  The proper determination of soil moisture on different scales is important
for applications in a variety of fields. We aim to develop a high-level soil
moisture product with high temporal and spatial resolution by assimilating the
multilayer soil moisture product SCATSAR-SWI (Scatterometer Synthetic Aperture
Radar Soil Water Index) into the surface model SURFEX. In addition, we probe
the impact of the findings on the Numerical Weather Prediction (NWP) in
Austria. The data assimilation system consists of the NWP model AROME and the
SURFEX Offline Data Assimilation, which provide atmospheric forcing and soil
moisture fields as mutual input. To address the known sensitivity of the
employed simplified Extended Kalman Filter to the specification of errors, we
compute the observation error variances of the SCATSAR-SWI locally using Triple
Collocation Analysis and implement them into the assimilation system. The
verification of the forecasted 2 m temperature and relative humidity against
measurements of Austrian weather stations shows that the actual impact of the
local error approach on the atmospheric forecast is slightly positive to
neutral compared to the standard error approach, depending on the time of the
year. The direct verification of the soil moisture analysis against a gridded
water balance product reveals a degradation of the unbiased root mean square
error for small observation errors.
