  Short-term precipitation forecasting is essential for planning of human
activities in multiple scales, ranging from individuals' planning, urban
management to flood prevention. Yet the short-term atmospheric dynamics are
highly nonlinear that it cannot be easily captured with classical time series
models. On the other hand, deep learning models are good at learning nonlinear
interactions, but they are not designed to deal with the seasonality in time
series. In this study, we aim to develop a forecasting model that can both
handle the nonlinearities and detect the seasonality hidden within the daily
precipitation data. To this end, we propose a seasonally-integrated autoencoder
(SSAE) consisting of two long short-term memory (LSTM) autoencoders: one for
learning short-term dynamics, and the other for learning the seasonality in the
time series. Our experimental results show that not only does the SSAE
outperform various time series models regardless of the climate type, but it
also has low output variance compared to other deep learning models. The
results also show that the seasonal component of the SSAE helped improve the
correlation between the forecast and the actual values from 4% at horizon 1 to
37% at horizon 3.
