用扩散模型做区域天气概率预测，更准更灵活。
Diffusion-LAM: Probabilistic Limited Area Weather Forecasting with Diffusion
- 基于边界数据条件扩散，生成指定区域的天气预报。
- 在MEPS数据集上实现高精度概率预报，优于传统方法。
- 适合需要精准局部天气预测的场景，如城市防灾。
机器学习方法在天气预报中表现出色，相比传统数值模型具有更快的速度和更高的准确性。早期研究多集中于确定性预测，但领域正转向概率预测以更好捕捉不确定性。大多数现有模型面向全球尺度，针对区域或有限区域的建模工作较少，而这类模型可为特定地区提供更专业化、灵活的建模能力。本文提出Diffusion-LAM，一种基于条件扩散的概率有限区域天气预报模型。通过利用周边区域的边界数据进行条件控制，该模型可在指定区域内生成气象预测。在MEPS有限区域数据集上的实验表明，Diffusion-LAM能够实现高精度的概率预报，展现出在有限区域天气预测中的巨大潜力。
原文摘要 · Abstract (English)
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.
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