Advancing oceanographic forecasts in the Bay of Bengal: an AI-based spatiotemporal prediction for SST and MLD

Hybrid U-Net and ConvLSTM-based spatial forecasting of marine heatwave intensity and occurrence probability

Overall, the hybrid deep learning framework demonstrated robust and stable performance in representing both the intensity and probability of MHW events up to a seven-day forecasting horizon.

Subseasonal predictability of the Bay of Bengal summer monsoon onset tied to three-ocean sea surface temperature anomalies

Deep Learning-Based Statistical Downscaling of Sea Surface Temperature Using a Residual Corrective Neural Network

This study proposes a novel deep learning framework that uses a U-Net to generate an initial high-resolution SST estimate, which is subsequently refined using a residual corrective approach, and progressively refines initial U-Net predictions by incorporating dynamically scaled residuals at each step, enabling accurate...

A Deep Learning Model for Decadal Indian Ocean Dipole

A deep learning model based on a bidirectional gated recurrent unit (BiGRU) to enhance its predictive skill is developed and used to improve the prediction skill of the IOD‐related Australian rainfall, underscoring the reliability of the BiGRU model for both IOD and related precipitation forecasts.

The Importance of Subsurface Thermal Effects to Indian Ocean Dipole Prediction Revealed by a Skillful Three-Dimensional Deep Learning Model

Deep learning for multi-step forecasting of stratospheric wind and temperature profiles over the Tibetan plateau

The MTPV-HDRNet model is proposed as a fixed-site, multi-level intelligent forecasting model driven by ECMWF ERA5 pressure-level reanalysis data, adopting a hybrid encoder–decoder architecture that explicitly represents temporal evolution and vertical stratification in a decoupled but complementary manner.