TY - JOUR
T1 - An Agenda for Land Data Assimilation Priorities
T2 - Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space
AU - Kumar, Sujay
AU - Kolassa, Jana
AU - Reichle, Rolf
AU - Crow, Wade
AU - de Lannoy, Gabrielle
AU - de Rosnay, Patricia
AU - MacBean, Natasha
AU - Quaife, Tristan
AU - Steele-Dunne, Susan
AU - More Authors, null
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - data assimilation
KW - hydrology
KW - land surface
KW - remote sensing
UR - http://www.scopus.com/inward/record.url?scp=85142896856&partnerID=8YFLogxK
U2 - 10.1029/2022MS003259
DO - 10.1029/2022MS003259
M3 - Review article
AN - SCOPUS:85142896856
SN - 1942-2466
VL - 14
JO - Journal of Advances in Modeling Earth Systems
JF - Journal of Advances in Modeling Earth Systems
IS - 11
M1 - e2022MS003259
ER -