Scale-Problems in Assimilating of Passive Microwave Observation into Coupled Models

• Applicant: Professor Dr. Clemens Simmer
• Subject Area: Hydrogeology, Hydrology, Limnology, Urban Water Management, Water Chemistry, Integrated Water Resources Management ; Atmospheric Science
• Term: from 2013 to 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 243358811

This project explores the use and value of passive L-band satellite observations for ensemble-based data assimilation with fully-coupled terrestrial system models for mesoscale catchments. Model resolutions are typically of the order of 100 m for land components and 1 km for the atmospheric component of such models, which is much smaller than for the satellite observations with typically tens of kilometers. Ensemble-based data assimilation requires the generation of synthetic observations from the terrestrial system model via an observation operator, which are compared with observations for the generation of the analysis ensemble. Since the model state does not necessarily include all information required for the observation operator, which is in large parts a radiative transfer model, missing information must be inferred from external data.The main objectives of the project are the development of a suitable observation operator, which is able to mimic L-Band satellite observations in the best possible way, and its use for the exploitation of such observations in the data assimilation context with the Terrestrial Systems Modeling Platform (TerrSysMP, Shrestha et al. 2014) coupled to the Parallelized Data Assimilation Framework (PDAF, Nerger et al. 2013). While Phase I focused mainly on the compilation of a flexible observation operator and its comparison with real observations, Phase II will pursue further improvements concerning the representation of vegetation by the observation operator and its operationalization, but mainly concentrate on its use for data assimilation. This includes (a) the quantification of biases between observations generated from the data assimilation model and observations both from the virtual and true reality, (b) data assimilation experiments which quantify the value of such observations given other observations (e.g. precipitation), and (c) pre-processing and filtering methods to better exploit the information content of the large-scale observations.

• DFG Programme: Research Units
• Subproject of: FOR 2131: Data Assimilation for Improved Characterisation of Fluxes across Compartmental Interfaces
• International Connection: Netherlands
• Cooperation Partner: Privatdozent Dr. Matthias Drusch