DOI: 10.1002/2016JD024821
论文题名: Deriving global parameter estimates for the Noah land surface model using FLUXNET and machine learning
作者: Chaney N.W. ; Herman J.D. ; Ek M.B. ; Wood E.F.
刊名: Journal of Geophysical Research: Atmospheres
ISSN: 2169897X
出版年: 2016
卷: 121, 期: 22 起始页码: 218
结束页码: 235
语种: 英语
英文关键词: evapotranspiration
; land surface model
; machine learning
Scopus关键词: algorithm
; bare soil
; climate modeling
; eddy covariance
; energy balance
; evapotranspiration
; machine learning
; numerical method
; numerical model
; potential evapotranspiration
; spatial resolution
; surface energy
; weather forecasting
英文摘要: With their origins in numerical weather prediction and climate modeling, land surface models aim to accurately partition the surface energy balance. An overlooked challenge in these schemes is the role of model parameter uncertainty, particularly at unmonitored sites. This study provides global parameter estimates for the Noah land surface model using 85 eddy covariance sites in the global FLUXNET network. The at-site parameters are first calibrated using a Latin Hypercube-based ensemble of the most sensitive parameters, determined by the Sobol method, to be the minimum stomatal resistance (rs,min), the Zilitinkevich empirical constant (Czil), and the bare soil evaporation exponent (fxexp). Calibration leads to an increase in the mean Kling-Gupta Efficiency performance metric from 0.54 to 0.71. These calibrated parameter sets are then related to local environmental characteristics using the Extra-Trees machine learning algorithm. The fitted Extra-Trees model is used to map the optimal parameter sets over the globe at a 5 km spatial resolution. The leave-one-out cross validation of the mapped parameters using the Noah land surface model suggests that there is the potential to skillfully relate calibrated model parameter sets to local environmental characteristics. The results demonstrate the potential to use FLUXNET to tune the parameterizations of surface fluxes in land surface models and to provide improved parameter estimates over the globe. ©2016. American Geophysical Union. All Rights Reserved. 资助项目: NA11OAR4310175
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/62781
Appears in Collections: 影响、适应和脆弱性 气候减缓与适应
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作者单位: Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, United States; Department of Civil and Environmental Engineering, UC, Davis, CA, United States; EMC/NCEP/NOAA, College Park, MD, United States; Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States
Recommended Citation:
Chaney N.W.,Herman J.D.,Ek M.B.,et al. Deriving global parameter estimates for the Noah land surface model using FLUXNET and machine learning[J]. Journal of Geophysical Research: Atmospheres,2016-01-01,121(22)