气候变化领域集成服务门户(CCPortal): A radar reflectivity data assimilation method based on background-dependent hydrometeor retrieval: An observing system simulation experiment

CCPortal

DOI	10.1016/j.atmosres.2020.105022
	A radar reflectivity data assimilation method based on background-dependent hydrometeor retrieval: An observing system simulation experiment
	Chen H.; Chen Y.; Gao J.; Sun T.; Carlin J.T.
发表日期	2020
ISSN	0169-8095
卷号	243
英文摘要	Radar reflectivity contains information about hydrometeors and plays an important role in the initialization of convective-scale numerical weather prediction (NWP). In this study, a new background-dependent hydrometeor retrieval method is proposed and retrieved hydrometeors are assimilated into the Weather Research and Forecasting model (WRF), with the aim of improving short-term severe weather forecasts. Compared to traditional approaches that are mostly empirical and static, the retrieval parameters for hydrometeor identification and reflectivity partitioning in the new scheme are extracted in real-time based on the background hydrometeor fields and observed radar reflectivity. It was found that the contributions of hydrometeors to reflectivity change a lot in different reflectivity ranges and heights, indicating that adaptive parameters are necessary for reflectivity partitioning and hydrometeor retrieval. The accuracy of the background-dependent hydrometeor retrieval method and its impact on the subsequent assimilation and forecast were examined through observing system simulation experiments (OSSEs). Results show that by incorporating the background information, the retrieval accuracy was greatly improved, especially in mixed-hydrometeor regions. The assimilation of retrieved hydrometeors helped improve both the hydrometeor analyses and forecasts. With an hourly update cycling configuration, more accurate hydrometeor information was properly transferred to other model variables, such as temperature and humidity fields through the model integration, leading to an improvement of the short-term (0−3 h) precipitation forecasts. © 2020
英文关键词	Convective-scale numerical weather prediction; Data assimilation; Hydrometeor retrieval; Radar reflectivity
语种	英语
scopus关键词	Clouds; Information retrieval; Radar; Reflection; Search engines; Background information; Numerical weather prediction; Observing system simulation experiments; Precipitation forecast; Radar reflectivities; Temperature and humidities; Traditional approaches; Weather research and forecasting models; Weather forecasting; accuracy assessment; atmospheric convection; data assimilation; hydrometeorology; radar; reflectivity; severe weather; simulation; weather forecasting
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/141872
作者单位	Key Laboratory of Meteorological Disaster of Ministry of Education (KLME), Joint International Research Laboratory of Climate and Environment Change (ILCEC), Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, 210044, China; NOAA/National Severe Storms Laboratory, Norman, OK, United States; Cooperative Institute for Mesoscale Meteorological Studies, NOAA/OAR National Severe Storms Laboratory, University of Oklahoma, Norman, OK, United States
推荐引用方式 GB/T 7714	Chen H.,Chen Y.,Gao J.,et al. A radar reflectivity data assimilation method based on background-dependent hydrometeor retrieval: An observing system simulation experiment[J],2020,243.
APA	Chen H.,Chen Y.,Gao J.,Sun T.,&Carlin J.T..(2020).A radar reflectivity data assimilation method based on background-dependent hydrometeor retrieval: An observing system simulation experiment.Atmospheric Research,243.
MLA	Chen H.,et al."A radar reflectivity data assimilation method based on background-dependent hydrometeor retrieval: An observing system simulation experiment".Atmospheric Research 243(2020).