  Applying machine learning models to meteorological data brings many
opportunities to the Geosciences field, such as predicting future weather
conditions more accurately. In recent years, modeling meteorological data with
deep neural networks has become a relevant area of investigation. These works
apply either recurrent neural networks (RNN) or some hybrid approach mixing RNN
and convolutional neural networks (CNN). In this work, we propose STConvS2S
(Spatiotemporal Convolutional Sequence to Sequence Network), a deep learning
architecture built for learning both spatial and temporal data dependencies
using only convolutional layers. Our proposed architecture resolves two
limitations of convolutional networks to predict sequences using historical
data: (1) they violate the temporal order during the learning process and (2)
they require the lengths of the input and output sequences to be equal.
Computational experiments using air temperature and rainfall data from South
America show that our architecture captures spatiotemporal context and that it
outperforms or matches the results of state-of-the-art architectures for
forecasting tasks. In particular, one of the variants of our proposed
architecture is 23% better at predicting future sequences and five times faster
at training than the RNN-based model used as a baseline.
