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

ds613.0
| DOI: 10.5065/KARJ-0E19
 
Abstract:

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
Updates:
Irregularly
Variables:
Precipitation Amount Surface Temperature
Vertical Levels:
See the detailed metadata for level information.
Data Types:
Grid
Spatial Coverage:
Longitude Range: Westernmost=134.727W Easternmost=67.007W
Latitude Range: Southernmost=25.062N Northernmost=51.141N Detailed coverage information
Data Contributors:
UCAR/NCAR/CGD
Climate and Global Dynamics Division, National Center for Atmospheric Research, University Corporation for Atmospheric Research
 |  UCAR/NCAR/RAL/HAP
Hydrometeorological Applications Program, Research Application Laboratory, National Center for Atmospheric Research, University Corporation for Atmospheric Research
 |  USASK/HYDROLOGY
Centre for Hydrology, University of Saskatchewan
Publications:
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:
More Details:
View a more detailed summary of the data, including specific date ranges and locations by parameter
Metadata Record:
Data License:
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