Deriving global parameter estimates for the Noah land surface model using FLUXNET and machine learning-Reference-Cited by-同舟云学术

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Deriving global parameter estimates for the Noah land surface model using FLUXNET and machine learning

Published:2016-11-18 Issue:22 Volume:121 Page:
ISSN:2169-897X
Container-title:Journal of Geophysical Research: Atmospheres
language:en
Short-container-title:J. Geophys. Res. Atmos.

Author:

Chaney Nathaniel W.¹,Herman Jonathan D.²,Ek Michael B.³,Wood Eric F.⁴

Affiliation:

1. Program in Atmospheric and Oceanic Sciences Princeton University Princeton New Jersey USA

2. Department of Civil and Environmental Engineering UC Davis California USA

3. EMC/NCEP/NOAA College Park Maryland USA

4. Department of Civil and Environmental Engineering Princeton University Princeton New Jersey USA

Funder

National Oceanic and Atmospheric Administration

Publisher

American Geophysical Union (AGU)

Subject

Space and Planetary Science,Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/2016JD024821

Reference51 articles.

1. Estimation of the Minimum Canopy Resistance for Croplands and Grasslands Using Data from the 2002 International H2O Project

2. 'Breathing' of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems

3. A Model Predicting Stomatal Conductance and its Contribution to the Control of Photosynthesis under Different Environmental Conditions

4. Land-Surface-Atmosphere Coupling in Observations and Models

5. Assessment of the Land Surface and Boundary Layer Models in Two Operational Versions of the NCEP Eta Model Using FIFE Data

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