SwinRDM: Integrate SwinRNN with Diffusion Model towards High-Resolution
  and High-Quality Weather Forecasting
Lei Chen Fei Du Yuan Hu Fan Wang Zhibin Wang
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.