  The Weather4cast 2021 competition gave the participants a task of predicting
the time evolution of two-dimensional fields of satellite-based meteorological
data. This paper describes the author's efforts, after initial success in the
first stage of the competition, to improve the model further in the second
stage. The improvements consisted of a shallower model variant that is
competitive against the deeper version, adoption of the AdaBelief optimizer,
improved handling of one of the predicted variables where the training set was
found not to represent the validation set well, and ensembling multiple models
to improve the results further. The largest quantitative improvements to the
competition metrics can be attributed to the increased amount of training data
available in the second stage of the competition, followed by the effects of
model ensembling. Qualitative results show that the model can predict the time
evolution of the fields, including the motion of the fields over time, starting
with sharp predictions for the immediate future and blurring of the outputs in
later frames to account for the increased uncertainty.
