Terrestrial Hydrology Research Group

Princeton University

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.

Data Access

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.

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Jul 13 2014
Dataset updated to 2010 for 1.0-degree versions using CRU TS3.1 monthly precipitation, temperature and DTR up to 2009 and merged with Willmott for 2010.
Nov 16 2012
This is not the updated version of the dataset that was used in the Sheffield et al. (2012) Nature paper. The updated version will be added soon.
Jul 3 2012
Errors in temperature values for Dec2005 and 2007 over the Himalaya region have been corrected. The corresponding shum files have been updated.
Mar 4 2011
Spuriously high values of dswrf, shum and tas have been corrected. The high dswrf values were mainly over high elevations. The spurious shum and tas values were due to very high DTR values in the CRU dataset, especially over Greenland that have been corrected by blending with more robust neighbouring values.
Jun 16 2010
Files for pres, tas, shum, wind for 1948-2006 updated because of corrections to long-term trends and consistency among variables
Apr 2 2010
Full dataset extended from 1948-2006 to 1948-2008 by merging with Willmott precipitation and temperature data, and updated SRB radiation.
Oct 12 2009
Downward longwave values (dlwrf) before 1984 were sometimes unrealistically low and even negative. This has been corrected and all 3hourly, daily and monthly dlwrf files have been updated
Jun 12 2009
Unrealistic downward shortwave values (dswrf) over parts of the Amazon and other isolated points have been corrected. All 3hourly, daily and monthly dswrf files have been updated.
Jun 1 2009
Complete update of the dataset (all files updated), including:
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.
Aug 11 2008
The algorithm for downscaling specific humidity was inconsistent with the downscaled relative humidity. This resulted in some unrealistic values (RH >> 100%). This has been corrected and all shum_3hourly* files have been updated.
Feb 26 2008
The dswrf and dlwrf files were scaled incorrectly resulting in no inter-annual variability. This has been corrected in all dswrf_3hourly* and dlwrf_3hourly* files. The daily and monthly files have been updated as well.
May 18 2007
The time variable in pres_3hourly_1960-1960.nc had some strange values, which have been corrected:
time(1213) has value: 66878.9 time(1216) has value: 66559.87 time(1248) has value: 66340.7 time(1284) has value: 66091.77
Nov 16 2006
A few unrealistically low and high air temperature values occurred over Greenland. These have been corrected and all tas_3hourly* files have been updated.
Nov 15 2006
Some physically unrealistic and spurious downward shortwave values were generated when scaling to match the SRB monthly value s, especially at high latitudes during winter. These have been corrected in all dswrf_3hourly* files.
Nov 13 2006
A second version of the precipitation dataset has been added which is not corrected for gauge undercatch. The file name prefix is "prcp_nuc" (No Undercatch Correction).
Nov 8 2006
A formatting error caused missing data in the air temperature file for 1949 (tas_3hourly_1949-1949.nc, record 274). The file was corrected.


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.


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