BMA Probabilistic Quantitative Precipitation Forecasting over the Huaihe Basin Using TIGGE Multimodel Ensemble Forecasts

Abstract

Abstract Bayesian model averaging (BMA) probability quantitative precipitation forecast (PQPF) models were established by calibrating their parameters using 1–7-day ensemble forecasts of 24-h accumulated precipitation, and observations from 43 meteorological stations in the Huaihe Basin. Forecasts were provided by four single-center (model) ensemble prediction systems (EPSs) and their multicenter (model) grand ensemble systems, which consider exchangeable members (EGE) in The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE). The four single-center EPSs were from the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environment Prediction (NCEP), and the Met Office (UKMO). Comparisons between the raw ensemble, logistic regression, and BMA for PQPFs suggested that the BMA predictive models performed better than the raw ensemble forecasts and logistic regression. The verification and comparison of five BMA EPSs for PQPFs in the study area showed that the UKMO and ECMWF were a little superior to the NCEP and CMA in general for lead times of 1–7 days for the single-center EPSs. The BMA model for EGE outperformed those for single-center EPSs for all 1–7-day ensemble forecasts, and mostly improved the quality of PQPF. Based on the percentile forecasts from the BMA predictive PDFs for EGE, a heavy-precipitation warning scheme is proposed for the test area.