This study aims to analyze impacts of the NESDIS new product of green vegetation fraction (GVF) data on simulated
surface air temperature and surface fluxes over the continental United States (CONUS) using the Nonhydrostatic
Mesoscale Model (NMM) core of the Weather Research and Forecasting (WRF) system, i.e. WRF-NMM, coupled with
the Noah land surface model (LSM). The new global 0.144 by 0.144 degree GVF dataset is an AVHHR-based, near real-time
weekly dataset starting from 1982. It has better quality and a higher temporal resolution than the old monthly GVF
dataset that is currently used in the NOAA operational numerical weather prediction models. The new weekly
climatology GVF data shows a higher percentage of greenness fraction over most US areas than the old dataset, with the
largest differences by 20-40% over the southeast U.S., the northern Middle West, and the west coast of California in
summer. We have performed some case studies over CONUS during July 2006. In general, using the new GVF data
cools predicted surface temperature over most regions compared to the old data, with the largest cooling over regions
with the largest GVF increase. The latent heat increases significantly over most areas while the sensible heat decreases
slightly. These results are physically consistent as more of the net radiation is dissipated in form of latent heat via
enhanced evapotranspiration in response to increasing vegetation cover. Compared with observations, the new GVF
application reduces the WRF-NMM 2-m surface air temperature warm biases, 2-m relative humidity negative biases, and
their RMSEs.
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