Open AccessEffects of vegetation and soil moisture on the simulated land surface processes from the coupled WRF/Noah model
Author(s) -
Hong Seungbum,
Lakshmi Venkat,
Small Eric E.,
Chen Fei,
Tewari Mukul,
Manning Kevin W.
Publication year - 2009
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2008jd011249
Subject(s) - weather research and forecasting model , environmental science , sensible heat , data assimilation , moderate resolution imaging spectroradiometer , water content , latent heat , vegetation (pathology) , atmospheric sciences , initialization , moisture , climatology , meteorology , satellite , geology , geography , physics , medicine , geotechnical engineering , pathology , computer science , programming language , astronomy
The coupled Weather Research and Forecasting (WRF) model with the Noah land surface model (Noah LSM) is an attempt of the modeling community to embody the complex interrelationship between land surface and atmosphere into numerical weather or climate prediction. This study describes coupled WRF/Noah model tests to evaluate the model sensitivity and improvement through vegetation fraction (Fg) parameterizations and soil moisture initialization. We utilized the 500 m 8‐day Moderate Resolution Imaging Spectroradiometer reflectance data to derive the model Fg parameter using two different methods: the linear and quadric methods. In addition, combining the Fg quadric method, we initialized soil moisture simulated by High‐Resolution Land Data Assimilation System, which has been developed for providing better soil moisture data in high spatial resolution by National Center for Atmospheric Research. We performed temporal comparisons of the simulated land surface variables: surface temperature (TS), sensible heat flux (SH), ground heat flux (GH), and latent heat flux (LH) to observed data during 2002 International H 2 O Project. Then these results were statistically validated with correlation coefficients and root mean square errors. The results indicate high sensitivity of the coupled model to vegetation fluctuations, showing overestimation of vegetation transpiration and very low variability of GH in highly vegetated area.