Accurate Precipitation Forecast by Efficiently Learning from Massive Atmospheric Variables and Unbalanced Distribution

TL;DR

This paper presents a novel precipitation forecasting model that effectively leverages large atmospheric datasets and introduces a specialized loss function to improve accuracy and efficiency in predicting scarce precipitation events.

Contribution

The study introduces a new forecasting model with automatic feature extraction and a WMCE loss function, enhancing prediction accuracy and computational efficiency for precipitation events.

Findings

Model outperforms existing baselines in accuracy and efficiency.

Significantly reduces computational costs compared to previous methods.

Demonstrates robustness across multiple datasets.

Abstract

Short-term (0-24 hours) precipitation forecasting is highly valuable to socioeconomic activities and public safety. However, the highly complex evolution patterns of precipitation events, the extreme imbalance between precipitation and non-precipitation samples, and the inability of existing models to efficiently and effectively utilize large volumes of multi-source atmospheric observation data hinder improvements in precipitation forecasting accuracy and computational efficiency. To address the above challenges, this study developed a novel forecasting model capable of effectively and efficiently utilizing massive atmospheric observations by automatically extracting and iteratively predicting the latent features strongly associated with precipitation evolution. Furthermore, this study introduces a 'WMCE' loss function, designed to accurately discriminate extremely scarce precipitation events while precisely predicting their intensity values. Extensive experiments on two datasets demonstrate that our proposed model substantially and consistently outperforms all prevalent baselines in both accuracy and efficiency. Moreover, the proposed forecasting model substantially lowers the computational cost required to obtain valuable predictions compared to existing approaches, thereby positioning it as a milestone for efficient and practical precipitation forecasting.