Probabilistic Forecast of the Extended Range Heatwave Over Eastern China

Abstract

Several probabilistic forecast methods for heatwave (HW) in extended-range scales over China are constructed using four models (ECMWF, CMA, UKMO, and NCEP) from the Subseasonal-to-Seasonal (S2S) database. The methods include four single-model ensembles (SME; ECMWF, CMA, UKMO, and NCEP), multi-model ensemble (MME), and Bayesian model averaging (BMA). The construction and verification of reforecasts are implemented by a defined heat wave index (HWI) which is not only able to reflect the actual occurrence of heatwaves, but also to facilitate forecast and verification. The performance is measured by traditional verification method at each grid point of the 105°E to 132°E; 20°N to 45°N domain for the July, August, and September (JAS) of 1999–2010. For deterministic evaluations of HWI forecast, BMA shows a better pattern correlation coefficient than SME and MME and comparable equitable threat score (ETS) with ECMWF and MME. The good performance of ECMWF and MME take advantage of setting the percentile thresholds for forecasting HW. For the probabilistic forecast, the Brier score of BMA is comparable (superior) to that of MME and ECMWF at short (long) lead-time. BMA also demonstrates an improvement on the reliability of probabilistic forecast, indicating that BMA method is a useful tool for an extended-range forecast of HW. Meanwhile, in the real-time extended-range probabilistic forecast, the beginning date, end date, and probability of HW event can be predicted by the HWI probabilistic forecast of BMA.