Streamflow data assimilation for numerical weather prediction models

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Numerical weather prediction (NWP) and therefore quantitative precipitation forecast (QPF) is to a large extent an initial and boundary value problem. Skill in summertime continental precipitation prediction depends an the initialization of soil moisture and other land surface states. Though the Deutscher Wetterdienst (DWD) implemented a method into the Local Model (LM) that assimilates 2m-air temperature and adjusts the soil state to meet the observed atmospheric state, the soil moisture of this method does not necessarily reflect reality. The objective of this project is to investigate the potential of streamflow data assimilation to obtain a better estimate of the horizontal and vertical distribution of the root zone soil moisture content especially in areas of dense vegetation to contribute to an improvement of the initial condition for quantitative precipitation forecast. An observation operator, which defines the contribution of an individual grid box to the spatially and temporally integrated streamflow observation will be derived based an model ensembles. The results of this project will provide the DWD and scientific community with an estimate of the potential of the streamflow data in soil moisture improvements and a quantification of the land surface model error with respect to the water balance. A routing model within the LM-TERRA will benefit not only the DWD but also the flood forecasting services.