Data-driven medium-range weather forecasting has attracted much attention in recent years. However, the forecasting accuracy at high resolution is unsatisfactory currently. Pursuing high-resolution and high-quality weather forecasting, we develop a data-driven model SwinRDM which integrates an improved version of SwinRNN with a diffusion model. SwinRDM performs predictions at 0.25-degree resolution and achieves superior forecasting accuracy to IFS (Integrated Forecast System), the state-of-the-art operational NWP model, on representative atmospheric variables including 500 hPa geopotential (Z500), 850 hPa temperature (T850), 2-m temperature (T2M), and total precipitation (TP), at lead times of up to 5 days. We propose to leverage a two-step strategy to achieve high-resolution predictions at 0.25-degree considering the trade-off between computation memory and forecasting accuracy. Recurrent predictions for future atmospheric fields are firstly performed at 1.40625-degree resolution, and then a diffusion-based super-resolution model is leveraged to recover the high spatial resolution and finer-scale atmospheric details. SwinRDM pushes forward the performance and potential of data-driven models for a large margin towards operational applications.

本文介绍了一种基于数据驱动模型SwinRDM的、合成了改进版SwinRNN和扩散模型的高分辨率天气预测方法，它在500 hPa高度位势（Z500）、850 hPa温度（T850）、2-m温度（T2M）和总降水（TP）等关键气象变量上的预测准确率超过了目前的NWP模型，可达到0.25度的空间分辨率，为将数据驱动的预测模型推向操作应用提供了重要参考。

SwinRDM:将SwinRNN与扩散模型结合，实现高分辨率、高质量的天气预测