Project: Data Assimilation for Improved Characterization of Fluxes across Compartmental Interfaces - Model simulations of water and energy fluxes in the subsurface-landsurface-atmosphere system (SLAS) are important ingredients to climate and weather prediction, flood forecasting, water resources management, agriculture, and water quality control. Due to the immense scale complexity of terrestrial systems already the estimation of its state - which is a prerequisite of any prediction - is often insufficient despite the wealth of observations available nowadays from in-situ and remote sensing. Data assimilation optimally exploits observations for the estimation of system state evolution, but different philosophies and methods exist in the geoscientific disciplines of meteorology, surface, vadose-zone and groundwater hydrology concerning the structure of SLAS models and its use in data assimilation. In order to improve predictions for terrestrial systems we believe that a unified data assimilation framework is essential including simulation platforms that treat the SLAS in an integrated fashion and avoid compartmentalization of the terrestrial system. The development of such a data assimilation framework is the prime objective of the Research Unit. Homepage of project see: http://for2131.de/ All simulations were conducted on the JUQUEEN and JUWELS supercomputers at Jülich Supercomputing Centre (JSC) at Forschungszentrum Jülich. Compute time was granted by the Gauss Centre for Supercomputing (GCS) and the John von Neumann Institute for Computing (NIC). This project is funded by DFG (Deutsche Forschungsgemeinschaft, https://www.dfg.de/en/index.jsp). Summary: This version of the fully coupled catchment simulation features the atmospheric model COSMO run at 1.1km (0.01°rotlat/lon grid), the land surface model CLM and the groundwater model Parflow, both run at 400m (regular lat/lon grid). Coupled with OASIS3-MCT.
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