Strategy analysis of the extrapolation adjusted by model prediction (ExAMP) blending scheme for rainfall nowcasting

Chih Chien Tsai, Jia Chyi Liou, Hsin Hao Liao, Yi Chiang Yu, Yu Chun Chen, Chung Yi Lin, Kao Shen Chung, Ben Jong Dao Jou

研究成果: 雜誌貢獻期刊論文同行評審

摘要

The strategies of the extrapolation adjusted by model prediction (ExAMP) blending scheme, which trusts the field pattern predicted by extrapolation and allows the field intensity to be adjusted by numerical weather prediction (NWP), for rainfall nowcasting are analyzed in this study. The McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE) and the Weather Research and Forecasting (WRF) model serve as the extrapolation and NWP models, respectively. Seven 150-min rainfall nowcasting experiments with different strategies are carried out for 37 sampled periods from seven heavy rainfall events in Taiwan in 2019. The results of the overall statistics indicate that, for the extrapolation component, extrapolating the current rainfall rate estimated from the lowest dual-polarimetric radar observations is a superior strategy. The ExAMP scheme that blends the MAPLE and WRF forecasts can surpass both components in 150-min rainfall nowcasting, and an empirical limitation on the innovation of intensity during the blending procedure is found unnecessary in this study. Moreover, the spatial performance for two contrasting events reveals the ability of ExAMP in grasping the rainfall strengthening and weakening in different areas. The skill statistics separately at rainfall strengthening gauges and weakening gauges further prove the effectiveness of ExAMP even though it is effective in intensity correction instead of pattern correction.

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文章編號16
期刊Terrestrial, Atmospheric and Oceanic Sciences
34
發行號1
DOIs
出版狀態已出版 - 12月 2023

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