The state of the art for physical hazard prediction from weather and climate
requires expensive km-scale numerical simulations driven by coarser resolution
global inputs. Here, a km-scale downscaling diffusion model is presented as a
cost effective alternative. The model is trained from a regional
high-resolution weather model over Taiwan, and conditioned on ERA5 reanalysis
data. To address the downscaling uncertainties, large resolution ratios (25km
to 2km), different physics involved at different scales and predict channels
that are not in the input data, we employ a two-step approach
(\textit{ResDiff}) where a (UNet) regression predicts the mean in the first
step and a diffusion model predicts the residual in the second step.
\textit{ResDiff} exhibits encouraging skill in bulk RMSE and CRPS scores. The
predicted spectra and distributions from ResDiff faithfully recover important
power law relationships regulating damaging wind and rain extremes. Case
studies of coherent weather phenomena reveal appropriate multivariate
relationships reminiscent of learnt physics. This includes the sharp wind and
temperature variations that co-locate with intense rainfall in a cold front,
and the extreme winds and rainfall bands that surround the eyewall of typhoons.
Some evidence of simultaneous bias correction is found. A first attempt at
downscaling directly from an operational global forecast model successfully
retains many of these benefits. The implication is that a new era of fully
end-to-end, global-to-regional machine learning weather prediction is likely
near at hand.

物理风险预测的最新技术需要进行昂贵的千米级数值模拟，而全球输入分辨率较粗。本文提出了一种千米级降尺度扩散模型作为一种经济有效的替代方法，该模型是从台湾的区域高分辨率天气模型经 ERA5 再分析数据训练而来。通过一个两步方法（	extit {ResDiff}），	extit {ResDiff} 在块状 RMSE 和 CRPS 评分上表现出令人鼓舞的技巧。预测的谱和分布能够忠实地恢复调节有害风和降水极端事件的重要幂律关系。研究了一些连贯的天气现象案例，显示了类似于学到的物理学的适当多变量关系，包括与冷锋中的强降雨相伴的尖锐风和温度变化，以及围绕台风眼壁的极端风和降雨带。找到了一些同时进行偏差校正的证据。首次尝试直接从操作全球预报模型进行降尺度成功地保留了许多这些优势。这意味着一个新的全球到区域的端到端机器学习天气预报时代可能即将到来。