| hasData_Center_Short_Name |
|
| hasDataset_Online_Resource |
|
| hasDataset_Title |
- Global Meteorological Forcing Dataset for Land Surface Modeling
|
| hasEntry_ID |
|
| hasReference |
-
Sheffield, J., G. Goteti, and E.F. Wood, 2006: Development of a
50-yr high-resolution global dataset of meteorological forcings for
land surface modeling. J. Climate, 19(13), 3088-3111.
|
| hasSummary |
-
A global dataset of meteorological forcings has been developed that
can be used to drive models of land surface hydrology. The dataset
is constructed by combining a suite of global observation-based
datasets with the NCEP/NCAR reanalysis. Known biases in the
reanalysis precipitation and near-surface meteorology have been
shown to exert an erroneous effect on modeled land surface water and
energy budgets and are thus corrected using observation-based
datasets of precipitation, air temperature and radiation.
Corrections are also made to the rain day statistics of the
reanalysis precipitation which have been found to exhibit a spurious
wave-like pattern in high-latitude wintertime. Wind-induced low
measurement of solid precipitation is removed using the results from
the World Meteorological Organization (WMO) Solid Precipitation
Measurement Intercomparison. Precipitation is disaggregated in space
to 1.0 degree and 0.25 degree by statistical downscaling using
relationships developed with the Global Precipitation Climatology
Project (GPCP) daily product. Disaggregation in time from daily to
3-hourly is accomplished similarly, using the Tropical Rainfall
Measuring Mission (TRMM) 3-hourly real-time dataset. Other
meteorological variables (downward short- and longwave, specific
humidity, surface air pressure and wind speed) are downscaled in
space with account for changes in elevation. The dataset is
evaluated against the bias-corrected forcing dataset of the second
Global Soil Wetness Project. The final product provides a long-term,
globally-consistent dataset of near-surface meteorological variables
that can be used to drive models of the terrestrial hydrologic and
ecological processes for the study of seasonal and interannual
variability and for the evaluation of coupled models and other land
surface prediction schemes.
|
| GCMD Sciencekeywords describing the dataset. Click on Keyword to find similar datasets | |
