# Deep Learning and Foundation Models for Weather Prediction: A Survey

arXiv: https://arxiv.org/abs/2501.06907 · HTML: https://mlsanity.com/paper/2501.06907/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 157 of its 341 references that are in the map.
- First posted on arXiv 12 Jan 2025.

## Builds on (most cited first, 30 of 157)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)

## Built on by (most cited first, 16 of 16)

- [Utilizing Strategic Pre-training to Reduce Overfitting: Baguan -- A Pre-trained Weather Forecasting Model](https://mlsanity.com/paper/2505.13873/) (2025)
- [SciVid: Cross-Domain Evaluation of Video Models in Scientific Applications](https://mlsanity.com/paper/2507.03578/) (2025)
- [Retrieval-Augmented Foundation Models for Water Level Prediction in the Everglades](https://mlsanity.com/paper/2508.04888/) (2025)
- [Task-Adaptive Parameter-Efficient Fine-Tuning for Weather Foundation Models](https://mlsanity.com/paper/2509.22020/) (2025)
- [Hierarchical AI-Meteorologist: LLM-Agent System for Multi-Scale and Explainable Weather Forecast Reporting](https://mlsanity.com/paper/2511.23387/) (2025)
- [Breaking the Statistical Similarity Trap in Extreme Convection Detection](https://mlsanity.com/paper/2509.09195/) (2025)
- [DPSformer: A long-tail-aware model for improving heavy rainfall prediction](https://mlsanity.com/paper/2509.25208/) (2025)
- [A Diversity-optimized Deep Ensemble Approach for Accurate Plant Leaf Disease Detection](https://mlsanity.com/paper/2511.16982/) (2025)
- [Real-Time Long Horizon Air Quality Forecasting via Group-Relative Policy Optimization](https://mlsanity.com/paper/2511.22169/) (2025)
- [Target Concept Tuning Improves Extreme Weather Forecasting](https://mlsanity.com/paper/2603.19325/) (2026)
- [Optimizing Multi-Agent Weather Captioning via Text Gradient Descent: A Training-Free Approach with Consensus-Aware Gradient Fusion](https://mlsanity.com/paper/2603.21673/) (2026)
- [Accurate, Efficient, and Explainable Deep Learning Approaches for Environmental Science Problems](https://mlsanity.com/paper/2605.19366/) (2026)
- [Apeliotes: A Diffusion-Based Modeling Framework for km-scale Multi-Level Atmospheric Fields](https://mlsanity.com/paper/2607.17037/) (2026)
- [Geospatial Diffusion-based Evolution Synthesis (GeoDES) for Storm-Centered Weather Augmentation](https://mlsanity.com/paper/2607.19522/) (2026)
- [A Station-Based Evaluation of Machine Learning-based Weather Forecasting Models in Northern Norway](https://mlsanity.com/paper/2609.10564/) (2026)
- [Physics-Guided Flow-Map Matching for Precipitation Nowcasting](https://mlsanity.com/paper/2609.37487/) (2026)

## Abstract (arXiv)

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.