Deep Learning and Foundation Models for Weather Prediction: A Survey

TL;DR

This survey reviews recent deep learning and foundation models for weather prediction, highlighting their architectures, challenges, applications, and open-source resources, aiming to advance practical and trustworthy AI-driven meteorology.

Contribution

It provides a comprehensive taxonomy and analysis of DL and foundation models in weather prediction, including challenges, insights, and future research directions.

Findings

Deep learning models often outperform traditional physics-based methods.

A taxonomy classifies models into deterministic, probabilistic, and pre-trained paradigms.

Open-source datasets and code repositories facilitate practical adoption.

Abstract

Physics-based numerical models have been the bedrock of atmospheric sciences for decades, offering robust solutions but often at the cost of significant computational resources. Deep learning (DL) models have emerged as powerful tools in meteorology, capable of analyzing complex weather and climate data by learning intricate dependencies and providing rapid predictions once trained. While these models demonstrate promising performance in weather prediction, often surpassing traditional physics-based methods, they still face critical challenges. This paper presents a comprehensive survey of recent deep learning and foundation models for weather prediction. We propose a taxonomy to classify existing models based on their training paradigms: deterministic predictive learning, probabilistic generative learning, and pre-training and fine-tuning. For each paradigm, we delve into the underlying model architectures, address major challenges, offer key insights, and propose targeted directions for future research. Furthermore, we explore real-world applications of these methods and provide a curated summary of open-source code repositories and widely used datasets, aiming to bridge research advancements with practical implementations while fostering open and trustworthy scientific practices in adopting cutting-edge artificial intelligence for weather prediction. The related sources are available at https://github.com/JimengShi/ DL-Foundation-Models-Weather.