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Data-driven medium-range weather forecasting has attracted much attention inrecent years. However, the forecasting accuracy at high resolution isunsatisfactory currently. Pursuing high-resolution and high-quality weatherforecasting, we develop a data-driven model SwinRDM which integrates animproved version of SwinRNN with a diffusion model. SwinRDM performspredictions at 0.25-degree resolution and achieves superior forecastingaccuracy to IFS (Integrated Forecast System), the state-of-the-art operationalNWP model, on representative atmospheric variables including 500 hPageopotential (Z500), 850 hPa temperature (T850), 2-m temperature (T2M), andtotal precipitation (TP), at lead times of up to 5 days. We propose to leveragea two-step strategy to achieve high-resolution predictions at 0.25-degreeconsidering the trade-off between computation memory and forecasting accuracy.Recurrent predictions for future atmospheric fields are firstly performed at1.40625-degree resolution, and then a diffusion-based super-resolution model isleveraged to recover the high spatial resolution and finer-scale atmosphericdetails. SwinRDM pushes forward the performance and potential of data-drivenmodels for a large margin towards operational applications.

SwinRDM: Integrate SwinRNN with Diffusion Model towards High-Resolution and High-Quality Weather Forecasting