em relação aos dados observados por pluviômetros no leste da Amazônia, abrangendo os estados do Pará, Tocantins e Maranhão no período de janeiro de 2017 a dezembro de 2021. Como referência, foram utilizados dados de precipitação provenientes da Agência Nacional de Águas, do Instituto Nacional de Meteorologia e do Instituto Tecnológico Vale, compondo ao todo 14 postos de medida. As estimativas de precipitação avaliadas foram provenientes dos produtos CHIRPS, CMORPH-HR, CMORPH-BLD, ERA5 e GSMAP, em uma resolução espacial de 0,25° de latitude e longitude. Foram calculadas as médias diária, mensal e anual para comparação subjetiva; e os coeficientes de Nash-Sutcliffe, coeficiente de exatidão, e correlação de Pearson, índice de desempenho e BIAS, para uma análise objetiva. Os resultados mostraram que a maioria dos estimadores subestimaram a precipitação, exceto o CHIRPS. Foi observado que na média mensal a precipitação apresentou melhores estimativas no período seco. Sendo o CMORPH-BLD entre os produtos que apresentou o melhor desempenho baseado nos índices. As diferenças encontradas entre as estimativas de precipitação mostram a importância de avaliações deste tipo, de forma que podem permitir a escolha das estimativas mais adequadas e de forma mais criteriosa em caso de ausência ou falha no conjunto de dados. A topografia e a proximidade do litoral podem ter sido fatores que influenciaram na acurácia das estimativas de precipitação no leste da Amazônia.