DOI: 10.1002/jgrd.50542
论文题名: Impacts of snow cover fraction data assimilation on modeled energy and moisture budgets
作者: Arsenault K.R. ; Houser P.R. ; De Lannoy G.J.M. ; Dirmeyer P.A.
刊名: Journal of Geophysical Research Atmospheres
ISSN: 21698996
出版年: 2013
卷: 118, 期: 14 起始页码: 7489
结束页码: 7504
语种: 英语
英文关键词: data assimilation
; land surface model
; snow cover
Scopus关键词: Budget control
; Errors
; Kalman filters
; Soil moisture
; Data assimilation
; Ensemble Kalman Filter
; Ensemble perturbation
; Hydrologic budgets
; Hydrological balance
; Hydrological budgets
; Land surface modeling
; Snow covers
; Snow
; data assimilation
; ensemble forecasting
; fractionation
; Kalman filter
; land surface
; moisture content
; numerical model
; one-dimensional modeling
; snow cover
英文摘要: Two data assimilation (DA) methods, a simple rule-based direct insertion (DI) approach and a one-dimensional ensemble Kalman filter (EnKF) method, are evaluated by assimilating snow cover fraction observations into the Community Land surface Model. The ensemble perturbation needed for the EnKF resulted in negative snowpack biases. Therefore, a correction is made to the ensemble bias using an approach that constrains the ensemble forecasts with a single unperturbed deterministic LSM run. This is shown to improve the final snow state analyses. The EnKF method produces slightly better results in higher elevation locations, whereas results indicate that the DI method has a performance advantage in lower elevation regions. In addition, the two DA methods are evaluated in terms of their overall impacts on the other land surface state variables (e.g., soil moisture) and fluxes (e.g., latent heat flux). The EnKF method is shown to have less impact overall than the DI method and causes less distortion of the hydrological budget. However, the land surface model adjusts more slowly to the smaller EnKF increments, which leads to smaller but slightly more persistent moisture budget errors than found with the DI updates. The DI method can remove almost instantly much of the modeled snowpack, but this also allows the model system to quickly revert to hydrological balance for nonsnowpack conditions. Key Points Snow cover assimilated via direct insertion(DI) and ensemble Kalman filter(EnKF) EnKF method performed better in higher elevations and DI in lower elevations Versus DI, EnKF leads to smaller model impacts but more hydrologic budget errors ©2013. American Geophysical Union. All Rights Reserved. 资助项目: NA07OAR4310221
; NNX08AU51G
; NNX08AV05H
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/63538
Appears in Collections: 影响、适应和脆弱性 气候减缓与适应
There are no files associated with this item.
作者单位: SAIC, Inc., Beltsville MD, United States; NASA/Goddard Space Flight Center, Greenbelt, MD 20771, United States; Department of Geography and GeoInformation Science, George Mason University, Fairfax VA, United States; USRA, Columbia MD, United States; Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax VA, United States; Center for Ocean-Land-Atmosphere Studies, Calverton MD, United States
Recommended Citation:
Arsenault K.R.,Houser P.R.,De Lannoy G.J.M.,et al. Impacts of snow cover fraction data assimilation on modeled energy and moisture budgets[J]. Journal of Geophysical Research Atmospheres,2013-01-01,118(14)