Statistical downscaling in the TRMM satellite rainfall estimates for the Goias state and the Federal District, Brazil

Rainfall is a fundamental component of agricultural production, and knowing its potential and variability can ensure the success of this activity. However, the number of meteorological stations is still small, even in states with agricultural aptitude, such as Goias. Geoprocessing techniques can be used to overcome this problem. Thus, this study aimed to evaluate the products of the Tropical Rainfall Measuring Mission (TRMM) satellite to describe the annual and monthly rainfall variability in the Goias state and the Federal District (Brazil). Interpolations were carried out to increase the spatial resolution by means of ordinary kriging and cluster analysis for spatial and temporal distribution. It was observed that the evaluated territory can be classified into three regions with differentiated water regimes up to 500 mm annually, with seasonality of accumulated precipitation from November to March. Even though the regression evaluation showed limitations for a monthly precipitation above 200 mm, the analysis of the TRMM satellite products demonstrated that this tool allows forecasts of provisional normals with a higher spatial resolution than the Brazilian National Institute of Meteorology (INMET) stations network, with known measurement errors for each evaluation period, allowing the data application in forecast models for agricultural planning involving water management.