Imroving Extreme Precipitation Prediction Accuracy: A Novel Probability-Matching-Based Loss Function for Deep Learning Models

In this paper, we explore the prevalent issue of underestimation of extreme precipitation values in deep learning models utilized for precipitation forecasting. We emphasize that this challenge arises from the double penalty phenomenon, which is exacerbated by the joint effect of the commonly adopted mean squared error (MSE) loss function and the intrinsic uncertainty of forecasting tasks. Drawing inspiration from probability-matching ensemble forecasting, we introduce Sort Loss, a straightforward yet highly effective deep learning loss function. By leveraging the ordinal relationships within meteorological data, Sort Loss circumvents the positional information-related double penalty problem. Experimental results from precipitation nowcasting and short-term forecasting tasks demonstrate that Sort Loss effectively diminishes the distributional discrepancies between model forecasts and actual observations. Consequently, it significantly enhances forecasting performance in heavy rainfall scenarios, while simultaneously maintaining stability across other weather conditions. This study offers a novel perspective on optimizing deep learning models for weather forecasting and showcases the potential of applying Sort Loss to improve the accuracy of extreme weather predictions.