Online Learning for Subseasonal Forecasting over South China

Since the initiation of the subseasonal-to-seasonal prediction project by the World Meteorological Organization, the accuracy of model forecasts has improved notably. However, substantial discrepancies have been observed among forecast results produced by different ensemble members when applied to South China. To enhance the accuracy of sub-seasonal forecasts in this region, it is essential to develop new methods that can effectively leverage multiple predictive models. This study introduces a weighted ensemble forecasting method based on online learning to improve forecast accuracy. We utilized ensemble forecasts from three models: the Integrated Forecasting System model from the European Centre for Medium-Range Weather Forecasts, the Climate Forecast System Version 2 model from the National Centers for Environmental Prediction, and the Beijing Climate Center-Climate Prediction System version 3 model from the China Meteorological Administration. The ensemble weights are trained using an online learning approach. The results indicate that the forecasts obtained through online learning outperform those of the original dynamical models. Compared to the simple ensemble results of the three models, the weighted ensemble model showed a stronger capability to capture temperature and precipitation patterns in South China. Therefore, this method has the potential to improve the accuracy of sub-seasonal forecasts in this region.