气候变化领域集成服务门户(CCPortal): Impacts of Assimilation Frequency on Ensemble Kalman Filter Data Assimilation and Imbalances

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DOI	10.1029/2020MS002187
	Impacts of Assimilation Frequency on Ensemble Kalman Filter Data Assimilation and Imbalances
	He H.; Lei L.; Whitaker J.S.; Tan Z.-M.
发表日期	2020
ISSN	19422466
卷号	12 期号:10
英文摘要	The ensemble Kalman filter (EnKF) has been widely used in atmosphere, ocean, and land applications. The observing network has been significantly developed, and thus, observations with highly dense temporal resolutions have become available. To better extract information from dense temporal observations, one straightforward strategy is to increase the assimilation frequency. However, more frequent assimilation may exacerbate the model imbalance and result in degraded forecasts. To combat the imbalance caused by ensemble-based data assimilation due to sampling error and covariance localization, three- and four-dimensional incremental analysis update (IAU) were proposed, which gradually introduce the analysis increments into model rather than intermittently updating the state. The trade-off between the assimilation frequency and imbalance is systematically explored here by using an idealized two-layer model and the NOAA GFS. Results from the idealized two-layer model show that increasing assimilation frequency can reduce errors for state variables that are not sensitive to imbalances. For state variable that carries the signal of the external gravity mode and is sensitive to imbalances, increasing assimilation frequency without (with) IAU reduces (increases) errors. Without IAU, more frequent updates result in smaller increments and less insertion noise, while the initialization of IAU cannot effectively mitigate the imbalances with increased assimilation frequency. Results with a low-resolution version of the NOAA GFS demonstrate that increasing assimilation frequency from 6 to 2 h improves the errors and biases of forecasts verified with conventional and radiance observations, although gravity wave noise in the forecast is increased. © 2020. The Authors.
英文关键词	assimilation frequency; assimilation imbalance; ensemble Kalman filter; incremental analysis update
语种	英语
scopus关键词	Economic and social effects; Errors; Forecasting; Data assimilation; Ensemble based data assimilation; Ensemble Kalman Filter; Extract informations; Incremental analysis; Land applications; Straightforward strategy; Temporal resolution; Kalman filters; data assimilation; detection method; gravity wave; Kalman filter; observational method
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/156617
作者单位	Key Laboratory of Mesoscale Severe Weather, Ministry of Education, Nanjing University, Nanjing, China; School of Atmospheric Sciences, Nanjing University, Nanjing, China; NOAA/Earth System Research Laboratory/Physical Sciences Division, Boulder, CO, United States
推荐引用方式 GB/T 7714	He H.,Lei L.,Whitaker J.S.,et al. Impacts of Assimilation Frequency on Ensemble Kalman Filter Data Assimilation and Imbalances[J],2020,12(10).
APA	He H.,Lei L.,Whitaker J.S.,&Tan Z.-M..(2020).Impacts of Assimilation Frequency on Ensemble Kalman Filter Data Assimilation and Imbalances.Journal of Advances in Modeling Earth Systems,12(10).
MLA	He H.,et al."Impacts of Assimilation Frequency on Ensemble Kalman Filter Data Assimilation and Imbalances".Journal of Advances in Modeling Earth Systems 12.10(2020).