Abstract. Combining numerical models, which simulate water and energy fluxes in the subsurface-land surface-atmosphere system in a physically consistent way, becomes increasingly important to understand and study fluxes at compartmental boundaries and interdependencies of states across these boundaries. Complete state evolutions generated by such models, when run at highest possible resolutions while incorporating as many processes as attainable, may be regarded as a proxy of the real world – a virtual reality – which can be used to test hypotheses on functioning of the coupled terrestrial system and may serve as source for virtual measurements to develop data-assimilation methods. Such simulation systems, however, face severe problems caused by the vastly different scales of the processes acting in the compartments of the terrestrial system. The present study is motivated by the development of cross-compartmental data-assimilation methods, which face the difficulty of data scarcity in the subsurface when applied to real data. With appropriate and realistic measurement operators, the virtual reality not only allows taking virtual observations in any part of the terrestrial system at any density, thus overcoming data-scarcity problems of real-world applications, but also provides full information about true states and parameters aimed to be reconstructed from the measurements by data assimilation. In the present study, we have used the Terrestrial Systems Modeling Platform TerrSysMP, which couples the meteorological model COSMO, the land-surface model CLM, and the subsurface model ParFlow, to set up the virtual reality for a regional terrestrial system roughly oriented at the Neckar catchment in southwest Germany. We find that the virtual reality is in many aspects quite close to real observations of the catchment concerning, e.g., atmospheric boundary-layer height, precipitation, and runoff. But also discrepancies become apparent both in the ability of such models to correctly simulate some processes – which still need improvement – and the realism of the results of some observation operators like the SMOS and SMAP sensors, when faced with model states. In a succeeding step, we will use the virtual reality to generate observations in all compartments of the system for coupled data assimilation. The data assimilation will rely on a coarsened and simplified version of the model system.

and Steele-Dunne S ( ) Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication. Front. Water : . doi: .

Abstract. Land surface models can model matter and energy fluxes between the land surface and atmosphere, and provide a lower boundary condition to atmospheric circulation models. For these applications, accurate soil moisture quantification is highly desirable but not always possible given limited observations and limited subsurface data accuracy. Cosmic-ray probes (CRPs) offer an interesting alternative to indirectly measure soil moisture and provide an observation that can be assimilated into land surface models for improved soil moisture prediction. Synthetic studies have shown the potential to estimate subsurface parameters of land surface models with the assimilation of CRP observations. In this study, the potential of a network of CRPs for estimating subsurface parameters and improved soil moisture states is tested in a real-world case scenario using the local ensemble transform Kalman filter with the Community Land Model. The potential of the CRP network was tested by assimilating CRP-data for the years 2011 and 2012 (with or without soil hydraulic parameter estimation), followed by the verification year 2013. This was done using (i) the regional soil map as input information for the simulations, and (ii) an erroneous, biased soil map. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the biased soil map, soil moisture characterization improved in both periods strongly from a ERMS of 0.11 cm3/cm3 to 0.03 cm3/cm3 (assimilation period) and from 0.12 cm3/cm3 to 0.05 cm3/cm3 (verification period) and the estimated soil hydraulic parameters were after assimilation closer to the ones of the regional soil map. Finally, the value of the CRP network was also evaluated with jackknifing data assimilation experiments. It was found that the CRP network is able to improve soil moisture estimates at locations between the assimilation sites from a ERMS of 0.12 cm3/cm3 to 0.06 cm3/cm3 (verification period), but again only if the initial soil map was biased.

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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