文献类型：期刊文献

英文题名：Quantifying the impact of assimilating sensitive observations on heavy precipitation forecast with ensemble-based forecast sensitivity to observations technique

作者：Huang, Lingdong[1,2];Zhang, Yu[3];Wang, Donghai[1,2,4];Zeng, Zhilin[5,6]

机构：[1]Sun Yat Sen Univ, Sch Atmospher Sci, Guangdong Prov Key Lab Climate Change, Key Lab Trop Atmosphere Ocean Syst,Minist Educ, Zhuhai 519082, Peoples R China;[2]Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai, Peoples R China;[3]Guangdong Ocean Univ, Coll Ocean & Meteorol, Zhanjiang, Peoples R China;[4]Macau Univ Sci & Technol, Macao Environm Res Inst, Natl Observat & Res Stn Coastal Ecol Environm Maca, Macau 999078, Peoples R China;[5]Guangdong Meteorol Serv, Guangdong Meteorol Observ, Guangzhou, Peoples R China;[6]Guangdong Meteorol Serv, China Meteorol Adm Tornado Key Lab, Guangzhou, Peoples R China

年份：2024

卷号：298

外文期刊名：ATMOSPHERIC RESEARCH

收录：SCI-EXPANDED(收录号：WOS:001135572300001)、、EI(收录号：20235015197937)、Scopus(收录号：2-s2.0-85179136697)、WOS

基金：This work was supported and funded by Guangdong Major Project of Basic and Applied Basic Research (2020B0301030004) , National Key R & D Program of China (2019YFC1510400) , and Foshan Special Project on Science and Technology in Social Field (2120001008761) . We thank the three reviewers for their comments that improved this article.

语种：英文

外文关键词：Data assimilation; EFSO; Sensitive observation

外文摘要：In this study, the Ensemble-based Forecast Sensitivity to Observations (EFSO) technique was used to examine the impact of assimilated sensitive observations on the forecast of heavy rainfall through three data assimilation strategies, i.e., running without observation assimilation (CNTL); with assimilation of all observations (CNTL_DA); with assimilation of sensitive observations (SEN_DA). The Infrared Atmospheric Sounder Interferometer presented the largest positive impact on the forecast, followed by the Advanced Microwave Sounding Unit A and surface land reports. The top 10% of observations contributing the largest positive impact on the forecast were located in the upstream ocean of the low-level winds associated with precipitation maximum. Compared to ensemble CNTL and CNTL_DA experiments, ensemble SEN_DA exhibited better precipitation probability, in which the representative member in ensemble SEN_DA successfully predicted the precipitation magnitude and location. Based upon the representative experiments of the ensemble CNTL, CNTL_DA, and SEN_DA, synoptic conditions were examined to understand how the SEN_DA improves the forecast of heavy precipitation. The three representative experiments exhibited similar characteristics and variations in large-scale circulation, horizontal water vapor transport, and wind fields. However, compared to the representative experiments of CNTL and CNTL_DA, the advection and barotropic components of potential divergence contributed to the increase and persistence of convective instability throughout heavy rainfall in SEN_DA. Heavy precipitation was closely associated with the intensity, duration, and location of the maximum convective instability. Furthermore, the release of convective instability depended on the mesoscale ascent, which determined the maximum and location of the precipitation. These results help to understand the optimization of observational design and assimilation strategy.