BMA probability quantitative precipitation forecasting of land-falling typhoons in south-east China

Abstract

The probability of quantitative precipitation forecast (PQPF) of three Bayesian Model Averaging (BMA) models based on three raw super ensemble prediction schemes (i. e., A, B, and C) are established, which through calibration of their parameters using 1–3 day precipitation ensemble prediction systems (EPSs) from the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) and observation during land-falling of three typhoons in south-east China in 2013. The comparison of PQPF shows that the performance is better in the BMA than that in raw ensemble forecasts. On average, the mean absolute error (MAE) of 1 day lead time forecast is reduced by 12.4%, and its continuous ranked probability score (CRPS) of 1–3 day lead time forecast is reduced by 26.2%, respectively. Although the amount of precipitation prediction by the BMA tends to be underestimated, but in view of the perspective of probability prediction, the probability of covering the observed precipitation by the effective forecast ranges of the BMA are increased, which is of great significance for the early warning of torrential rain and secondary disasters induced by it.

Keywords

Bayesian model averaging / probabilistic quantitative precipitation forecasting / ensemble prediction / typhoon precipitation

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Acknowledgments

This research was funded by the National Key R&D Program of China (No. 2017YFC1502000), and the Chinese Ministry of Science and Technology Project (No. 2015CB452806), the National Natural Science Foundation of China (Grant No. 41475044) and National Key Technology Research and Development Program of the Ministry of Science and Technology of China (Grant No. 2015BAK10B03). We gratefully acknowledge the anonymous reviewers for spending their valuable time and providing constructive comments and suggestions on this manuscript.

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