Probabilistic Precipitation Forecasting over East Asia Using Bayesian Model Averaging

Abstract

Bayesian model averaging (BMA) was applied to improve the prediction skill of 1–15-day, 24-h accumulated precipitation over East Asia based on the ensemble prediction system (EPS) outputs of ECMWF, NCEP, and UKMO from the TIGGE datasets. Standard BMA deterministic forecasts were accurate for light-precipitation events but with limited ability for moderate- and heavy-precipitation events. The categorized BMA model based on precipitation categories was proposed to improve the BMA capacity for moderate and heavy precipitation in this study. Results showed that the categorized BMA deterministic forecasts were superior to the standard one, especially for moderate and heavy precipitation. The categorized BMA also provided a better calibrated probability of precipitation and a sharper prediction probability density function than the standard one and the raw ensembles. Moreover, BMA forecasts based on multimodel EPSs outperformed those based on a single-model EPS for all lead times. Comparisons between the two BMA models, logistic regression, and raw ensemble forecasts for probabilistic precipitation forecasts illustrated that the categorized BMA method performed best. For 10–15-day extended-range probabilistic forecasts, the initial BMA performances were inferior to the climatology forecasts, while they became much better after preprocessing the initial data with the running mean method. With increasing running steps, the BMA model generally had better performance for light to moderate precipitation but had limited ability for heavy precipitation. In general, the categorized BMA methodology combined with the running mean method improved the prediction skill of 1–15-day, 24-h accumulated precipitation over East Asia.