Satellite inspired hydrology in an uncertain future: a H SAF and HEPEX workshop

The performance of numerical weather prediction (NWP) can be improved significantly by assimilating remotely sensed soil moisture in the included land surface model. In our study, we investigate the improvement of the NWP in the Austrian domain by assimilating the multi-layer fused data product SCATSAR-SWI (Soil Water Index) with the surface model SURFEX. The SURFEX assimilation employs a multi-layer diffusion scheme approach and the simplified Extended Kalman Filter. The data has a grid sampling of 500 m, whereas the assimilation system is run with different grid resolutions. Our goal is to find the optimum relation between exploiting the high-resolution information and reasonable computational effort as well as to test the limits of the underlying physical parametrisations in the NWP model.

The soil moisture analysis with 1.25 km resolution shows a positive impact on the NWP skill metrics compared to 2.5 km when using the same setup for the atmospheric forecast model. To probe the quality of the soil moisture analysis directly, we plan furthermore a comparison with in-situ measurements in Austria.