气候变化领域集成服务门户(CCPortal): A rapid refresh ensemble based data assimilation and forecast system for the RELAMPAGO field campaign

CCPortal

DOI	10.1016/j.atmosres.2021.105858
	A rapid refresh ensemble based data assimilation and forecast system for the RELAMPAGO field campaign
	Dillon M.E.; Maldonado P.; Corrales P.; García Skabar Y.; Ruiz J.; Sacco M.; Cutraro F.; Mingari L.; Matsudo C.; Vidal L.; Rugna M.; Hobouchian M.P.; Salio P.; Nesbitt S.; Saulo C.; Kalnay E.; Miyoshi T.
发表日期	2021
ISSN	0169-8095
卷号	264
英文摘要	This paper describes the lessons learned from the implementation of a regional ensemble data assimilation and forecast system during the intensive observing period of the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign (central Argentina, November–December 2018). This system is based on the coupling of the Weather Research and Forecasting (WRF) model and the Local Ensemble Transform Kalman Filter (LETKF). It combines multiple data sources both global and locally available like high-resolution surface networks, AMDAR data from local aircraft flights, soundings, AIRS retrievals, high-resolution GOES-16 wind estimates, and local radar data. Hourly analyses with grid spacing of 10 km are generated along with warm-start 36-h ensemble-forecasts, which are initialized from the rapid refresh analyses every three hours. A preliminary evaluation shows that a forecast error reduction is achieved due to the assimilated observations. However, cold-start forecasts initialized from the Global Forecasting System Analysis slightly outperform the ones initialized from the regional assimilation system discussed in this paper. The system uses a multi-physics approach, focused on the use of different cumulus and planetary boundary layer schemes allowing us to conduct an evaluation of different model configurations over central Argentina. We found that the best combinations for forecasting surface variables differ from the best ones for forecasting precipitation, and that differences among the schemes tend to dominate the forecast ensemble spread for variables like precipitation. Lessons learned from this experimental system are part of the legacy of the RELAMPAGO field campaign for the development of advanced operational data assimilation systems in South America. © 2021
英文关键词	Regional data assimilation; Regional ensemble forecasts; RELAMPAGO
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/236551
作者单位	CONICET (Consejo Nacional de Investigaciones Científicas y Técnicas), Argentina; Servicio Meteorológico Nacional de, Argentina; Departamento de Ciencias de la Atmósfera y los Océanos, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina; Centro de Investigaciones del Mar y la Atmósfera (CONICET-UBA), Argentina; Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos (UMI-3351 IFAECI/CNRS-CONICET-UBA), Argentina; Barcelona Supercomputing Center, Spain; Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, United States; Department of Atmospheric and Oceanic Sciences, University of Maryland, United States; RIKEN Center for Computational Science, Japan
推荐引用方式 GB/T 7714	Dillon M.E.,Maldonado P.,Corrales P.,et al. A rapid refresh ensemble based data assimilation and forecast system for the RELAMPAGO field campaign[J],2021,264.
APA	Dillon M.E..,Maldonado P..,Corrales P..,García Skabar Y..,Ruiz J..,...&Miyoshi T..(2021).A rapid refresh ensemble based data assimilation and forecast system for the RELAMPAGO field campaign.Atmospheric Research,264.
MLA	Dillon M.E.,et al."A rapid refresh ensemble based data assimilation and forecast system for the RELAMPAGO field campaign".Atmospheric Research 264(2021).