  Deep learning methods dominate short-term high-resolution precipitation
nowcasting in terms of prediction error. However, their operational usability
is limited by difficulties explaining dynamics behind the predictions, which
are smoothed out and missing the high-frequency features due to optimizing for
mean error loss functions. We experiment with hand-engineering of the
advection-diffusion differential equation into a PhyCell to introduce more
accurate physical prior to a PhyDNet model that disentangles physical and
residual dynamics. Results indicate that while PhyCell can learn the intended
dynamics, training of PhyDNet remains driven by loss optimization, resulting in
a model with the same prediction capabilities.
