Comment on gmd-2022-146

Abstract

As a challenge in the construction of a “seamless forecast” system, improving the prediction skills of subseasonal forecasts is a key issue for meteorologists. In view of the evolution characteristics of numerical models and recent deep learning models for subseasonal forecasts, as forecast times increase, forecast results tend to become intraseasonal low-frequency components, which are essential to the change in general circulation on the subseasonal timescale as well as persistent extreme weather. In this paper, the Global Subseasonal Forecast Model (GSFM v1.0) first extracted the intraseasonal oscillation (ISO) components of atmospheric signals and used an improved deep learning model (SE-ResNet) to train and predict the ISO components of geopotential height at 500 hPa (Z500) and temperature at 850 hPa (T850). The results show that the 10–30 day prediction performance of the model used in this paper is better than that of the model trained directly with original data. Compared with other models/methods, the SE-ResNet model has a good ability to depict the subseasonal evolution of the ISO components of Z500 and T850. In particular, although the CFSv2 results have a better prediction performance through 10 days, the SE-ResNet model is substantially superior to CFSv2 through 10–30 day, especially in the middle and high latitudes. The SE-ResNet model also has a better effect in predicting 3–8 planetary waves, which leads to the difference in model prediction performance in extratropical areas. A case study shows that the SE-ResNet model depicted the phase change and propagation characteristics of planetary waves well. Thus, the application of data- driven subseasonal forecasts of atmospheric ISO components may shed light on improving the skill of seasonal forecasts.