Articles | Volume 19, issue 5
https://doi.org/10.5194/gmd-19-2197-2026
https://doi.org/10.5194/gmd-19-2197-2026
Model evaluation paper
 | 
17 Mar 2026
Model evaluation paper |  | 17 Mar 2026

Assessing and enhancing Noah-MP land surface modeling over tropical forests using machine learning techniques

Yanyan Cheng, Yaomin Wang, Kalli Furtado, Cenlin He, Fei Chen, Alan D. Ziegler, Song Chen, Matteo Detto, Yuna Mao, Baoxiang Pan, Yoshiko Kosugi, Marryanna Lion, Shoji Noguchi, Satoru Takanashi, Lulie Melling, and Baoqing Zhang

Data sets

Simulation results for "Assessing and enhancing Noah-MP land surface modeling over tropical forests using machine learning techniques" Yaomin Wang and Yanyan Cheng https://doi.org/10.5281/zenodo.18809781

Noah-MP.Cheng Yanyan Cheng https://doi.org/10.5281/zenodo.16780672

Model code and software

Code-for-Assessing-and-enhancing-Noah-MP-over-tropical-forests Yaomin Wang https://github.com/Areoreo/Code-for-Assessing-and-enhancing-Noah-MP-over-tropical-forests

HRLDAS: High Resolution Land Data Assimilation System) Community Model Repository NCAR - National Center for Atmospheric Research https://github.com/NCAR/hrldas

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Short summary
Tropical land surface processes shape the Earth's climate, but models often lack accuracy in the tropics due to limited data for validation. We improved the Noah with Multi-Parameterizations (Noah-MP) land surface model for the tropics using data from forests in Panama and Malaysia. Calibration enhanced simulations of energy and water fluxes, and revealed key vegetation and soil parameters, as well as future directions for model improvement in tropical regions.
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