Preprints
https://doi.org/10.5194/gmd-2021-38
https://doi.org/10.5194/gmd-2021-38

Submitted as: development and technical paper 08 Sep 2021

Submitted as: development and technical paper | 08 Sep 2021

Review status: this preprint is currently under review for the journal GMD.

Coupling the Community Land Model version 5.0 to the parallel data assimilation framework PDAF: Description and applications

Lukas Strebel1,2, Heye Bogena1,2, Harry Vereecken1,2, and Harrie-Jan Hendricks Franssen1,2 Lukas Strebel et al.
  • 1Agrosphere Institute, IBG-3, Forschungszentrum Jülich GmbH, Germany
  • 2Centre for High-Performance Scientific Computing in Terrestrial Systems: HPSC TerrSys, Geoverbund ABC/J, Leo-Brandt-Strasse, 52425 Jülich

Abstract. Land surface models are important for improving our understanding of the earth system. They are continuously improving and becoming more accurate in describing the varied surface processes, e.g. the Community Land Model version 5 (CLM5). Similarly, observational networks and remote sensing operations are increasingly providing more and higher quality data. For the optimal combination of land surface models and observation data, data assimilation techniques have been developed in the past 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 paper, we present the further development of the PDAF to enable its application in combination with CLM5. This novel coupling adapts the optional CLM5 ensemble mode to enable integration of PDAF filter routines while keeping 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 modelling by enabling the assimilation of globally available observational data.

Lukas Strebel et al.

Status: open (until 03 Nov 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-38', Anonymous Referee #1, 21 Sep 2021 reply
    • AC1: 'Reply on RC1', Lukas Strebel, 04 Oct 2021 reply
  • RC2: 'Comment on gmd-2021-38', Anonymous Referee #2, 29 Sep 2021 reply

Lukas Strebel et al.

Model code and software

TSMP CLM5-PDAF coupling code Lukas Strebel https://doi.org/10.5281/zenodo.4534157

Lukas Strebel et al.

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Short summary
We present the technical coupling between a land surface model (CLM5) and the Parallel Data Assimilation Framework (PDAF). This coupling enables measurement data to update simulated model states and parameters in a statistically optimal way. We demonstrate the viability of the model framework using an application in a forested catchment where the inclusion of soil water measurements significantly improved the simulation quality.