Probabilistic Forecast of the Extended Range Heatwave Over Eastern China - Zhang, Wei; Gao, Jianyun; Lai, Qiaozhen; Chi, Yanzhen; Su, Tonghua (Frontiers in Earth Science, 2022)
Several probabilistic forecast methods for heat wave (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 reforecast are implemented by a defined heat wave index (HWI) which is not only able to reflect the actually occurrence of heat waves, but also to facilitate for 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 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 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 extended-range forecast of HW. Meanwhile, in 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.