摘要
选取河北省2023年7月28~31日极端暴雨事件(简称“23·7”极端暴雨),采用相关系数、多种误差指标以及客观评分指标,综合解析中国区域高时空分辨率多源融合降水近实时实况分析产品(Chinese Meteorological Administration Multi-source Merged Precipitation Analysis System,CMPAS)对河北省“23·7”极端暴雨的表征能力,研究发现:CMPAS融合降水数据对地面观测降水的表征能力较好,能较为准确地刻画地面降水强度、范围以及暴雨中心落区,且降水量级越大、落区越集中,刻画效果越好,但仍存在高估降水低值和低估降水高值的情况;CMPAS融合降水产品在低海拔、地势平坦地区的表征能力更好,随海拔高度的增加,误差也随之增大。总体来说,CMPAS融合降水产品对于大雨及以下量级降水强度、落区以及暴雨中心落区的刻画较为精准。另外,在灾害性天气发生时,对于无法获取观测站点降水资料的地区,CMPAS融合降水产品可作为站点实况的有效补充。
In this study,the“23·7”extreme rainstorm event in Hebei Province is selected as the research object,and correlation coefficients,multiple error indicators,and objective scoring indicators are used to comprehensively analyze the ability of the CMPAS(Chinese Meteorological Administration Multi-source Merged Precipitation Analysis System)to characterize the“23·7”extreme rainstorm using the CMPAS fusion precipitation data on ground observations in near real time.Based on the multisource precipitation analysis system(CMPAS)employed to investigate the“23·7”extreme rainstorm in Hebei Province,this study found that CMPAS fused precipitation data on the surface observation of precipitation have better characterization ability and can more accurately portray the surface and central fallout area of the storm’s precipitation intensity and extent,along with surface rainstorm.Moreover,the larger the precipitation magnitude and more centralized the fallout area,the better the portrayal effect;however,low precipitation values are overestimated,and high precipitation values are underestimated in these data.Furthermore,CMPAS fused precipitation products have better characterization ability in low-altitude and flat-terrain areas,with errors increasing with the altitude.In general,the CMPAS fused precipitation product is more accurate in characterizing the intensity and fallout zone of light to heavy rainfall,as well as the central fallout zone of rainstorms.In addition,during catastrophic weather events,CMPAS fused precipitation products can be used as an effective supplement to ground observation data where station data are sparse.
作者
杨荣芳
幺伦韬
刘文忠
孔祥如
YANG Rongfang;YAO Luntao;LIU Wenzhong;KONG Xiangru(China Meteorological Administration Xiong’an Atmospheric Boundary Layer Key Laboratory,Xiong’an New Area,Hebei Province 071800;Key Laboratory of Meteorology and Ecological Environment of Hebei Province,Shijiazhuang 050021;Hebei Meteorological Technology and Equipment Center,Shijiazhuang 050021)
出处
《气候与环境研究》
CSCD
北大核心
2024年第4期405-418,共14页
Climatic and Environmental Research
基金
中国气象局大气探测重点开放实验室课题2023KLAS02M。
