Application of a weighted ensembleforecasting method based on online learningin subseasonal forecast in the South China

Under the proposal of seamless forecasting, it has become a key problem for meteorologists to improve the skillsof subseasonal forecasts. Since the launch of the subseasonal-to-seasonal (S2S) plan by WMO, the precision of modelpredictions has been further developed. However, when we are focusing on the practical applications of modelsin the South China (SC) in recent years, we found that large disagreements appear between forecast members. Someof the members predicted well in this area, while others are not satisfactory. To improve the accuracy of subseasonalforecast in the SC, new methods making full use of different forecast models must be proposed. In this passage, weintroduced a weighted ensemble forecasting method based on online learning (OL) to overcome this difficulty. Asthe state-of-the-art forecast models in the world, three models from China Meteorological Administration, EuropeanCentre for Medium-Range Weather Forecasts and National Centers for Environmental Prediction provided by the S2Sprediction dataset are used as ensemble members, and an ensemble weight is trained through the aforementionedOL model for the predictions of temperature and precipitation in subseasonal timescale in the SC. The results showthat the forecast results produced under the OL method are better than the original model predictions. Comparedwith the three model ensemble results, the weighted ensemble model has a good ability in depicting the temperatureand precipitation in the SC. Furthermore, we also compared this strategy against the climatology predictionsand found out that the weighted ensemble model is superior in 1030 days. Thus, the weighted ensemble methodtrained thorough OL may shed light on improving the skill of subseasonal forecasts.