  Meteorological satellite imagery is critical for meteorologists. The data
have played an important role in monitoring and analyzing weather and climate
changes. However, satellite imagery is a kind of observation data and exists a
significant time delay when transmitting the data back to Earth. It is
important to make accurate predictions for meteorological satellite images,
especially the nowcasting prediction up to 2 hours ahead. In recent years,
there has been growing interest in the research of nowcasting prediction
applications of weather radar images based on deep learning. Compared to the
weather radar images prediction problem, the main challenge for meteorological
satellite images prediction is the large-scale observation areas and therefore
the large sizes of the observation products. Here we present a deep
multi-scales extrapolation fusion method, to address the challenge of the
meteorological satellite images nowcasting prediction. First, we downsample the
original satellite images dataset with large size to several images datasets
with smaller resolutions, then we use a deep spatiotemporal sequences
prediction method to generate the multi-scales prediction images with different
resolutions separately. Second, we fuse the multi-scales prediction results to
the targeting prediction images with the original size by a conditional
generative adversarial network. The experiments based on the FY-4A
meteorological satellite data show that the proposed method can generate
realistic prediction images that effectively capture the evolutions of the
weather systems in detail. We believe that the general idea of this work can be
potentially applied to other spatiotemporal sequence prediction tasks with a
large size.
