Résumé: In terms of SST prediction tasks by machine learning approaches, high-resolution and accurate prediction can hardly be achieved with easily accessible SST data. This is because most of the SST data is spatiotemporally discrete and sparse, meaning a small sample size since it is hard to learn the spatiotemporal correlation given the data of limited density. This paper presents a numerical computation-based few-shot learning method for intelligent SST prediction. The proposed method generates synthetic SST sequences via ROMS and merges them with SST data captured by satellites to deal with small sample size. Convolutional LSTM (Conv-LSTM) network is trained end-to-end in order to learn spatiotemporal correlation of time-varying SST and eventually obtain high-resolution and accurate SST predictions. SST data from August to December in 2011 are employed for model training, and the prediction results are compared to reliable SST reanalysis datasets in the experiment, which shows fine performance of the proposed method.

Langue: Anglais