Perspective on Satellite-Based Land Data Assimilation to Estimate Water Cycle Components in an Era of Advanced Data Availability and Model Sophistication

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.

Bibliographic Details

Main Authors:	De Lannoy, Gabrielle, Bechtold, Michel, Albergel, Clément, Brocca, Luca, Calvet, Jean-Christophe, Carrassi, Alberto, Crow, Wade T., De Rosnay, Patricia, Durand, Michael, Forman, Bart, Geppert, Gernot, Girotto, Manuela, Franssen, Harrie-Jan Hendricks, Jonas, Tobias, Kumar, Sujay V., Lievens, Hans, Lu, Yang, Massari, Christian, Pauwels, Valentjn, Reichle, Rolf, Steele-Dunne, Susan
Other Authors:	European Commission
Format:	artículo de revisión biblioteca
Language:	English
Published:	Frontiers Media 2022-09-16
Subjects:	Data assimilation, Soil moisture, Snow, Vegetation, Microwave remote sensing, Land surface modeling, Targeted observations,
Online Access:	http://hdl.handle.net/10261/277859 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100000844 http://dx.doi.org/10.13039/501100003130
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