Validation of rainfall data estimated by GPM satellite on Southern Amazon region

Abstract

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 Disaster Mitigation of Slow-Landslide Movement Induced By Rainfall Based on Ps-Insar Method

“…The availability of the TRMM data only to the end of December 2019; thus, the continued data was using the Global Precipitation Measurement (GPM) satellite product. The study of [26] was shown the correlation of GPM data above 0.83; therefore, it possible to use in this work.…”

Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil

“…In contrast, the spatial differences between precipitation estimates by remote sensing and reanalysis, and the measurements at the weather stations can be partly due to differences in the spatial scales represented by these techniques. A rain gauge consists of a point measurement, while the gridded products (i.e., satellites and land surface models or reanalysis) obtain an average value over the analyzed pixel, although there are different adjustments depending on the local characteristics of each region [33,34]. As a result, satellites and reanalysis products have the ability to estimate precipitation events in areas that a rain gauge was unable to record [30].…”

“…As a result, satellites and reanalysis products have the ability to estimate precipitation events in areas that a rain gauge was unable to record [30]. It should be noted that remote sensing products can mistakenly estimate precipitation events due to the thickness and temperature of clouds [34].…”

Influence of El Niño and La Niña phenomena on seasonality of the relative frequency of rainfall in southern Amazonas mesoregion

“…The lack of such studies in the Amazon basin is due to the scarcity and heterogeneity of weather stations, especially regarding data access and the high number of failures (Ronchail et al, 2002;Debortoli et al, 2015;Santos et al, 2019). Data estimated by the Global Land Data Assimilation System (GLDAS) enable studies on the spatiotemporal variation of meteorological variables, as this product was developed from advanced land surface modeling and data assimilation methods (Rodell et al, 2004).…”