Terrestrial Hydrology Research Group
Global Meteorological Forcing Dataset for land surface modeling
This dataset provides near-surface meteorological data for driving land surface models and other terrestrial modeling systems. It blends reanalysis data with observations and disaggregates in time and space. The dataset is currently available at 1.0 degree (plus 0.5 and 0.25 degree), 3-hourly (plus daily and monthly) resolution globally for 1948-2008. Experimental updates include a 1901-2012 version (that will become V2), real-time updates, higher resolution versions for Africa (that assimilates all available gauge data) and future climate projections based on bias-corrected climate model output.
The dataset is freely available but we ask that you leave a few details about yourself and how you intend to use the dataset. Also, please cite the reference below if you use the data. The dataset is updated periodically, and these updates are listed below. The data is best used for research into long-term and broad scale problems, rather than applications for specific locations and/or dates. Data are provided for the oceans as well, but have not been evaluated.
i) extension to 2006;
ii) improved sampling procedure for correction of rain day statistics;
iii) use of latest versions of CRU (TS3.0), SRB (V3.0) and TRMM products;
iv) improved consistency between specific and relative humidity and air temperat ure.
time(1213) has value: 66878.9 time(1216) has value: 66559.87 time(1248) has value: 66340.7 time(1284) has value: 66091.77
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
A global, 50-year, 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 undercatch 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 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 (GSWP-2). 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 inter-annual variability and for the evaluation of coupled models and other land surface prediction schemes.