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 generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis. To address the large resolution ratio, different physics involved at different scales and prediction of channels beyond those in the input data, we employ a two-step approach where a UNet predicts the mean and a corrector diffusion (CorrDiff) model predicts the residual. CorrDiff exhibits encouraging skill in bulk MAE and CRPS scores. The predicted spectra and distributions from CorrDiff faithfully recover important power law relationships in the target data. Case studies of coherent weather phenomena show that CorrDiff can help sharpen wind and temperature gradients that co-locate with intense rainfall in cold front, and can help intensify typhoons and synthesize rain band structures. Calibration of model uncertainty remains challenging. The prospect of unifying methods like CorrDiff with coarser resolution global weather models implies a potential for global-to-regional multi-scale machine learning simulation.
@article{arxiv.2309.15214,
title = {Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling},
author = {Morteza Mardani and Noah Brenowitz and Yair Cohen and Jaideep Pathak and Chieh-Yu Chen and Cheng-Chin Liu and Arash Vahdat and Mohammad Amin Nabian and Tao Ge and Akshay Subramaniam and Karthik Kashinath and Jan Kautz and Mike Pritchard},
journal= {arXiv preprint arXiv:2309.15214},
year = {2024}
}