The spatial and temporal distribution of precipitation is of great importance for the rain-fed agricultural production and the socioeconomics of Mato Grosso (MT), Brazil. MT has a sparse network of ground rain gauges that limits the effective use of precipitation information for sustainable agricultural production and water resources in the region. Several gridded precipitation products from remote sensing and reanalysis of land surface models are currently available that can enhance the use of such information. However, these products are available at different spatial and temporal resolutions which add some challenges to stakeholders (users) to identify their appropriateness for specific applications (e.g., irrigation requirements, length of growing season, and drought monitoring). Thus, it is necessary to provide an assessment of the reliability of these precipitation estimates. The objective of this work was to compare regional precipitation estimates over MT as provided by the Global Land Data Assimilation (GLDAS), Modern-Era Retrospective Analysis for Research and Applications (MERRA), Tropical Rainfall Measurement Mission (TRMM), Global Precipitation Measurement (GPM), and the Global Precipitation Climatology Project (GPCP) with ground-based measurements. The comparison was conducted for the 2000–2018 period at eleven ground-based weather stations that covered different climate zones in MT using daily, monthly, and annual temporal resolutions. The comparison used the Pearson correlation index–r, Willmott index–d, root mean square error—RMSE, and the Wilks methods. The results showed GPM and GLDAS estimates did not differ significantly with the measured daily, monthly, and annual precipitation. TRMM estimates slightly overestimated daily precipitation by about 4.7% but did not show significant difference on the monthly and annual scales when compared with local measurements. The GPCP underestimated annual precipitation by about 7.1%. MERRA underestimated daily, monthly, and annual precipitation by about 22.9% on average. In general, all products satisfactorily estimated monthly precipitation, and most of them satisfactorily estimated annual precipitation; however, they showed low accuracy when estimating daily precipitation. The TRMM, GPM, GPCP, and GLDAS estimates had the highest performance, from high to low, while MERRA showed the lowest performance. The findings of this study can be used to support the decision-making process in the region in application related to water resources management, sustainability of agriculture production, and drought management.

O município de Humaitá, no sul do Estado do Amazonas, apresenta uma notável taxa de crescimento populacional, o que favorece o desmatamento e, consequentemente, afeta o regime pluviométrico regional. Estudos sobre precipitação na região amazônica são baseados em dados espacial, mensais ou anuais das chuvas. Então, esse trabalho objetivou estudar a frequência relativa horária e a intensidade da precipitação no município de Humaitá-AM. Os dados de precipitação horária foram coletados na estação meteorológica do Instituto Nacional de Meteorologia INMET, localizada no Instituto Federal do Amazonas - IFAM (7.55º S e 63.07º W e 54 m). Foi feita a somatória de todos os horários em que houve ocorrências de precipitação registrada pela estação meteorológica no período de julho de 2012 até julho de 2017. A frequência relativa e a variabilidade horária média sazonal foram calculadas pelo método proposto por SPIEGEL. Verificou-se que durante a estação de outono as chuvas são frequentes no período da manhã (03:00hs e 06:00hs), com picos secundários no início da tarde (14:00hs) e mínimas entre 09:00hs e 13:00hs. No inverno às máximas se concentram à tarde (15:00hs e 16:00hs) e mínimas (03:00hs e 04:00hs). Na primavera esses eventos são comuns no decorrer da tarde e parte da noite (14:00hs, 16:00hs e 20:00hs), e são menos constantes entre 10:00hs e 12:00hs. Na estação de verão as chuvas se concentram entre 01:00h e 07:00hs, com pico máximo as 04:00hs e mínima (12:00hs). Notou-se ainda que no período chuvoso foram registradas máximas (06:00hs e 07:00hs), e mínimas (10:00hs e 12:00hs). No período seco as chuvas predominam (06:00hs e 08:00hs) e mínimas (09:00hs, 12:00hs e 16:00hs). A intensidade das chuvas entre as estações do ano é, na maioria, classificada como fraca, seguido por eventos de categoria moderada e forte. Verificou-se que eventos com intensidade muito forte ocorreram apenas durante a estação da primavera. Por fim, a precipitação demonstrou ser mais comum durante a manhã no outono e verão e mais frequentes no período da tarde na primavera e inverno.

Rainfall is a meteorological variable of great importance for hydric balance and for weather studies. Rainfall estimation, carried out by satellites, has increased the climatological dataset related to precipitation. However, the accuracy of these data is questionable. This paper aimed to validate the estimates done by the Global Precipitation Measurement (GPM) satellite for the mesoregion of Southern Amazonas State, Brazil. The surface data were collected by the National Water Agency – ANA and National Institute of Meteorology – INMET, and is available at both institutions’ websites. The satellite precipitation data were accessed directly from the NASA webpage. Statistical analysis of Pearson correlation was used, as well as the Willmott’s “d” index and errors from the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error). The GPM satellite satisfactorily estimated the precipitation, once it had correlations above 73% and high Willmott coefficients (between 0.86 and 0.97). The MAE and RMSE showed values that varied from 36.50 mm to 72.49 mm and 13.81 mm to 71.76 mm, respectively. Seasonal rain variations are represented accordingly. In some cases, either an underestimation or an overestimation of the rain data was observed. In the yearly totals, a high rate of similarity between the estimated and measured values was observed. We concluded that the GPM-based multi-satellite precipitation estimates can be used, even though they are not 100% reliable. However, adjustments in calibration for the region are necessary and recommended.

The global and Amazon climate change, mainly in rainfall, due to the El Niño and La Ninã phenomena. The objective of this study was to analyze the relative frequency (RF) of rainfall, in different periods of the day, during occurrences of El Niño, La Niña events and neutrality condition. The research was carried out in the Southern mesoregion of the state of Amazonas (Apuí, Boca do Acre, Lábrea, Manicoré and Humaitá). Data were analyzed by the Spiegel´s method. The rainfall data were obtained from the Global Land Data Assimilation database from January 1st, 2000 to December 31st, 2018. Data were made available every three hours and integrated into six hours. RF of rainfall was higher in the afternoon in La Niña, Neutro and El Niño years in all municipalities. The RF in La Niña year was higher than during El Niño. The RF during the Neutral year was higher than during El Niño in Apuí and Boca do Acre, and had no difference in Lábrea, Manicoré and Humaitá. The rainfall in the southern Amazon mesoregion was more frequent during the afternoon.