气候变化领域集成服务门户(CCPortal): Lightning data assimilation in the WRF-ARW model for short-term rainfall forecasts of three severe storm cases in Italy

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DOI	10.1016/j.atmosres.2020.105246
	Lightning data assimilation in the WRF-ARW model for short-term rainfall forecasts of three severe storm cases in Italy
	Comellas Prat A.; Federico S.; Torcasio R.C.; Fierro A.O.; Dietrich S.
发表日期	2021
ISSN	0169-8095
卷号	247
英文摘要	This study analyses the impact of total lightning data assimilation on cloud-resolving short-term (3 and 6 h) precipitation forecasts of three heavy rainfall events that occurred recently in Italy by providing an evaluation of forecast skill using statistical scores for 3-hourly thresholds against observational data from a dense rain gauge network. The experiments are performed with two initial and boundary conditions datasets as a sensitivity test. The three rainfall events have been chosen to better represent the convective regime spectrum: from a short-lived and localised thunderstorm to a long-lived and widespread event, along with a case that had elements of both. This analysis illustrates the ability of the lightning data assimilation (LDA) to notably improve the short-term rainfall forecasts with respect to control simulations without LDA. The assimilation of lightning enhances the representation of convection in the model and translates into a better spatiotemporal positioning of the storm systems. The results of the statistical scores reveal that simulations with LDA always improve the probability of detection, particularly for rainfall thresholds exceeding 40 mm/3 h. The false alarm ratio also improves but appears to be more sensitive to the model initial and boundary conditions. Overall, these results show a systematic advantage of the LDA with a 3-h forecast range over 6-h. © 2020 Elsevier B.V.
英文关键词	Heavy rainfall; Lightning data assimilation; Numerical weather prediction; Short term forecast
语种	英语
scopus关键词	Boundary conditions; Lightning; Rain gages; Storms; Weather forecasting; Control simulation; Initial and boundary conditions; Observational data; Precipitation forecast; Probability of detection; Rain gauge networks; Rainfall thresholds; Short-term rainfall forecast; Rain; boundary condition; data assimilation; lightning; numerical method; prediction; rainfall; storm; weather forecasting; Italy
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/141736
作者单位	ISAC-CNR, Strada Prov.le Lecce-Monteroni Km 1.2, Lecce, Italy; ISAC-CNR, Via del Fosso del Cavaliere 100, Rome, Italy; Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, NOAA/OAR/National Severe Storms Laboratory, Norman, OK, United States
推荐引用方式 GB/T 7714	Comellas Prat A.,Federico S.,Torcasio R.C.,et al. Lightning data assimilation in the WRF-ARW model for short-term rainfall forecasts of three severe storm cases in Italy[J],2021,247.
APA	Comellas Prat A.,Federico S.,Torcasio R.C.,Fierro A.O.,&Dietrich S..(2021).Lightning data assimilation in the WRF-ARW model for short-term rainfall forecasts of three severe storm cases in Italy.Atmospheric Research,247.
MLA	Comellas Prat A.,et al."Lightning data assimilation in the WRF-ARW model for short-term rainfall forecasts of three severe storm cases in Italy".Atmospheric Research 247(2021).