An agenda for land data assimilation priorities: Realizing the promise of terrestrial water, energy, and vegetation observations from space

Authors: KUMAR, S. - Goddard Space Flight Center; KOLASSA, J. - Goddard Space Flight Center; REICHLE, R. - Goddard Space Flight Center; DE LANNOY, G. - Ku Leuven; DE ROSNAY, P. - European Centre For Medium-Range Weather Forecasts (ECMWF); MACBEAN, N. - Western University; GIROTTO, M. - University Of California Berkeley; FOX, A. - Collaborator; QUAIFE, T. - University Of Reading; DRAPER, C. - National Oceanic & Atmospheric Administration (NOAA); FORMAN, B. - University Of Maryland; BALSAMO, G. - University Of Reading; STEELE-DUNNE, S. - Delft University; ALBERGEL, C. - University Of Toulouse; BONAN, B. - University Of Toulouse; CALVET, J. - University Of Toulouse; DONG, J. - Tianjin University; LIDDY, H. - Columbia University - New York; RUSTON, B. - Collaborator; Crow, Wade

Submitted to: Journal of Advances in Modeling Earth Systems
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/3/2022
Publication Date: 10/6/2022
Citation: Kumar, S., Kolassa, J., Reichle, R., De Lannoy, G., De Rosnay, P., Macbean, N., Girotto, M., Fox, A., Quaife, T., Draper, C., Forman, B., Balsamo, G., Steele-Dunne, S., Albergel, C., Bonan, B., Calvet, J.C., Dong, J., Liddy, H., Ruston, B., Crow, W.T. 2022. 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. 14(11). https://doi.org/10.1029/2022MS003259.
DOI: https://doi.org/10.1029/2022MS003259

Interpretive Summary: By intelligently combining information acquired from a variety of sources (e.g., modelling, satellite remote sensing, and ground observations), land surface data assimilation systems provide a valuable tool for generating optimized estimates of land surface states like soil moisture, soil temperature, and vegetation biomass required for agricultural monitoring and forecasting applications. This paper describes the current state of the art in land surface data assimilation techniques and identifies future research priorities related to improving the coupling between the water, energy, and carbon cycles in large-scale land surface and agroecosystem models. By focusing research efforts on this promising direction, this paper will encourage the development of land data assimilation approaches that enhance USDA’s ability to globally monitor water resource availability and agricultural productivity.

Technical Abstract: The task of quantifying the spatial and temporal variations of the terrestrial water, energy, and vegetation conditions is challenging due to the significant complexity and heterogeneity of these processes, all of which are impacted by climate change and anthropogenic activities. To address this challenge, Earth Observations (EOs) and their utilization within data assimilation (DA) systems are vital. Satellite EOs, in particular, are very relevant, as they offer quasi-global coverage, are non-intrusive, and provide uniformity, rapid measurements, and continuity. The past three decades have seen an unprecedented growth in the number and variety of remote sensing technologies launched by space agencies and commercial companies around the world. There have also been significant developments in modeling and DA systems to provide tools that can exploit these measurements. Despite these advances, several important gaps remain current land DA (LDA) 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 LDA priorities so that the next generation LDA 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.