LAUSR

Demand for quality weather forecasts has increased in the last decades, leading national meteorological centers to develop new forecasting models. These models have parameterizations which can produce different predictions for the same location and agrometeorological variable. In the state of Paraná Brazil, studies on rain forecasting are important for planning the soybean crop. The objective of this study was to compare, based on a gold-standard and using bootstrapping residuals, forecasts of total rainfall by virtual stations of the following centers: Canadian Meteorological Center (CMC), European Center for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP) and Center for Weather Forecasting and Climate Studies (CPTEC). Gold-standard measurements were obtained from Meteorological System of Paraná (SIMEPAR) meteorological stations. The studied region was the state of Paraná, in October–March of the harvest years 2011/2012–2015/2016; forecast ranges were 24 and 240 hours. Knowledge Discovery in Databases (KDD), focused on data mining techniques, was the chosen methodology. In the data preprocessing stage, spatial and temporal stratification, cleansing and grouping were performed. For the comparisons, 24 h and 240 h weather forecasts were used, being grouped in five-day and ten-day periods, respectively, and coefficients of agreement with the gold-standard measure were calculated. The choice of forecast center should consider the geographic location of a certain pluviometric station, and the temporal range of the forecast, according to its measure of agreement with the gold standard measure. Spatial variations of forecasting centers were identified within the mesoregions, which suggests the employment of different forecasting centers in a certain mesoregion.