气象学报（英文版）2021，Vol.35Issue(3)：1-54,54.
基于相似网格点的多源定量降水预报融合算法
Quantitative Precipitation Forecasting Using Multi-Model Blending with Supplemental Grid Points: Experiments and Prospects in China
摘要
Abstract
Quantitative Precipitation Forecast (QPF) is a challenging issue in seamless prediction. QPF faces the following difficulties: (i) single rather than multiple model products are still used; (ⅰ) most QPF methods require long-term training samples not easily available, and (ⅱ) local features are insufficiently reflected. In this work, a multi-model blending (MMB) algorithm with supplemental grid points (SGPs) is experimented to overcome these shortcomings. The MMB algorithm includes three steps: (1) single-model bias-correction, (2) dynamic weight MMB, and (3) light-precipitation elimination. In step 1, quantile mapping (QM) is used and SGPs are configured to expand the sample size. The SGPs are chosen based on similarity of topography, spatial distance, and climatic characteristics of local precipitation. In step 2, the dynamic weight MMB uses the idea of ensemble forecasting: a precipitation process can be forecast if more than 40％ of the models predict such a case; moreover, threat score (TS) is used to update the weights of ensemble members. Finally, in step 3, the number of false alarms of light precipitation is reduced, thus al-leviating unreasonable expansion of the precipitation area caused by the blending of multiple models. Verification results show that using the MMB algorithm has effectively improved the TS and bias score (BS) for blended 6-h QPF. The rate of increase in TS for heavy rainfall (25-mm threshold) reaches 20％?40％; in particular, the improvement has reached 47.6％ for forecast lead time of 24 h, compared with the ECMWF model. Meanwhile, the BS is closer to 1, which is better than any single-model forecast. In sum, the QPF using MMB with SGPs shows great potential to further improve the present operational QPF in China.关键词
多模式融合/相似点/分位映射/弱降水消空Key words
multi-model blending (MMB)/supplemental grid points (SGPs)/quantile mapping (QM)/light-precipit-ation elimination/seamless prediction引用本文复制引用
Yu WANG,Kan DAI,Zhiping ZONG,Yue SHEN,Ruixia ZHAO,Jian TANG,Couhua LIU..基于相似网格点的多源定量降水预报融合算法[J].气象学报（英文版）,2021,35(3):1-54,54.基金项目
Supported by the National Key Research and Development Program of China(2017YFC1502004),Special Project for Forecasters of China Meteorological Administration(CMAYBY2020-162),and Special Project for Forecasters of National Meteorological Center(Y202135). （2017YFC1502004）
