  The objective of this work is to provide high-resolution rain rate maps at
short lead-time forecasts (nowcasts) necessary to anticipate flooding and
properly manage sewage systems in urban areas by combining radars, rain gauges,
and operational microwave links, and taking into account their respective
uncertainties. A variational approach (3D-Var) is used to find the best
estimate for the rain rate, and its error covariance, from the different rain
sensors. Short-term rain rate forecasts are then produced by assuming
Lagrangian persistence. A velocity field is obtained from the operational
radar-derived rain fields, and the rain rate field is advected using the Total
Variance Diminishing (TVD) scheme. The error covariance associated to the
estimated rain rate is also propagated, and we use these two in the 3D-Var at
the next observation time step. This approach can be seen as a Variational
Kalman Filter (VKF), in which the covariance of the prior is not constant but
dependent on time. The proposed approach has been tested using data from 14
rain gauges, 14 microwave links and the operational radar rain product from
MeteoSwiss in the area of Zurich (Switzerland). During the applications the
assumption of the Lagrangian persistence appears to be valid up to 20 min (a
bit longer for stratiform events). During convective events, the algorithm is
less powerful and shorter lead times should be considered (i.e., 15 min).
Although such lead times are short, they are still useful to various
hydrological and outdoor applications.
