  When providing the boundary conditions for hydrological flood models and
estimating the associated risk, interpolating precipitation at very high
temporal resolutions (e.g. 5 minutes) is essential not to miss the cause of
flooding in local regions. In this paper, we study optical flow-based
interpolation of globally available weather radar images from satellites. The
proposed approach uses deep neural networks for the interpolation of multiple
video frames, while terrain information is combined with temporarily
coarse-grained precipitation radar observation as inputs for self-supervised
training. An experiment with the Meteonet radar precipitation dataset for the
flood risk simulation in Aude, a department in Southern France (2018),
demonstrated the advantage of the proposed method over a linear interpolation
baseline, with up to 20% error reduction.
