# Building Machine Learning Limited Area Models: Kilometer-Scale Weather Forecasting in Realistic Settings

arXiv: https://arxiv.org/abs/2504.09340 · HTML: https://mlsanity.com/paper/2504.09340/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2025.

## Built on by (most cited first, 13 of 13)

- [High-Resolution Probabilistic Data-Driven Weather Modeling with a Stretched-Grid](https://mlsanity.com/paper/2511.23043/) (2025)
- [HRRRCast: a data-driven emulator for regional weather forecasting at convection allowing scales](https://mlsanity.com/paper/2507.05658/) (2025)
- [STCast: Adaptive Boundary Alignment for Global and Regional Weather Forecasting](https://mlsanity.com/paper/2509.25210/) (2025)
- [Universal Diffusion-Based Probabilistic Downscaling](https://mlsanity.com/paper/2602.11893/) (2026)
- [A comparison of stretched-grid and limited-area modelling for data-driven regional weather forecasting](https://mlsanity.com/paper/2507.18378/) (2025)
- [Finetuning a Weather Foundation Model with Lightweight Decoders for Unseen Physical Processes](https://mlsanity.com/paper/2506.19088/) (2025)
- [Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting](https://mlsanity.com/paper/2605.15470/) (2026)
- [A Station-Based Evaluation of Machine Learning-based Weather Forecasting Models in Northern Norway](https://mlsanity.com/paper/2609.10564/) (2026)
- [Butterfly Effect and the Kinetic Energy Cascade in Probabilistic Machine Learning Weather Prediction Models](https://mlsanity.com/paper/2609.18489/) (2026)
- [West-WRF AI 2-km: High-Resolution Prediction of Integrated Vapor Transport and Precipitation](https://mlsanity.com/paper/2609.25512/) (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)
- [Artificial intelligence pathways from weather to climate](https://mlsanity.com/paper/2610.09770/) (2026)
- [Learning Kilometer-Scale Weather Prediction with Global-Regional Alignment](https://mlsanity.com/paper/2610.12401/) (2026)

## Abstract (arXiv)

Machine learning is revolutionizing global weather forecasting, with models that efficiently produce highly accurate forecasts. Apart from global forecasting there is also a large value in high-resolution regional weather forecasts, focusing on accurate simulations of the atmosphere for a limited area. Initial attempts have been made to use machine learning for such limited area scenarios, but these experiments do not consider realistic forecasting settings and do not investigate the many design choices involved. We present a framework for building kilometer-scale machine learning limited area models with boundary conditions imposed through a flexible boundary forcing method. This enables boundary conditions defined either from reanalysis or operational forecast data. Our approach employs specialized graph constructions with rectangular and triangular meshes, along with multi-step rollout training strategies to improve temporal consistency. We perform systematic evaluation of different design choices, including the boundary width, graph construction and boundary forcing integration. Models are evaluated across both a Danish and a Swiss domain, two regions that exhibit different orographical characteristics. Verification is performed against both gridded analysis data and in-situ observations, including a case study for the storm Ciara in February 2020. Both models achieve skillful predictions across a wide range of variables, with our Swiss model outperforming the numerical weather prediction baseline for key surface variables. With their substantially lower computational cost, our findings demonstrate great potential for machine learning limited area models in the future of regional weather forecasting.