Abstract
A fundamental challenge in numerical weather prediction is to efficiently produce high-resolution forecasts. A common solution is applying downscaling methods, which include dynamical downscaling and statistical downscaling, to the outputs of global models. This work focuses on statistical downscaling, which establishes statistical relationships between low-resolution and high-resolution historical data using statistical models. Deep learning has emerged as a powerful tool for this task, giving rise to various high-performance super-resolution models, which can be directly applied for downscaling, such as diffusion models and Generative Adversarial Networks. This work relies on a diffusion-based downscaling framework named CorrDiff. In contrast to the original work of CorrDiff, the region considered in this work is nearly 20 times larger, and we not only consider surface variables as in the original work, but also encounter high-level variables (six pressure levels) as target downscaling variables. In addition, a global residual connection is added to improve accuracy. In order to generate the 3km forecasts for the China region, we apply our trained models to the 25km global grid forecasts of CMA-GFS, an operational global model of the China Meteorological Administration (CMA), and SFF, a data-driven deep learning-based weather model developed from Spherical Fourier Neural Operators (SFNO). CMA-MESO, a high-resolution regional model, is chosen as the baseline model. The experimental results demonstrate that the forecasts downscaled by our method generally outperform the direct forecasts of CMA-MESO in terms of MAE for the target variables. Our forecasts of radar composite reflectivity show that CorrDiff, as a generative model, can generate fine-scale details that lead to more realistic predictions compared to the corresponding deterministic regression models.
Abstract (translated)
数值天气预报中的一个基本挑战是高效地生成高分辨率的预测。一种常见的解决方案是采用下采样方法，包括动力学下采样和统计下采样，来处理全球模型输出的数据。本研究专注于统计下采样，即利用统计模型在低分辨率与高分辨率历史数据之间建立统计关系的方法。深度学习作为一种强大的工具，在此任务中得到广泛应用，并催生了各种高性能的超分辨率模型，如扩散模型和生成对抗网络（GAN），可以直接应用于下采样过程。 这项工作基于一个名为CorrDiff的扩散基础下采样框架。与原始的CorrDiff相比，本研究涉及的区域几乎是其20倍大，并且我们不仅考虑了表面变量，还处理到了六个气压层的高度变量作为目标下采样变量。此外，在此模型中添加了一个全局残差连接以提高精度。 为了生成中国地区的3公里预报，我们将训练好的模型应用于中国的气象局（CMA）操作全球模式CMA-GFS和从球面傅立叶神经算子(SFNO)发展的数据驱动深度学习天气模型SFF所给出的25公里全球网格预报。高分辨率区域模型CMA-MESO被选作基线模型。 实验结果表明，通过我们方法下采样的预测通常在目标变量方面（以平均绝对误差(MAE)衡量）优于直接由CMA-MESO生成的预测。我们的雷达综合反射率预报显示，作为生成模型的CorrDiff能够产生更精细的细节，从而比相应的确定性回归模型提供更加真实的预测结果。
URL
https://arxiv.org/abs/2512.05377
