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An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space

Abstract : The 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.
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https://hal-meteofrance.archives-ouvertes.fr/meteo-03842193
Contributor : Jean-Christophe CALVET Connect in order to contact the contributor
Submitted on : Monday, November 7, 2022 - 2:03:22 PM
Last modification on : Thursday, November 17, 2022 - 4:12:07 AM

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Sujay Kumar, Jana Kolassa, Rolf Reichle, Wade Crow, Gabrielle de Lannoy, et al.. An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space. Journal of Advances in Modeling Earth Systems, 2022, 14, ⟨10.1029/2022ms003259⟩. ⟨meteo-03842193⟩

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