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Deriving global parameter estimates for the Noah land surface model using FLUXNET and machine learning
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2016
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Source: Journal of Geophysical Research: Atmospheres, 121(22)
[PDF-4.00 MB]
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Journal Title:Journal of Geophysical Research: Atmospheres
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Description: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 modelparameteruncertainty,particularly at unmonitoredsites.This study providesglobal parameterestimatesfor 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 ( r s,min), the Zilitinkevich empirical constant ( Czil), and the bare soil evaporation exponent ( fxexp). Calibration leads to an increase in the mean
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Source:Journal of Geophysical Research: Atmospheres, 121(22)
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ISSN:2169-897X;2169-8996;
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