Subseasonal-to-seasonal (S2S) forecasting plays a significant role in the early warning of extreme weather events and disaster risk prevention, but its predictive capability remains limited. Extreme cold events in East Asia trigger widespread disasters in the form of cold temperatures and snow, causing severe impacts on transportation, energy supply, and public life. However, present S2S models typically have limited predictive ability for extreme cold events beyond two weeks.
A recent study, "High-skill members among subseasonal forecast ensemble of extreme cold events in East Asia", published in Atmospheric and Oceanic Science Letters , used ECMWF (European Centre for Medium-Range Weather Forecasts) and ERA5 (fifth major global reanalysis produced by ECMWF) data to assess the forecasting skill for extreme cold events in East Asia.
The findings reveal that, although the ensemble mean of the ECMWF model has limited forecasting ability for extreme cold events after two weeks, some ensemble members exhibit significantly high forecasting skill. The members with high forecasting skill can accurately predict the rapid change of surface air temperature and the intensity of the minimum temperature during an extreme cold event. This mainly depends on the accurate prediction of the atmospheric circulation situation in Eurasia (sea level pressure and 500-hPa geopotential height).
"Among the ensemble members, at least 10% were always high-skill members offering valuable insights," notes Xinli Liu, author of the paper.
Future efforts should focus on identifying high-skill members using historical analogs or AI and assigning them larger weights in ensemble forecasts.
"In ensemble forecasting, appropriately increasing the number of ensemble members and allocating larger weights to high-skill members will improve the forecasting accuracy and credibility," the corresponding author Jingzhi Su adds, pointing to optimized prediction systems.
