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The Global Land Data Assimilation System

Bibliographic Data

ID23334263
AuthorsMatthew Rodell (0000-0003-0106-7437, Goddard Space Flight Center), Paul R Houser (0000-0002-2991-0441, Goddard Space Flight Center), U Jambor (University of Maryland, College Park), Jon Gottschalck (University of Maryland, College Park), K Mitchell (0000-0001-9345-1481), C-J Meng, K Arsenault, Kristi R Arsenault (0000-0002-6830-6271, University of Maryland, College Park), B Cosgrove (0000-0002-3921-0553, University of Maryland, College Park), J Radakovich (University of Maryland, College Park), M Bosilovich, M G Bosilovich (Goddard Space Flight Center), J K Entin, Jared Entin (University of Maryland, College Park), Jeffrey P Walker (0000-0002-4817-2712, The University of Melbourne), Dag Lohmann (0009-0004-6337-4656, NOAA National Centers for Environmental Prediction), D L Toll (Goddard Space Flight Center), D Toll
Year2004
Volume85
Issue3
Pages381-394
Publication date2004-03-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueBulletin of the American Meteorological Society (JOURNAL)
Journal identifiersISSN: 0003-0007 • E-ISSN: 1520-0477
PublisherAmerican Meteorological Society (PUBLISHER • US)
DOI10.1175/bams-85-3-381
OpenAlexW2119132330
LanguageEN
Citations received101
References cited1

A Global Land Data Assimilation System (GLDAS) has been developed. Its purpose is to ingest satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes. GLDAS is unique in that it is an uncoupled land surface modeling system that drives multiple models, integrates a huge quantity of observation-based data, runs globally at high resolution (0.25°), and produces results in near–real time (typically within 48 h of the present). GLDAS is also a test bed for innovative modeling and assimilation capabilities. A vegetation-based “tiling” approach is used to simulate subgrid-scale variability, with a 1-km global vegetation dataset as its basis. Soil and elevation parameters are based on high-resolution global datasets. Observation-based precipitation and downward radiation and output fields from the best available global coupled atmospheric data assimilation systems are employed as forcing data. The high-quality, global land surface fields provided by GLDAS will be used to initialize weather and climate prediction models and will promote various hydrometeorological studies and applications. The ongoing GLDAS archive (started in 2001) of modeled and observed, global, surface meteorological data, parameter maps, and output is publicly available.

Assimilation (phonology) · Climate change · Climate model · Climatology · Data assimilation · Forcing (mathematics) · Geography · Hydrometeorology · Meteorology · Precipitation · Remote sensing · Satellite · Climate change and permafrost · Computer Science · Cryospheric studies and observations · Environmental Science · Geology · Soil Moisture and Remote Sensing

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Unique citing works101
Citations per year5,05
Citation span2006 - 2026 (21)
Citation velocitycurrent
Highly citedYes
Citation typesNeutral: 98

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