# Graph-based Neural Weather Prediction for Limited Area Modeling

arXiv: https://arxiv.org/abs/2309.17370 · HTML: https://mlsanity.com/paper/2309.17370/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 29 Sep 2023.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Fourier Neural Operator for Parametric Partial Differential Equations](https://mlsanity.com/paper/2010.08895/) (2020)
- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [Interaction Networks for Learning about Objects, Relations and Physics](https://mlsanity.com/paper/1612.00222/) (2016)
- [ClimaX: A foundation model for weather and climate](https://mlsanity.com/paper/2301.10343/) (2023)
- [GraphCast: Learning skillful medium-range global weather forecasting](https://mlsanity.com/paper/2212.12794/) (2022)
- [Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers](https://mlsanity.com/paper/2111.13587/) (2021)
- [WeatherBench 2: A benchmark for the next generation of data-driven global weather models](https://mlsanity.com/paper/2308.15560/) (2023)
- [Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere](https://mlsanity.com/paper/2306.03838/) (2023)
- [FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead](https://mlsanity.com/paper/2304.02948/) (2023)
- [Forecasting Global Weather with Graph Neural Networks](https://mlsanity.com/paper/2202.07575/) (2022)
- [MultiScale MeshGraphNets](https://mlsanity.com/paper/2210.00612/) (2022)
- [Simulating Continuum Mechanics with Multi-Scale Graph Neural Networks](https://mlsanity.com/paper/2106.04900/) (2021)
- [AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning](https://mlsanity.com/paper/2308.13280/) (2023)

## Built on by (most cited first, 22 of 22)

- [PyG 2.0: Scalable Learning on Real World Graphs](https://mlsanity.com/paper/2507.16991/) (2025)
- [Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey](https://mlsanity.com/paper/2312.03014/) (2023)
- [Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling](https://mlsanity.com/paper/2408.10958/) (2024)
- [Regional data-driven weather modeling with a global stretched-grid](https://mlsanity.com/paper/2409.02891/) (2024)
- [Diffusion-LAM: Probabilistic Limited Area Weather Forecasting with Diffusion](https://mlsanity.com/paper/2502.07532/) (2025)
- [Local Off-Grid Weather Forecasting with Multi-Modal Earth Observation Data](https://mlsanity.com/paper/2410.12938/) (2024)
- [FireCastNet: Earth-as-a-Graph for Seasonal Fire Prediction](https://mlsanity.com/paper/2502.01550/) (2025)
- [HRRRCast: a data-driven emulator for regional weather forecasting at convection allowing scales](https://mlsanity.com/paper/2507.05658/) (2025)
- [MetMamba: Regional Weather Forecasting with Spatial-Temporal Mamba Model](https://mlsanity.com/paper/2408.06400/) (2024)
- [Universal Diffusion-Based Probabilistic Downscaling](https://mlsanity.com/paper/2602.11893/) (2026)
- [Regional Ocean Forecasting with Hierarchical Graph Neural Networks](https://mlsanity.com/paper/2410.11807/) (2024)
- [A comparison of stretched-grid and limited-area modelling for data-driven regional weather forecasting](https://mlsanity.com/paper/2507.18378/) (2025)
- [Deep Learning Weather Models for Subregional Ocean Forecasting: A Case Study on the Canary Current Upwelling System](https://mlsanity.com/paper/2505.24429/) (2025)
- [Bridging short- and medium-range weather forecasting with machine learning](https://mlsanity.com/paper/2608.26822/) (2026)
- [IndiaWeatherBench: A Dataset and Benchmark for Data-Driven Regional Weather Forecasting over India](https://mlsanity.com/paper/2509.00653/) (2025)
- [Harnessing AI data-driven global weather models for climate attribution: An analysis of the 2017 Oroville Dam extreme atmospheric river](https://mlsanity.com/paper/2409.11605/) (2024)
- [On the use of Graphs for Satellite Image Time Series](https://mlsanity.com/paper/2505.16685/) (2025)
- [Ensemble Graph Neural Networks for Probabilistic Sea Surface Temperature Forecasting via Input Perturbations](https://mlsanity.com/paper/2603.06153/) (2026)
- [From Nowcasting to Forecasting: Adapting a Reanalysis-Trained](https://mlsanity.com/paper/2609.03763/) (2026)
- [A Station-Based Evaluation of Machine Learning-based Weather Forecasting Models in Northern Norway](https://mlsanity.com/paper/2609.10564/) (2026)
- [Varda-single-1.0: deterministic data-driven weather forecasting at 1 km resolution over Switzerland's complex topography](https://mlsanity.com/paper/2610.01835/) (2026)
- [Learning Kilometer-Scale Weather Prediction with Global-Regional Alignment](https://mlsanity.com/paper/2610.12401/) (2026)

## Abstract (arXiv)

The rise of accurate machine learning methods for weather forecasting is creating radical new possibilities for modeling the atmosphere. In the time of climate change, having access to high-resolution forecasts from models like these is also becoming increasingly vital. While most existing Neural Weather Prediction (NeurWP) methods focus on global forecasting, an important question is how these techniques can be applied to limited area modeling. In this work we adapt the graph-based NeurWP approach to the limited area setting and propose a multi-scale hierarchical model extension. Our approach is validated by experiments with a local model for the Nordic region.