DOI: 10.1002/2015GL063366
论文题名: Optimal ensemble size of ensemble Kalman filter in sequential soil moisture data assimilation
作者: Yin J. ; Zhan X. ; Zheng Y. ; Hain C.R. ; Liu J. ; Fang L.
刊名: Geophysical Research Letters
ISSN: 0094-9522
EISSN: 1944-9253
出版年: 2015
卷: 42, 期: 16 起始页码: 6710
结束页码: 6715
语种: 英语
英文关键词: data assimilation
; ensemble Kalman filter
; ensemble size
Scopus关键词: Balloons
; Kalman filters
; Moisture
; Soil moisture
; Weather forecasting
; Data assimilation
; Ensemble Kalman Filter
; Ensemble size
; Land surface modeling
; Limited sensitivity
; Mathematical derivation
; Maximum Efficiency
; Optimal ensemble
; Soil surveys
; data assimilation
; ensemble forecasting
; Kalman filter
; land surface
; numerical model
; optimization
; soil moisture
; weather forecasting
英文摘要: The ensemble Kalman filter (EnKF) has been extensively applied in sequential soil moisture data assimilation to improve the land surface model performance and in turn weather forecast capability. Usually, the ensemble size of EnKF is determined with limited sensitivity experiments. Thus, the optimal ensemble size may have never been reached. In this work, based on a series of mathematical derivations, we demonstrate that the maximum efficiency of the EnKF for assimilating observations into the models could be reached when the ensemble size is set to 12. Simulation experiments are designed in this study under ensemble size cases 2, 5, 12, 30, 50, 100, and 300 to support the mathematical derivations. All the simulations are conducted from 1 June to 30 September 2012 over southeast USA (from -90°W, 30°N to -80°W, 40°N) at 25 km resolution. We found that the simulations are perfectly consistent with the mathematical derivation. This optical ensemble size may have theoretical implications on the implementation of EnKF in other sequential data assimilation problems. © 2015. The Authors. URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941739439&doi=10.1002%2f2015GL063366&partnerID=40&md5=86915f0357c46ed1827783690576cead
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/9137
Appears in Collections: 科学计划与规划 气候变化与战略
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作者单位: Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, China
Recommended Citation:
Yin J.,Zhan X.,Zheng Y.,et al. Optimal ensemble size of ensemble Kalman filter in sequential soil moisture data assimilation[J]. Geophysical Research Letters,2015-01-01,42(16).