Journal Article

FZJ-2023-00511

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication

De Lannoy, G. J. M. (Corresponding author) ; Bechtold, M. ; Albergel, C. ; Brocca, L. ; Calvet, J.-C. ; Carrassi, A. ; Crow, W. T. ; de Rosnay, P. ; Durand, M. ; Forman, B. ; Geppert, G. ; Girotto, M. ; Hendricks-Franssen, H.-J.FZJ* ; Jonas, T. ; Kumar, S. ; Lievens, H. ; Lu, Y. ; Massari, C. ; Pauwels, V. R. N. ; Reichle, R. H. ; Steele-Dunne, S.

2022
Frontiers Media Lausanne

This record in other databases:    

Please use a persistent id in citations: http://hdl.handle.net/2128/33546  doi:10.3389/frwa.2022.981745

Abstract: The beginning of the 21st century is marked by a rapid growth of land surface satellite data and model sophistication. This offers new opportunities to estimate multiple components of the water cycle via satellite-based land data assimilation (DA) across multiple scales. By resolving more processes in land surface models and by coupling the land, the atmosphere, and other Earth system compartments, the observed information can be propagated to constrain additional unobserved variables. Furthermore, access to more satellite observations enables the direct constraint of more and more components of the water cycle that are of interest to end users. However, the finer level of detail in models and data is also often accompanied by an increase in dimensions, with more state variables, parameters, or boundary conditions to estimate, and more observations to assimilate. This requires advanced DA methods and efficient solutions. One solution is to target specific observations for assimilation based on a sensitivity study or coupling strength analysis, because not all observations are equally effective in improving subsequent forecasts of hydrological variables, weather, agricultural production, or hazards through DA. This paper offers a perspective on current and future land DA development, and suggestions to optimally exploit advances in observing and modeling systems.

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Contributing Institute(s):

1. Agrosphäre (IBG-3)

Research Program(s):

1. 2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217) (POF4-217)

Appears in the scientific report 2022

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