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Ensemble Dressing of North American Land Data Assimilation version 2 (EDN2)

| DOI: 10.5065/KARJ-0E19

Most datasets of surface meteorology are deterministic, yet many applications using these datasets require or can benefit from uncertainty estimates in meteorological fields. Motivated by this gap, we applied a locally-weighted spatial regression technique with the widely-used North American Land Data Assimilation version 2 (NLDAS-2) dataset values to generate ensemble estimates for daily precipitation, daily mean temperature, and diurnal temperature range. The approach is a form of ensemble dressing. This uncertainty dataset and methods from this work are made publicly available to support research such a data assimilation or model uncertainty studies.

The dataset includes a 100-member ensemble for daily precipitation, temperature and diurnal temperature range at 1/8th degree for the NLDAS-2 domain (25 to 53 North, 125 to 67 West), for the time period 1979-2019. It also includes the spatial regression coefficients and other inputs needed to run the Gridded Meteorological Ensemble Tool (GMET) used to generate the ensembles. A limited number of summary statistical analyses of the dataset are also included.

Temporal Range:
1979-01-01 00:00 +0000 to 2019-12-31 00:00 +0000 (Entire dataset) Period details by dataset product
Precipitation Amount Surface Temperature
Vertical Levels:
See the detailed metadata for level information.
Data Types:
Spatial Coverage:
Longitude Range: Westernmost=134.727W Easternmost=67.007W
Latitude Range: Southernmost=25.062N Northernmost=51.141N Detailed coverage information
Data Contributors:
Climate and Global Dynamics Division, National Center for Atmospheric Research, University Corporation for Atmospheric Research
Hydrometeorological Applications Program, Research Application Laboratory, National Center for Atmospheric Research, University Corporation for Atmospheric Research
Centre for Hydrology, University of Saskatchewan
Liu, H., A. W. Wood, A. J. Newman, and M. P. Clark, 2022: Ensemble dressing of meteorological fields: using spatial regression to estimate uncertainty in deterministic gridded meteorological datasets J. Hydrometeorology, 23(10), 1525-1543 (DOI: 10.1175/JHM-D-21-0176.1).

Total Volume:
1.06 TB (Entire dataset) Volume details by dataset product
Data Formats:
Metadata Record:
Data License:
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