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 skills
of subseasonal forecasts. Since the launch of the subseasonal-to-seasonal (S2S) plan by WMO, the precision of model
predictions has been further developed. However, when we are focusing on the practical applications of models
in the South China (SC) in recent years, we found that large disagreements appear between forecast members. Some
of the members predicted well in this area, while others are not satisfactory. To improve the accuracy of subseasonal
forecast in the SC, new methods making full use of different forecast models must be proposed. In this passage, we
introduced a weighted ensemble forecasting method based on online learning (OL) to overcome this difficulty. As
the state-of-the-art forecast models in the world, three models from China Meteorological Administration, European
Centre for Medium-Range Weather Forecasts and National Centers for Environmental Prediction provided by the S2S
prediction dataset are used as ensemble members, and an ensemble weight is trained through the aforementioned
OL model for the predictions of temperature and precipitation in subseasonal timescale in the SC. The results show
that the forecast results produced under the OL method are better than the original model predictions. Compared
with the three model ensemble results, the weighted ensemble model has a good ability in depicting the temperature
and precipitation in the SC. Furthermore, we also compared this strategy against the climatology predictions
and found out that the weighted ensemble model is superior in 10–30 days. Thus, the weighted ensemble method
trained thorough OL may shed light on improving the skill of subseasonal forecasts.