Tm. Crawford et al., Value of incorporating satellite-derived land cover data in MM5/PLACE for simulating surface temperatures, J HYDROMETE, 2(5), 2001, pp. 453-468
The Parameterization for Land-Atmosphere-Cloud Exchange (PLACE) module is u
sed within the Fifth-Generation Pennsylvania State University-National Cent
er for Atmospheric Research Mesoscale Model (MM5) to determine the importan
ce of individual land surface parameters in simulating surface temperatures
. Sensitivity tests indicate that soil moisture and the coverage and thickn
ess of green vegetation [as manifested by the values of fractional green ve
getation coverage (fVEG) and leaf area index (LAI)] have a large effect on
the magnitudes of surface sensible heat fluxes. The combined influence of L
AI and fVEG is larger than the influence of soil moisture on the partitioni
ng of the surface energy budget. Values for fVEG, albedo, and LAI, derived
from 1-km-resolution Advanced Very High Resolution Radiometer data, are ins
erted into PLACE, and changes in model-simulated 1.5-m air temperatures in
Oklahoma during July of 1997 are documented. Use of the land cover data pro
vides a clear improvement in afternoon temperature forecasts when compared
with model runs with monthly climatological values for each land cover type
. However, temperature forecasts from MM5 without PLACE are significantly m
ore accurate than those with PLACE, even when the land cover data are incor
porated into the model. When only the temperature observations above 37 deg
reesC are analyzed, however, the simulations from the high-resolution land
cover dataset with PLACE significantly outperform MM5 without PLACE. Previo
us land surface models have simply used (at best) climatological values of
these crucial land cover parameters. The ability to improve model simulatio
ns of surface energy fluxes and the resultant temperatures in a diagnostic
sense provides promise for future attempts at ingesting satellite-derived l
and cover data into numerical models. These model improvements would likely
be most helpful in predictions of extreme temperature events (during droug
ht or extremely wet conditions) for which current numerical weather predict
ion models often perform poorly. The potential value of real-time land cove
r information for model initialization is substantial.