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| Hauptseite > Publikationsdatenbank > Coupling the Community Land Model version 5.0 to the parallel data assimilation framework PDAF: description and applications |
| Hauptseite > Publikationsdatenbank > Coupling the Community Land Model version 5.0 to the parallel data assimilation framework PDAF: description and applications |
| Journal Article | FZJ-2022-00815 |
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2022
Copernicus
Katlenburg-Lindau
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Please use a persistent id in citations: http://hdl.handle.net/2128/30468 doi:10.5194/gmd-15-395-2022
Abstract: Land surface models are important for improving our understanding of the Earth system. They are continuously improving and becoming better in representing the different land surface processes, e.g., the Community Land Model version 5 (CLM5). Similarly, observational networks and remote sensing operations are increasingly providing more data, e.g., from new satellite products and new in situ measurement sites, with increasingly higher quality for a range of important variables of the Earth system. For the optimal combination of land surface models and observation data, data assimilation techniques have been developed in recent decades that incorporate observations to update modeled states and parameters. The Parallel Data Assimilation Framework (PDAF) is a software environment that enables ensemble data assimilation and simplifies the implementation of data assimilation systems in numerical models. In this study, we present the development of the new interface between PDAF and CLM5. This newly implemented coupling integrates the PDAF functionality into CLM5 by modifying the CLM5 ensemble mode to keep changes to the pre-existing parallel communication infrastructure to a minimum. Soil water content observations from an extensive in situ measurement network in the Wüstebach catchment in Germany are used to illustrate the application of the coupled CLM5-PDAF system. The results show overall reductions in root mean square error of soil water content from 7 % up to 35 % compared to simulations without data assimilation. We expect the coupled CLM5-PDAF system to provide a basis for improved regional to global land surface modeling by enabling the assimilation of globally available observational data.
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