A revised forecast method of ECMWF precipitation based on CNN feature extraction

Abstract

The paper proposes a method for correcting the precipitation forecast of the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The method first uses the ECMWF physical quantity field to draw the flow field diagram in the form of weather, and uses it as the input of the convolutional neural network(CNN) and performs feature extraction on it. Secondly, select several characteristic factors that are highly correlated with the forecast object from the many characteristic factors. Finally, the selected characteristic factors and the precipitation forecast factors of the ECMWF model are used as the input factors of the random forest regression model for modeling and forecasting. Through the correction forecast test of the next 24h precipitation at 10 forecast test stations, the results show that the ECMWF precipitation correction forecast method proposed in this paper is 15% less than the forecast method using ECMWF interpolation to the station on the MAE and RMSE indicators. And 17%. At the same time, on the TS scores of rainstorms above 24h, the revised forecast method proposed in this paper has a false alarm rate significantly lower than the forecast method of ECMWF interpolation to the station.