Deep learning for precipitation nowcasting: A survey from the perspective of time series forecasting

TL;DR

This survey reviews recent deep learning models for short-term precipitation nowcasting, focusing on their methodologies, evaluation, and challenges to guide future research and improve forecasting accuracy.

Contribution

It provides a comprehensive categorization and analysis of deep learning approaches for precipitation forecasting, highlighting current limitations and future research directions.

Findings

Deep learning models outperform traditional methods in precipitation nowcasting.

Evaluation metrics and preprocessing significantly impact model performance.

Current models face challenges in generalization and real-time application.

Abstract

Deep learning-based time series forecasting has dominated the short-term precipitation forecasting field with the help of its ability to estimate motion flow in high-resolution datasets. The growing interest in precipitation nowcasting offers substantial opportunities for the advancement of current forecasting technologies. Nevertheless, there has been a scarcity of in-depth surveys of time series precipitation forecasting using deep learning. Thus, this paper systemically reviews recent progress in time series precipitation forecasting models. Specifically, we investigate the following key points within background components, covering: i) preprocessing, ii) objective functions, and iii) evaluation metrics. We then categorize forecasting models into \textit{recursive} and \textit{multiple} strategies based on their approaches to predict future frames, investigate the impacts of models using the strategies, and performance assessments. Finally, we evaluate current deep learning-based models for precipitation forecasting on a public benchmark, discuss their limitations and challenges, and present some promising research directions. Our contribution lies in providing insights for a better understanding of time series precipitation forecasting and in aiding the development of robust AI solutions for the future.