  With the highly demand of large-scale and real-time weather service for
public, a refinement of short-time cloudage prediction has become an essential
part of the weather forecast productions. To provide a
weather-service-compliant cloudage nowcasting, in this paper, we propose a
novel hierarchical Convolutional Long-Short-Term Memory network based deep
learning model, which we term as FORECAST-CLSTM, with a new Forecaster loss
function to predict the future satellite cloud images. The model is designed to
fuse multi-scale features in the hierarchical network structure to predict the
pixel value and the morphological movement of the cloudage simultaneously. We
also collect about 40K infrared satellite nephograms and create a large-scale
Satellite Cloudage Map Dataset(SCMD). The proposed FORECAST-CLSTM model is
shown to achieve better prediction performance compared with the
state-of-the-art ConvLSTM model and the proposed Forecaster Loss Function is
also demonstrated to retain the uncertainty of the real atmosphere condition
better than conventional loss function.
