Open AccessPrecipitation forecast over China for different thresholds using the multimodel bias-removed ensemble mean
Author(s) -
Yang Lv,
Xiefei Zhi,
Shoupeng Zhu
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/675/1/012053
Subject(s) - precipitation , environmental science , climatology , ensemble average , statistics , mathematics , meteorology , geography , geology
Based on the daily accumulated precipitation data obtained from the ensemble forecasts by three meteorological agencies and the CMORPH observational data, the experiments of bias-removed ensemble mean (BREM) towards classified samples of different precipitation thresholds are carried out with results as follows: (1) The Classified BREM (CBREM) is characterized by higher skill in precipitation forecast in contrast to BREM. Most visible improvements can be observed for light precipitation, but there is a negative impact for a moderate threshold. (2) The CBREM after choosing optimal grading thresholds for each grid point further improves forecast skill of precipitation, which shows greatest advancement for moderate precipitation with the threat score improving percentage of over 20% on average.