Spatiotemporal neural network with attention mechanism for El Niño forecasts

Jinah Kim, Minho Kwon, Sung Dae Kim, Jong Seong Kug, Joon Gyu Ryu, Jaeil Kim

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

To learn spatiotemporal representations and anomaly predictions from geophysical data, we propose STANet, a spatiotemporal neural network with a trainable attention mechanism, and apply it to El Niño predictions for long-lead forecasts. The STANet makes two critical architectural improvements: it learns spatial features globally by expanding the network’s receptive field and encodes long-term sequential features with visual attention using a stateful long-short term memory network. The STANet conducts multitask learning of Nino3.4 index prediction and calendar month classification for predicted indices. In a comparison of the proposed STANet performance with the state-of-the-art model, the accuracy of the 12-month forecast lead correlation coefficient was improved by 5.8% and 13% for Nino3.4 index prediction and corresponding temporal classification, respectively. Furthermore, the spatially attentive regions for the strong El Niño events displayed spatial relationships consistent with the revealed precursor for El Niño occurrence, indicating that the proposed STANet provides good understanding of the spatiotemporal behavior of global sea surface temperature and oceanic heat content for El Niño evolution.

Original languageEnglish
Article number7204
JournalScientific Reports
Volume12
Issue number1
DOIs
StatePublished - Dec 2022

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