Coupling the Community Land Model version 5.0 to the parallel data assimilation framework PDAF: description and applications
<p>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 sens...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-01-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/15/395/2022/gmd-15-395-2022.pdf |
| Summary: | <p>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.</p> |
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| ISSN: | 1991-959X 1991-9603 |