DOI: 10.1002/2016MS000627
Scopus记录号: 2-s2.0-84971343360
论文题名: Localizing the impact of satellite radiance observations using a global group ensemble filter
作者: Lei L ; , Anderson J ; L ; , Whitaker J ; S
刊名: Journal of Advances in Modeling Earth Systems
ISSN: 19422466
出版年: 2016
卷: 8, 期: 2 起始页码: 719
结束页码: 734
语种: 英语
英文关键词: Bandpass filters
; Carrier concentration
; Errors
; Forecasting
; Kalman filters
; Satellites
; Sounding apparatus
; Weather forecasting
; Background-error covariances
; Data assimilation
; Ensemble Kalman Filter
; Hierarchical ensemble
; Localization
; Localization functions
; Numerical weather prediction
; Radiance observation
; Tracking (position)
英文摘要: Assimilation of satellite radiances has been proven to have positive impacts on the forecast skill, especially for regions with sparse conventional observations. Localization is an essential component to effectively assimilate satellite radiances in ensemble Kalman filters with affordable ensemble sizes. However, localizing the impact of radiance observations is not straightforward, since their location and separation from grid point model variables are not well defined. A global group filter (GGF) is applied here to provide a theoretical estimate of vertical localization functions for radiance observations being assimilated for global numerical weather prediction. As an extension of the hierarchical ensemble filter, the GGF uses groups of climatological ensembles to provide an estimated localization function that reduces the erroneous increments due to ensemble correlation sampling error. Results from an idealized simulation with known background error covariances show that the GGF localization function is superior to the optimal Gaspari and Cohn (GC) localization function. When the GGF is applied to the AMSU-A radiances, it can provide different localization functions for different channels, which indicates the complexity and large computational cost of tuning the localization scales for radiance observations. The GC, GGF, and fitted GGF (FGGF) localization functions are compared using experiments with the NCEP GFS and the NOAA operational EnKF. Verifications relative to the conventional observations, AMSU-A radiances, and the ECMWF analyses show that the GGF and FGGF have smaller errors than GC except in the tropics, and the advantages of the GGF and FGGF persist through 120 h forecast lead time. © 2016. The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75902
Appears in Collections: 影响、适应和脆弱性 气候变化与战略
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作者单位: Key Laboratory of Mesoscale Severe Weather, Ministry of Education, School of Atmospheric Sciences, Nanjing University, Nanjing, China; Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, United States; Physical Sciences Division, NOAA/Earth System Research Laboratory, Boulder, CO, United States; National Center for Atmospheric Research, Boulder, CO, United States
Recommended Citation:
Lei L,, Anderson J,L,et al. Localizing the impact of satellite radiance observations using a global group ensemble filter[J]. Journal of Advances in Modeling Earth Systems,2016-01-01,8(2)