气候变化领域集成服务门户(CCPortal): A comparison of variational; ensemble-based; and hybrid data assimilation methods over East Asia for two one-month periods

CCPortal

DOI	10.1016/j.atmosres.2020.105257
	A comparison of variational; ensemble-based; and hybrid data assimilation methods over East Asia for two one-month periods
	Yang E.-G.; Kim H.M.
发表日期	2021
ISSN	0169-8095
卷号	249
英文摘要	In this study, the performances of variational (three-dimensional variational; 3DVAR), ensemble-based (ensemble Kalman filter; EnKF), and hybrid (E3DVAR) data assimilation (DA) methods based on the Advanced Research Weather Research and Forecasting (WRF) model are investigated over East Asia for two one-month period of January and July in 2016. Before a comparison between three methods for two one-month periods, a single observation experiment is conducted to tune and optimize background error covariance depending on each method, so that all methods have similar influence radius. For a comparison between three methods for two one-month period by assimilating conventional observations, the E3DVAR outperforms 3DVAR and EnKF for both two seasons. The 3DVAR outperforms EnKF in January, whereas EnKF outperforms 3DVAR in July. The root mean of difference total energy (RM-DTE) for January increases as a forecast time increases, saturating at the value less than 5 m s−1. On the contrary, RM-DTE in July keeps increasing until 72 h forecast time reaching at the value less than 7 m s−1. Relatively larger moisture error in initial condition for summer season can grow rapidly and change large-scale feature considerably, which can contribute to the continuous growth of RM-DTE in July. Furthermore, rank histogram and spread statistics results confirm that ensemble spreads are represented reasonably for January and July in 2016, although spreads in July are slightly overestimated compared to those in January. In conclusion, the hybrid DA method (E3DVAR) is the most appropriate among three DA methods over East Asia. In addition, for the better performance, it is necessary to tune and optimize the DA system depending on DA method for the given area. © 2020 Elsevier B.V.
英文关键词	East Asia; Ensemble Kalman filter; Hybrid data assimilation; Three-dimensional variational data assimilation
语种	英语
scopus关键词	Earth atmosphere; Meteorology; Advanced researches; Background-error covariances; Data assimilation; Ensemble Kalman Filter; Initial conditions; Mean of difference; Rank histograms; Weather research and forecasting models; Weather forecasting; comparative study; data assimilation; error analysis; Kalman filter; optimization; three-dimensional modeling; Far East
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/162303
作者单位	Atmospheric Predictability and Data Assimilation Laboratory, Department of Atmospheric Science, Yonsei University, Seoul, South Korea
推荐引用方式 GB/T 7714	Yang E.-G.,Kim H.M.. A comparison of variational; ensemble-based; and hybrid data assimilation methods over East Asia for two one-month periods[J],2021,249.
APA	Yang E.-G.,&Kim H.M..(2021).A comparison of variational; ensemble-based; and hybrid data assimilation methods over East Asia for two one-month periods.Atmospheric Research,249.
MLA	Yang E.-G.,et al."A comparison of variational; ensemble-based; and hybrid data assimilation methods over East Asia for two one-month periods".Atmospheric Research 249(2021).