Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 11339-11350· 0 citations· 29 references
Abstract
Regional high-resolution ocean environmental forecasting combines spatial numerical modeling with temporal prediction, and is essential for monitoring the ecological security of specific ocean regions. In recent years, deep learning methods are generally more computationally efficient than traditional numerical models and enable fast, accurate forecasting. However, as data resolution increases, the training and computational costs of existing approaches increase substantially. To address this issue, we introduce Slow-OCast, a transfer-learning based model designed for high-resolution ocean environmental forecasting. Specifically, Slow-OCast incorporates the slow-varying motion characteristics of the ocean and comprises two insightful modules. The Fluid Motion Separator that injects low-frequency background dynamics into the fine-tuning process of a foundation model, functioning as a "magnifier" to encode physical priors of ocean dynamics. The Hydrokinetic Energy Path Integrator that provides an implicit representation of flow-field evolution, serving as a "compass" to guide accurate change prediction. We evaluate Slow-OCast on two high-resolution Mediterranean datasets, and results demonstrate Slow-OCast consistently outperforms all baseline methods across forecasting tasks with different lead times.
Accurate multi-day forecasting of floating-object trajectories on the ocean surface is critical for applications ranging from search-and-rescue to environmental tracking. This task remains however challenging due to the complex interplay of influencing factors such as ocean currents and winds. In this work, we frame trajectory prediction as a denoising task and present Conditional Diffusion models for Trajectories (CoDiT), which adapts the denoising diffusion framework, originally developed for image synthesis, to the problem of trajectory forecasting. CoDiT generates realistic trajectory forecasts, conditioned on heterogeneous context data: ocean currents and winds from reanalysis products, bathymetry, and the initial position. We train and evaluate CoDiT on two global, specialized datasets focusing on the open ocean and coastal regions, using GPS trajectories from the Global Drifter Program as ground truth. We compare CoDiT rigorously against various baselines, including a convolutional neural network that predicts velocity fields, and physical forecasts generated directly from the current and wind fields. Quantitative evaluations show that CoDiT achieves the lowest position error across both datasets and all forecast horizons, and the best probabilistic forecast quality among all methods, as measured by the energy score. Notably, in the coastal setting, CoDiT is the only method to surpass the naive persistence baseline in position error.
Christian Donner, Shirin Goshtasbpour, Emanuele Dalsasso et al.· Machine Learning: Earth· 0 citations
Ocean forecasting is crucial for both scientific research and societal benefits. Large artificial intelligence (AI)-based models have recently boosted forecasting efficiency and accuracy. However, it remains challenging to develop a comprehensive AI-driven ocean forecasting system capable of integrating cross-spatiotemporal and atmospheric forcing. This study introduces LangYa, a cross-spatiotemporal and atmospheric forcing ocean forecasting system featuring: (1) a large-language-model-based (LLM-based) time embedding to explicitly represent forecast lead times, (2) an asynchronous cross-iterative random sampling strategy to represent the impacts of atmospheric forcing on ocean processes, (3) an ocean self-attention module to enhance network stability and accelerate training convergence, and (4) an adaptive loss function to capture ocean dynamics in the thermocline, at depths ranging from tens of meters to about 300 m. LangYa is trained on 27 years of global ocean data from the Global Ocean Reanalysis and Simulation, version 12 (GLORYS12). Using reanalysis and observational data, compared to existing open-source AI-based forecasting systems and numerical models, LangYa enables a single model to produce forecasts with lead times of 1 to 7 d (1/12°, daily) and achieves 7 d RMSEs below 0.0736 m/s, 0.0701 m/s, 0.4376 ℃, and 0.1302 psu for global currents, temperature, and salinity respectively. These quantitative results indicate that LangYa provides clear advantages in forecast accuracy, lead-time robustness, and stability for global OSV forecasting, demonstrating its potential for real-time operational deployment.
Nan Yang, Chong Wang, Zi-Meng Zhao et al.· Science Bulletin· 0 citations
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.
Onkar Jadhav, Tim French, I. Janeković et al.· 1 citation
Inferring ocean dynamics remains challenging due to the complexity of multiscale nonlinear interactions, sparsity of observations, and limitations of numerical models. Deep learning (DL) has emerged as a powerful data-driven framework that exploits nonlinear dependencies across heterogeneous datasets, complementing traditional approaches. Here, we review recent progress in applying DL to ocean dynamics, including spatiotemporal interpolation of satellite and in situ data, estimation of unobserved ocean variables, neural data assimilation, ocean forecasting, and the development of eddy parameterizations for hybrid models. DL methods have excelled at fusing multimodal observations, reconstructing multiscale fields, learning complex distributions, and providing computationally efficient predictions that often match or exceed those of conventional statistical approaches. Despite the rapid methodological progress, opportunities remain in uncertainty quantification, ensuring physical consistency, generalization to unseen conditions, and leveraging DL beyond obtaining operational gains to advance scientific discovery and human-level understanding of ocean dynamics.
G. Manucharyan, Scott A. Martin, D. Balwada et al.· Annual Review of Marine Scie...· 0 citations
OceanBench is a benchmark designed to evaluate and accelerate global short-range data-driven ocean forecasting, constructed from a curated dataset comprising first-guess trajectories, nowcasts, and atmospheric forcings from operational physical ocean models, typically unavailable in public datasets due to assimilation cycles.
A. El Aouni, Quentin Gaudel, J. E. Johnson et al.· Neural Information Processin...· 7 citations· ⚡2
4DVarGen is proposed, a 4DVar-inspired generative framework for reconstructing sea surface variable fields at eddy-resolving scales from sparse remote-sensing observations that establishes a mathematical equivalence between 4DVar and an observation-guided denoising process.
Jun-Peng Huang, Wu-Xin Wang, Xiao-Yong Li et al.· Proceedings of the Thirty-Fi...· 0 citations
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