PostRainBench: A comprehensive benchmark and a new model for precipitation forecasting

TL;DR

This paper introduces PostRainBench, a comprehensive benchmark for precipitation forecasting, and CAMT, a novel multi-task learning model that significantly outperforms existing methods and NWP in heavy rain prediction.

Contribution

The paper presents a new benchmark dataset and a channel attention-based multi-task learning model for improved precipitation forecasting, especially in heavy rain scenarios.

Findings

CAMT outperforms state-of-the-art methods by up to 26.8% in rain CSI.

Our model surpasses NWP predictions in heavy rain CSI by up to 31.8%.

First deep learning model to outperform NWP in heavy rainfall forecasting.

Abstract

Accurate precipitation forecasting is a vital challenge of societal importance. Though data-driven approaches have emerged as a widely used solution, solely relying on data-driven approaches has limitations in modeling the underlying physics, making accurate predictions difficult. We focus on the Numerical Weather Prediction (NWP) post-processing based precipitation forecasting task to couple Machine Learning techniques with traditional NWP. This task remains challenging due to the imbalanced precipitation data and complex relationships between multiple meteorological variables. To address these limitations, we introduce the \textbf{PostRainBench}, a comprehensive multi-variable NWP post-processing benchmark, and \textbf{CAMT}, a simple yet effective Channel Attention Enhanced Multi-task Learning framework with a specially designed weighted loss function. Extensive experimental results on the proposed benchmark show that our method outperforms state-of-the-art methods by 6.3\%, 4.7\%, and 26.8\% in rain CSI and improvements of 15.6\%, 17.4\%, and 31.8\% over NWP predictions in heavy rain CSI on respective datasets. Most notably, our model is the first deep learning-based method to outperform NWP approaches in heavy rain conditions. These results highlight the potential impact of our model in reducing the severe consequences of extreme rainfall events. Our datasets and code are available at https://github.com/yyyujintang/PostRainBench.