Correction model for rainfall forecasts using the LSTM with multiple meteorological factors

Abstract

AbstractThe goal of this study was to improve the accuracy of model forecasting, such that forecasters could use model products to make more efficient daily weather predictions. Historical data of the 12 hr following a given time for various meteorological factors from the control forecasts of the European Centre for Medium‐Range Weather Forecasting (ECMWF) between 20 ° and 40 ° N latitude and 110 °–130 ° E longitude were used to verify the performance of the proposed method. Eight major meteorological factors were selected via correlation analysis between control forecast meteorological factors and real‐time rainfall. The samples were divided into four types using the K‐means clustered method. Each type was respectively modelled by long short‐term memory (LSTM) in order to correct rainfall forecasts for eastern China. The eight major meteorological factors were used as the model input, and the differences between real‐time rainfall data and model‐forecast rainfall were used as the model output. The corrected results revealed that the root mean square error decreased by 0.65, and the threat scores of light rainfall and rainstorms were improved.