  Forecasting the formation and development of clouds is a central element of
modern weather forecasting systems. Incorrect clouds forecasts can lead to
major uncertainty in the overall accuracy of weather forecasts due to their
intrinsic role in the Earth's climate system. Few studies have tackled this
challenging problem from a machine learning point-of-view due to a shortage of
high-resolution datasets with many historical observations globally. In this
paper, we present a novel satellite-based dataset called ``CloudCast''. It
consists of 70,080 images with 10 different cloud types for multiple layers of
the atmosphere annotated on a pixel level. The spatial resolution of the
dataset is 928 x 1530 pixels (3x3 km per pixel) with 15-min intervals between
frames for the period 2017-01-01 to 2018-12-31. All frames are centered and
projected over Europe. To supplement the dataset, we conduct an evaluation
study with current state-of-the-art video prediction methods such as
convolutional long short-term memory networks, generative adversarial networks,
and optical flow-based extrapolation methods. As the evaluation of video
prediction is difficult in practice, we aim for a thorough evaluation in the
spatial and temporal domain. Our benchmark models show promising results but
with ample room for improvement. This is the first publicly available
global-scale dataset with high-resolution cloud types on a high temporal
granularity to the authors' best knowledge.
