Bias correction method based on artificial neural networks for quantitative precipitation forecast

The nowcasting and very short-term prediction system (SisPI, for its acronym in Spanish) is among the tools used by the National Meteorological Service of Cuba, for the quantitative precipitation forecast (QPF). SisPI uses the WRF model as the core of its forecasts and one of the challenges to overcome is to improve the precision of the QPF. With this purpose, in this work we present the results of the application of a bias correction method based on artificial neural networks. The method is applied to the highest resolution domain of SisPI (3km), and the correction is made from the precipitation estimation GPM satellite product. Results shows higher correlation with the artificial neural network model in relation to the values ​​predicted by SisPI (0.76 and 0.34 respectively). The mean square error applying the artificial neural network model is 3.69, improving the performance of SisPI with 6.78. In general, the bias correction has good ability to correct the precipitation forecast provided by SisPI, being less evident in cases where precipitation is reported and SisPI is not capable of forecasting it. In cases of overestimation by SisPI (which happens quite frequently), the correction achieves the best results.