文献类型：期刊文献

英文题名：Improving Heavy Precipitation Forecast Using Data Assimilation with Ensemble-Based Forecast Sensitivity to Observations Technique: A Case Study

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

机构：[1] School of Atmospheric Sciences, Sun Yat-sen University, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai, China; [2] Southern Marine Science and Engineering Guangdong Laboratory [Zhuhai], Zhuhai, China; [3] College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang, China; [4] National Observation and Research Station of Coastal Ecological Environments in Macao, Macao Environmental Research Institute, Macau University of Science and Technology, 999078, China; [5] Guangdong Meteorological Observatory, China Meteorological Administration Tornado Key Laboratory, Guangdong Meteorological Service, Guangdong, China

年份：2023

外文期刊名：SSRN

收录：EI(收录号：20230227301)

语种：英文

外文关键词：Oceanography - Weather forecasting

外文摘要：To evaluate the role of observational data assimilated in model, the ensemble-based forecast sensitivity to observations (EFSO) technique was employed to explore the sensitive observations in numerical experiments on a heavy rainfall event over South China through three strategies of data assimilation (i.e., without data assimilation, CNTL; with all observation assimilation, CNTL_DA; with specific observation assimilation based on EFSO, SEN_DA). The results showed the Infrared Atmospheric Sounder Interferometer exhibiting the greatest impact, followed by Advanced Microwave Sounding Unit A and surface land reports. The top 10% of observation impact (i.e., positive contribution to the rainfall forecast) was found to be located in the upstream ocean of the low-level winds of the heavy rainfall. SEN_DA exhibited the better precipitation in magnitude and location than CNTL and CNTL_DA via assimilating the top 10% of observations over the upstream ocean. The improvement of prediction combined with EFSO technique was preliminarily explored in synoptic perspective. Compared to CNTL and CNTL_DA, SEN_DA exhibited stronger monsoonal southwesterly and horizontal water vapor transport over the South China Sea, the upstream ocean of the heavy rainfall. Although CNTL, CNTL_DA, and SEN_DA had similar magnitudes of convective instability, the SEN_DA had stronger vertical ascent, favoring heavy coastal rainfall through releasing convective instability. Moreover, SEN_DA exhibited a performance of near-surface horizontal mass convergence superposed horizontal mass divergence at mid-to-low levels. Such collectively vertical configuration of horizontal mass divergence potentially promoted the coastal ascent. These results help to optimize the future strategy of observations and data assimilation over South China. ? 2023, The Authors. All rights reserved.