CRPS-LAM: Regional ensemble weather forecasting from matching marginals

• URL: http://arxiv.org/abs/2510.09484v1
• Date: Fri, 10 Oct 2025 15:48:31 GMT
• Title: CRPS-LAM: Regional ensemble weather forecasting from matching marginals
• Authors: Erik Larsson, Joel Oskarsson, Tomas Landelius, Fredrik Lindsten,
• Abstract summary: We introduce CRPS-LAM, a probabilistic LAM forecasting model trained with a CRPS-based objective.<n>By sampling and injecting a single latent noise vector into the model, CRPS-LAM generates ensemble members in a single forward pass.<n>We evaluate the model on the MEPS regional dataset, where CRPS-LAM matches the low errors of diffusion models.
• Score: 12.929305404466747
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Machine learning for weather prediction increasingly relies on ensemble methods to provide probabilistic forecasts. Diffusion-based models have shown strong performance in Limited-Area Modeling (LAM) but remain computationally expensive at sampling time. Building on the success of global weather forecasting models trained based on Continuous Ranked Probability Score (CRPS), we introduce CRPS-LAM, a probabilistic LAM forecasting model trained with a CRPS-based objective. By sampling and injecting a single latent noise vector into the model, CRPS-LAM generates ensemble members in a single forward pass, achieving sampling speeds up to 39 times faster than a diffusion-based model. We evaluate the model on the MEPS regional dataset, where CRPS-LAM matches the low errors of diffusion models. By retaining also fine-scale forecast details, the method stands out as an effective approach for probabilistic regional weather forecasting

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