Gridded Sub-daily Climate Forcings for North America Based on Daymet and GSWP3 (Daymet-GSWP3)
10.13139/OLCF/2331286To support high spatial and temporal resolution land surface modeling, this dataset provides 3-hourly time step historic weather forcing at 1-km spatial resolution for the entire North America. The latest Daymet V4 data provides gridded historic daily weather observations at 1-km spatial resolution from 1980 to 2014. Using sub-daily temporal information from the Global Soil Wetness Project Phase 3 (GSWP3), Daymet was further temporally downscaled to 3-hourly time steps and provided in the format required for land surface model simulations. The process of temporal downscaling preserves the relative magnitude in each sub-daily time step from GSWP3 while maintaining the total and average values from Daymet for each day. This results in a blended 1980-2014 Daymet-GSWP3 dataset. Available variables include surface air temperature, precipitation, specific humidity, shortwave and longwave radiation, wind speed, and pressure. These data can be used as a high-resolution meteorological forcing dataset to support high-resolution land surface modeling where accurate meteorological forcing datasets built from historic observations and/or reanalysis datasets are desirable.
Published: 2024-05-13 16:16:26 Download DatasetDataset Properties
Field | Value |
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Project Identifier | CLI144 |
Dataset Type | ND Numeric Data |
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Software Needed | Any type of NetCDF readers. |
Originating Organizations | Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States) |
Sponsoring Organizations | Office of Science (SC) Biological and Environmental Research (BER) (SC-23);DOE Biological and Environmental Research Program Energy Exascale Earth System Modeling Program |
DOE Contract | DE-AC05-00OR22725 |
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Acknowledgements
Papers using this dataset are requested to include the following text in their acknowledgements:
*Support for 10.13139/OLCF/2331286 is provided by the U.S. Department of Energy, project CLI144 under Contract DE-AC05-00OR22725. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility.