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Open access
Aug 2026
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.
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· Atmospheric Science Letters · 0 citations
Aug 2026
Advancing oceanographic forecasts in the Bay of Bengal: an AI-based spatiotemporal prediction for SST and MLD
A deep-learning model based on ConvLSTM to forecast SST and MLD in the Bay of Bengal for up to four weeks ahead contributes to improved ocean variable forecasting and demonstrates potential for ISMR prediction and disaster management.
B. Kumar, Rahul Kumar, B. Gayen et al.
· Proceedings of the Indian Ac... · 0 citations
Preprint
Aug 2026
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...
Onkar Jadhav, Tim French, I. Janeković et al.
· 1 citation
Review
Open access
Aug 2026
Deep learning techniques for extreme rainfall prediction: a review of state-of-the-art
The analysis reveals a transition from deterministic DI models to hybrid, physics-informed, uncertainty-aware, and operational forecasting systems that increasingly integrate AI with physical knowledge and heterogeneous environmental observations.
Braiton U. Mukhalela, S. Viriri, D. Ndzi et al.
· Frontiers in Artificial Inte... · 0 citations
Open access
Aug 2026
Improving simulation of Earth system variability through weakly coupled ocean data assimilation in E3SM
Abstract. Accurate initialization of ocean states is essential for skillful prediction of Earth system variability across seasonal-to-decadal timescales. In this study, we evaluate the impact of a newly developed four-dimensional ensemble variational (4DEnVar)-based weakly coupled ocean data assimilation (WCODA) system...
Peng-Fei Shi, L. R. Leung, Zhao-Xia Pu et al.
· Geoscientific Model Developm... · 0 citations
Open access
Sep 2026
Rapid Joint Downscaling of Multiple Atmospheric Fields to Kilometer Scale With Deep Learning
Predicting weather and climate hazards typically relies on computationally expensive kilometer‐scale numerical models. This study introduces a U‐Net‐based deep learning framework, the Joint Atmospheric fields Downscaling Network (JADNet), for rapid, joint downscaling of multiple atmospheric variables to kilometer res...
Hong-Xing Cui, H. Dasari, S. Sanikommu et al.
· Journal of Geophysical Resea... · 0 citations