A rainfall prediction model based on ERA5 and Elman neural network

Abstract

The heightened frequency and intensity of heavy rainfall, brought about by global climate warming, significantly affect various regions. Rainfall prediction plays a pivotal role in ensuring societal security and fostering development. There has been limited exploration of the impact of input parameters on the accuracy of rainfall predictions. Despite the advantages of Elman neural network models in handling non-linear relationships and spatiotemporal data, their application in weather forecasting is restricted. This paper assesses the accuracy of the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) with a dataset from meteorological stations. It introduces a novel rainfall forecasting model based on the Elman neural network and ERA5 reanalysis data. The study also identifies optimal input parameters through factor correlation analysis. Experimental results showcase the precision and stability of ERA5 across various rainfall conditions. Meteorological parameters, such as Precipitable Water Vapor (PWV), exhibit noticeable correlations with temporal variables and precipitation volume. The seven-factor model, including PWV, Zenith Tropospheric Delay, temperature, relative humidity, day-of-year, and hour of day, outperforms in precision evaluation. It achieves a mean critical success index of 57.06 %, a correct forecast rate of 91.39 %, and a false alarm rate of 39.48 %. This rainfall forecasting model introduces a novel approach and empirical research to enhance predictive accuracy, holding significant implications for the amelioration of meteorological alert systems, risk mitigation, and the safeguarding of life and property.