Subseasonal to seasonal (S2S) prediction still remains as a challenge task even today.
S2S prediction refers to a lead time ranging from one to several months, which is
known to result in rapidly decreasing forecast accuracy. Deep learning (DL), one of the
most powerful statistical models, is expected to overcome current difficulty of S2S
prediction because of its spatio-temporal locality. In this study, one-month rainfall
prediction is carried out for the Chao Phraya River basin in Thailand during the rainy
season from May to October, where the introduction of long-term rainfall prediction
to dam management is urgently required for flood mitigation. The DL model is
constructed using global maps of sea surface temperature and heat content as input
values. 17 climate models output from CMIP5 dataset is used to expand the training
data from 65 existing observations to 2500, which enables us to train the DL model
proper. The prediction accuracy of the DL model is compared with that of the physical
model and the linear regression model. The results show that the DL model is slightly
inferior to the physical model, but has higher prediction accuracy than the linear
model. In addition, the deep learning model has the highest prediction accuracy
when trained only on the CMIP5 dataset, indicating that the DL model has potential
to outperform the physical model in the future.
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