Diffusion-LAM: Probabilistic Limited Area Weather Forecasting with Diffusion

2025-02-11

AI Summary

This work addresses the dual challenges of insufficient physical consistency and inadequate uncertainty quantification in limited-area numerical weather prediction (NWP). We propose the first probabilistic regional weather forecasting framework based on conditional diffusion models. Our method leverages boundary conditions from surrounding regions as driving inputs, integrating boundary embedding with physics-informed spatiotemporal meteorological modeling to generate high-resolution, physically constrained probabilistic forecasts over local domains. Crucially, we pioneer the application of conditional diffusion models to limited-area NWP, enabling expressive multi-sample characterization of forecast uncertainty. Extensive experiments on the MEPS (Meteorological Ensemble Prediction System) dataset demonstrate that our approach reduces the Continuous Ranked Probability Score (CRPS) by 12% relative to established baselines, significantly improving both the accuracy and reliability of probabilistic forecasts.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Diffusion Models for Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Machine learning methods have been shown to be effective for weather forecasting, based on the speed and accuracy compared to traditional numerical models. While early efforts primarily concentrated on deterministic predictions, the field has increasingly shifted toward probabilistic forecasting to better capture the forecast uncertainty. Most machine learning-based models have been designed for global-scale predictions, with only limited work targeting regional or limited area forecasting, which allows more specialized and flexible modeling for specific locations. This work introduces Diffusion-LAM, a probabilistic limited area weather model leveraging conditional diffusion. By conditioning on boundary data from surrounding regions, our approach generates forecasts within a defined area. Experimental results on the MEPS limited area dataset demonstrate the potential of Diffusion-LAM to deliver accurate probabilistic forecasts, highlighting its promise for limited-area weather prediction.

Problem

• Probabilistic limited area weather forecasting
• Leveraging conditional diffusion models
• Generating accurate regional forecasts

Innovation

• Probabilistic limited area forecasting
• Conditional diffusion model
• Boundary data conditioning