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An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From SpaceThe task of quantifying spatial and temporal variations in terrestrial water, energy, and vegetation conditions is challenging due to the significant complexity and heterogeneity of these conditions, all of which are impacted by climate change and anthropogenic activities. To address this challenge, Earth Observations (EOs) of the land and their utilization within data assimilation (DA) systems are vital. Satellite EOs are particularly relevant, as they offer quasi-global coverage, are non-intrusive, and provide uniformity, rapid measurements, and continuity. The past three decades have seen unprecedented growth in the number and variety of land remote sensing technologies launched by space agencies and commercial companies around the world. There have also been significant developments in land modeling and DA systems to provide tools that can exploit these measurements. Despite these advances, several important gaps remain in current land DA research and applications. This paper discusses these gaps, particularly in the context of using DA to improve model states for short-term numerical weather and sub-seasonal to seasonal predictions. We outline an agenda for land DA priorities so that the next generation of land DA systems will be better poised to take advantage of the significant current and anticipated shifts and advancements in remote sensing, modeling, computational technologies, and hardware resources.
Document ID
20220015159
Acquisition Source
Goddard Space Flight Center
Document Type
Accepted Manuscript (Version with final changes)
Authors
Sujay Kumar ORCID
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Jana Kolassa ORCID
(Science Systems and Applications (United States) Lanham, Maryland, United States)
Rolf Reichle ORCID
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Wade Crow ORCID
(United States Department of Agriculture Washington D.C., District of Columbia, United States)
Gabrielle de Lannoy ORCID
(KU Leuven Leuven, Belgium)
Patricia de Rosnay ORCID
(European Centre for Medium-Range Weather Forecasts Reading, United Kingdom)
Natasha MacBean ORCID
(Western University London, Ontario, Canada)
Manuela Girotto ORCID
(University of California, Berkeley Berkeley, California, United States)
Andy Fox ORCID
(Joint Center for Satellite Data Assimilation)
Tristan Quaife ORCID
(University of Reading Reading, United Kingdom)
Clara Draper ORCID
(National Oceanic and Atmospheric Administration Washington D.C., District of Columbia, United States)
Barton Forman
(University of Maryland, College Park College Park, Maryland, United States)
Gianpaolo Balsamao
(European Centre for Medium-Range Weather Forecasts Reading, United Kingdom)
Susan Steele-Dunne ORCID
(Delft University of Technology Delft, Zuid-Holland, Netherlands)
Clement Albergel ORCID
(European Centre for Space Applications and Telecommunications Didcot, United Kingdom)
Bertrand Bonan
(University of Toulouse Toulouse, Midi-Pyrénées, France)
Jean-Christophe Calvet ORCID
(University of Toulouse Toulouse, Midi-Pyrénées, France)
Jianzhi Dong ORCID
(Tianjin University Tianjin, China)
Hannah Liddy ORCID
(Columbia University New York, New York, United States)
Benjamin Ruston
(Joint Center for Satellite Data Assimilation)
Date Acquired
October 7, 2022
Publication Date
October 6, 2022
Publication Information
Publication: Journal of Advances in Modelling Earth Systems (JAMES)
Publisher: American Geophysical Union
Volume: 14
Issue: 11
Issue Publication Date: October 27, 2022
e-ISSN: 1942-2466
Subject Category
Earth Resources and Remote Sensing
Funding Number(s)
WBS: 389018.02.15.06.93
CONTRACT_GRANT: NNG17HP01C
CONTRACT_GRANT: 80NSSC21D0002
CONTRACT_GRANT: 80NSSC20K1301
CONTRACT_GRANT: 80NSSC20K0741
CONTRACT_GRANT: 80NSSC20M0282
Distribution Limits
Public
Copyright
Portions of document may include copyright protected material.
Technical Review
External Peer Committee
Keywords
land surface
data assimilation
remote sensing
hydrology
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