Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.06907 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917891098542080 |
|---|---|
| author | Shi, Jimeng Shirali, Azam Jin, Bowen Zhou, Sizhe Hu, Wei Rangaraj, Rahuul Wang, Shaowen Han, Jiawei Wang, Zhaonan Lall, Upmanu Wu, Yanzhao Bobadilla, Leonardo Narasimhan, Giri |
| author_facet | Shi, Jimeng Shirali, Azam Jin, Bowen Zhou, Sizhe Hu, Wei Rangaraj, Rahuul Wang, Shaowen Han, Jiawei Wang, Zhaonan Lall, Upmanu Wu, Yanzhao Bobadilla, Leonardo Narasimhan, Giri |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06907 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Learning and Foundation Models for Weather Prediction: A Survey Shi, Jimeng Shirali, Azam Jin, Bowen Zhou, Sizhe Hu, Wei Rangaraj, Rahuul Wang, Shaowen Han, Jiawei Wang, Zhaonan Lall, Upmanu Wu, Yanzhao Bobadilla, Leonardo Narasimhan, Giri Machine Learning 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. |
| title | Deep Learning and Foundation Models for Weather Prediction: A Survey |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.06907 |