An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space. (27th October 2022) - Kumar, Sujay; Kolassa, Jana; Reichle, Rolf; Crow, Wade; de Lannoy, Gabrielle; de Rosnay, Patricia; MacBean, Natasha; Girotto, Manuela; Fox, Andy; Quaife, Tristan; Draper, Clara; Forman, Barton; et al.
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. Plain Language Summary: Satellite remote sensing measurements have enabled the monitoring of the Earth's land surface with unprecedented scale and frequency. These measurements allow us to monitor the changes on the land surface and understand the contribution of human activities toward them. The information from such observations is combined with the modeled estimates through data assimilation (DA) algorithms. This article discusses the progress made in the development of land DA systems and the major gaps that remain. The paper also outlines priorities that we need to consider in the development of next generation land DA systems so that the potential of land remote sensing measurements can be fully realized. Key Points: Land data assimilation has shown significant promise for short‐term forecasting applications Significant gaps remain in the current land data assimilation systems related to observation utilization and models Coordinated development with the modeling and observational community and adoption of technological enhancements are needed in the future