Performance of the ECMWF in air temperature and precipitation estimates in the Brazilian Amazon - de Moraes, José Reinaldo da Silva Cabral [UNESP]; Rolim, Glauco de Souza [UNESP]; Martorano, Lucieta Guerreiro; Aparecido, Lucas Eduardo de Oliveira [UNESP]; Bispo, Rafael Carlos; Valeriano, Taynara Tuany Borges [UNESP]; Esteves, João Trevizoli [UNESP] (Springer Nature, 2020)
We evaluated the performance of general atmosphere circulation model (GCM) from the European Center for Medium Range Weather Forecasts (ECMWF) for estimating surface air temperature (T) and precipitation (P) in 55 locations in the Brazilian Amazon. We compared data from surface meteorological stations obtained by the Brazilian Institute of Meteorology (INMET) and ECMWF by linear regression analysis (LRA) using R2 and Willmott et al. (J Geophys Res C5:8995–9005,1985) index (d) as measurement of precision and accuracy, respectively. We applied the Fourier series analysis by extracting the trend and frequency components of P events with noise reduction in the time series. We used the multivariate K-means method to separate weather stations by Groups of Similar Performances (GSPs). The northwest region is characterized as the area with the highest precipitation supply but the lowest performances for T and P, with R2 lower than 0.18. ECMWF tend to overestimate P in dry season and to underestimate in rainy season. The proposed methodology of calibration of P data by the Fourier series was a good tool to predict an extreme event every 5 to 7 months in the region. ECMWF presented high performance (R2 > 0.60) when estimating P in a monthly scale and medium performance (R2 < 0.60) when estimating T in a monthly and 10-day period. The highest concentrations of surface meteorological stations in the eastern/southeastern portion of the Amazon region were decisive in the ECMWF performance expression, indicating an increased meteorological predictability in the anthropic areas, precisely where the agricultural areas of grain were established in the region.