Title: ResCast: Enhancing Global Medium-range Precipitation Forecasting with Residual Diffusion Model
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Medium-range precipitation forecasting;

Abstract: Machine learning techniques have been successfully applied to global weather forecasting, achieving significant results across various applications. However, existing data-driven machine learning methods struggle to provide accurate medium-range meteorological predictions. As a result, precipitation forecasts regressed from these predictions are less accurate, making reliable medium-range precipitation forecasting difficult. The root causes of these issues are error accumulation in meteorological variable forecasts and a lack of effective variable interaction in the precipitation regression module. In this paper, we propose ResCast, a novel approach to global medium-range precipitation forecasting by combining meteorological residual diffusion modeling and precipitation regression.
The diffusion component consists of (i) the Details Network (DetNet), which captures global features, and (ii) the Multi-Attention U-Net (AttUnet), which generates residuals for meteorological variables to reduce prediction bias. Then, a precipitation regression module quantifies the influence of residual-enhanced meteorological variables on precipitation, improving forecast accuracy. We evaluate our approach on the ERA5, an established dataset from the ECMWF, using comprehensive metrics and compare global medium-range precipitation forecasts against four state-of-the-art baselines (such as ENS, GraphCast, etc.). The results demonstrate the effectiveness and superiority of the proposed framework. The code implementation can be found in https://anonymous.4open.science/r/ResCast-78BD.

TL;DR: We propose ResCast, a novel method for medium-range precipitation forecasting that combines meteorological residual diffusion modeling and precipitation regression, achieving state-of-the-art performance on ERA5 data.