气候变化领域集成服务门户(CCPortal): Strongly Coupled Data Assimilation in Multiscale Media: Experiments Using a Quasi-Geostrophic Coupled Model

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DOI	10.1029/2019MS001652
	Strongly Coupled Data Assimilation in Multiscale Media: Experiments Using a Quasi-Geostrophic Coupled Model
	Penny S.G.; Bach E.; Bhargava K.; Chang C.-C.; Da C.; Sun L.; Yoshida T.
发表日期	2019
ISSN	19422466
起始页码	1803
结束页码	1829
卷号	11 期号:6
英文摘要	Strongly coupled data assimilation (SCDA) views the Earth as one unified system. This allows observations to have an instantaneous impact across boundaries such as the air-sea interface when estimating the state of each individual component. Operational prediction centers are moving toward Earth system modeling for all forecast timescales, ranging from days to months. However, there have been few studies that examine fundamental aspects of SCDA and the transition from traditional approaches that apply data assimilation only to a single component, whether forecasts were derived from a coupled model or an uncoupled forced model. The SCDA approach is examined here in detail using numerical experiments with a simple coupled atmosphere-ocean quasi-geostrophic model. The impact of coupling is explored with respect to its impact on the Lyapunov spectrum and on data assimilation system stability. Different data assimilation methods are compared within the context of SCDA, including the 3-D and 4-D Variational methods, the ensemble Kalman filter, and the hybrid gain method. The impact of observing system coverage is also investigated. We find that SCDA is generally superior to weakly coupled or uncoupled approaches. Dynamically defined background error covariance estimates are essential for SCDA to achieve an accurate coupled state estimate as the observing system becomes sparser. As a clarification of seemingly contradictory findings from previous studies, it is shown that ocean observations can adequately constrain atmospheric state estimates provided that the analysis-observing frequency is sufficiently high and the ensemble size determining the background error covariance is sufficiently large. ©2019. The Authors.
英文关键词	4D-Var; coupled data assimilation; coupled model; ensemble Kalman filter; hybrid gain; strongly coupled data assimilation
语种	英语
scopus关键词	Astrophysics; Forecasting; Interface states; State estimation; System stability; 4D-Var; Coupled modeling; Data assimilation; Ensemble Kalman Filter; hybrid gain; Kalman filters; air-sea interaction; atmospheric modeling; coupling; data assimilation; error analysis; experimental study; Kalman filter; quasi-geostrophic flow; three-dimensional modeling
来源期刊	Journal of Advances in Modeling Earth Systems
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156892
作者单位	Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, United States; NOAA National Centers for Environmental Prediction, College Park, MD, United States; RIKEN Center for Computational Science, Kobe, Japan; Institute for Physical Science and Technology, University of Maryland, College Park, MD, United States; Climate Prediction Division, Japan Meteorological Agency, Tokyo, Japan
推荐引用方式 GB/T 7714	Penny S.G.,Bach E.,Bhargava K.,et al. Strongly Coupled Data Assimilation in Multiscale Media: Experiments Using a Quasi-Geostrophic Coupled Model[J],2019,11(6).
APA	Penny S.G..,Bach E..,Bhargava K..,Chang C.-C..,Da C..,...&Yoshida T..(2019).Strongly Coupled Data Assimilation in Multiscale Media: Experiments Using a Quasi-Geostrophic Coupled Model.Journal of Advances in Modeling Earth Systems,11(6).
MLA	Penny S.G.,et al."Strongly Coupled Data Assimilation in Multiscale Media: Experiments Using a Quasi-Geostrophic Coupled Model".Journal of Advances in Modeling Earth Systems 11.6(2019).