Fig. 4: Architecture of CTEFNet for ENSO predictions.
From: Toward long-range ENSO prediction with an explainable deep learning model
CTEFNet consists of an input layer, a CNN-based feature extractor, a Transformer spatiotemporal analysis module, two fully connected layers, and an output layer. The input predictors include SST, HC, MLD, SSS, SLP, UO, VO, TAUU, and TAUV anomaly fields, all spanning 12 consecutive months in the region defined by (60°S–60°N, 0°–360°E). The Niño 3.4 index for the subsequent 24 months serves as the predictands for supervised training.
