Assessment and Enhancement of MERRA Land Surface Hydrology Estimates

Author:

Reichle Rolf H.1,Koster Randal D.1,De Lannoy Gabriëlle J. M.2,Forman Barton A.3,Liu Qing4,Mahanama Sarith P. P.4,Touré Ally5

Affiliation:

1. Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

2. Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, and Goddard Earth Sciences and Technology Center, University of Maryland, Baltimore County, Baltimore, Maryland, and Laboratory of Hydrology and Water Management, Ghent University, Ghent, Belgium

3. Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland, and Oak Ridge Associated Universities, Oak Ridge, Tennessee

4. Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, and Science Applications International Corporation, Beltsville, Maryland

5. Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, and Goddard Earth Sciences and Technology Center, University of Maryland, Baltimore County, Baltimore, Maryland

Abstract

Abstract The Modern-Era Retrospective Analysis for Research and Applications (MERRA) is a state-of-the-art reanalysis that provides, in addition to atmospheric fields, global estimates of soil moisture, latent heat flux, snow, and runoff for 1979–present. This study introduces a supplemental and improved set of land surface hydrological fields (“MERRA-Land”) generated by rerunning a revised version of the land component of the MERRA system. Specifically, the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameter values in the rainfall interception model, changes that effectively correct for known limitations in the MERRA surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in land surface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ECMWF Re-Analysis-Interim (ERA-I). MERRA-Land and ERA-I root zone soil moisture skills (against in situ observations at 85 U.S. stations) are comparable and significantly greater than that of MERRA. Throughout the Northern Hemisphere, MERRA and MERRA-Land agree reasonably well with in situ snow depth measurements (from 583 stations) and with snow water equivalent from an independent analysis. Runoff skill (against naturalized stream flow observations from 18 U.S. basins) of MERRA and MERRA-Land is typically higher than that of ERA-I. With a few exceptions, the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using MERRA output for land surface hydrological studies.

Publisher

American Meteorological Society

Subject

Atmospheric Science

Reference59 articles.

1. The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present);Adler;J. Hydrometeor.,2003

2. Development of a hydrometeorological forcing data set for global soil moisture estimation;Berg;Int. J. Climatol.,2005

3. Global energy and water budgets in MERRA;Bosilovich;J. Climate,2011

4. A global analysis of snow depth for numerical weather prediction;Brasnett;J. Appl. Meteor.,1999

5. Canadian Meteorological Centre (CMC) Daily Snow Depth Analysis Data;Brown,2010

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