  Numerical Weather Prediction (NWP), is widely used in precipitation
forecasting, based on complex equations of atmospheric motion requires
supercomputers to infer the state of the atmosphere. Due to the complexity of
the task and the huge computation, this methodology has the problems of
inefficiency and non-economic. With the rapid development of meteorological
technology, the collection of plentiful numerical meteorological data offers
opportunities to develop data-driven models for NMP task. In this paper, we
consider to combine NWP with deep learning. Firstly, to improve the
spatiotemporal modeling of meteorological elements, a deconstruction mechanism
and the multi-scale filters are composed to propose a multi-scale deconstructed
ConvLSTM (MSD-ConvLSTM). The MSD-ConvLSTM captures and fuses the contextual
information by multi-scale filters with low parameter consumption. Furthermore,
an encoder-decoder is constructed to encode the features of multiple
meteorological elements by deep CNN and decode the spatiotemporal information
from different elements by the MSD-ConvLSTM. Our method demonstrates the
data-driven way is significance for the weather prediction, which can be
confirmed from the experimental results of precipitation forecasting on the
European Centre Weather Forecasts (EC) and China Meteorological Forecasts (CM)
datasets.
