Superensemble Approach to S2S Model for Predicting Surface Air Temperature in Summer in East Asia from 2016 to 2020

Abstract

A multimodel superensemble technique was applied to improve the accuracy of a model predicting the subseasonal surface air temperatures in East Asia. Superensemble forecast data based on seven subseasonal-to-seasonal (S2S) forecast models were produced for the initial times from May to August in 2016–2020 and forecast days in weeks 1–4. Superensemble techniques based on the root-mean-square error ensemble mean (SUPR) and the linear regression coefficient ensemble mean (SUPL) were evaluated and compared with the forecast bias-removed ensemble mean (ENS) data. The magnitudes of the forecasting root-mean-square errors (RMSE) of the SUPR and SUPL methods were 4.2% and 13.8% lower, respectively, than that of the ENS method, and the most significant effect was in 2020. The RMSE improvements of SUPR and SUPL were high when the initial date was in May, June, and August, and the forecast date was within weeks 1–4. Applying multimodel superensemble methods effectively reduced the forecasting errors of the S2S models. If weight coefficients based on the characteristics of the models are appropriately applied, the forecasting performance can be significantly improved.