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

Marine Heatwaves (MHW) are extreme sea surface temperature events that significantly affect marine ecosystems, fisheries, and coral reef environments, highlighting the need for accurate forecasting systems in vulnerable regions such as the Lesser Sunda Islands. This study aims to develop a hybrid deep learning-based MHW prediction system by integrating U-Net and ConvLSTM models over the regions of Bali, West Nusa Tenggara, and East Nusa Tenggara. The datasets used consist of NOAA OISST V2.1 daily sea surface temperature data for the period 1985–2024, along with Niño 3.4 and Dipole Mode Index (DMI) atmospheric indices. The U-Net model was applied to predict Sea Surface Temperature Anomaly (SSTA) intensity, while ConvLSTM was used to estimate the probability of MHW occurrence. The results demonstrate that the best forecasting accuracies of the U-Net intensity model for 1-, 3-, 5-, and 7-day lead times reached 0.8896, 0.8597, 0.8462, and 0.8674, respectively, with optimal thresholds of 0.92 °C, 0.56 °C, 0.51 °C, and 0.31 °C. The ConvLSTM probability model produced maximum FAR values of 0.9101, 0.8962, 0.8941, and 0.8854 with optimal probability thresholds of 0.25, 0.1, 0.1, and 0.1. RMSE evaluation increased gradually from 0.27 °C on day-1 to 0.94 °C on day-7 forecasts. 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 capture of broad patterns and fine-grained features such as eddies and fronts.

A Deep Learning Model for Decadal Indian Ocean Dipole

The rapid and unprecedented warming of the Indian Ocean intensifies climate risks across socio‐economically vulnerable regions, highlighting the need of accurate prediction over the coming decade. This study first assesses the decadal predictability of the Indian Ocean Dipole (IOD) using the Decadal Climate Prediction Project (DCPP) models of Coupled Model Intercomparison Project Phase 6 (CMIP6), then develops a deep learning model based on a bidirectional gated recurrent unit (BiGRU) to enhance its predictive skill. The BiGRU model is designed to process multisource sequential data and is trained on the IOD index derived from DCPP simulations. Results show the MME achieves moderate skill on detrended IOD with an anomaly correlation coefficient (ACC) and mean squared skill score (MSSS) of 0.51 and 0.20 during 1963–2020, respectively. The BiGRU model improved the skill with an anomaly correlation coefficient (ACC) of 0.88 and a mean squared skill score (MSSS) of 0.72 during the testing period of 2009–2020, and accurately captured extreme IOD events. Based on the enhanced IOD index, we further used it to improve the prediction skill of the IOD‐related Australian rainfall, achieving ACC and MSSS values of 0.90 and 0.70 during 2009–2020, compared to 0.29 and −1.62 from the MME, 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.