Oceanographic and Atmospheric Processes

Últimos artículos

• HClimRep-Ocean: A Global Ocean Emulator on an Unstructured Mesh

  Kacper Nowak, Aleksei Koldunov, Nikolay Koldunov, Savvas Melidonis, Ankit Patnala, Simon Grasse, Julius Polz, Christian Lessig, Martin Schultz, Thomas Jung · 25 de septiembre de 2026

  Machine-learning (ML) emulators for atmospheric processes have advanced rapidly in recent years, transforming weather forecasting. Although early ML ocean forecasting models now exist, they remain less developed than their atmospheric counterparts. Unlike the atmosphere, much of the ocean's kinetic …

• OceanMoE: Structured Conditional Sparse Computation for Long-Horizon Multivariate Ocean Forecasting

  Yishun Zhu, Jian Wang · 18 de septiembre de 2026

  Multivariate ocean forecasting must exploit shared evolution in a coupled ocean system while adapting to the heterogeneous statistical and dynamical characteristics of different prediction variables and locations. Fully shared models may lack the flexibility to handle this heterogeneity, whereas ful…

• Drift Field Net: Learning Ocean Lagrangian advection fields from in-situ and satellite observations

  Th\'eo Archambault, Pierre Garcia, Mattia Romero, Anastase Charantonis, Dominique B\'er\'eziat · 16 de septiembre de 2026

  The North Pacific Subtropical Gyre (NPSG) is a major accumulation zone for floating plastic debris, resulting from basin-scale convergent ocean circulation. Effective cleanup strategies in this region rely on accurate forecasts of Lagrangian particle drift. Here, we introduce Drift Field Net (DFN), …

• Neptune: An AI model for Global Ocean Subseasonal Prediction

  Davide Donno, Italo Epicoco, Massimo Cafaro, Gabriele Accarino, Mohammad M. Amirian, Viviana Acquaviva, Paola Nassisi, Doroteaciro Iovino, Annalisa Bracco, Simona Masina, Pierre Gentine · 9 de septiembre de 2026

  Subseasonal-to-seasonal (S2S) forecasting is societally critical, supporting decision-making in sectors ranging from water and agricultural management to disaster risk reduction, energy planning, and insurance. Achieving reliable predictions at these timescales requires representing the ocean and it…

• Generative data assimilation highlights fronts as key regulators of ocean energy cascade

  Scott A. Martin, Georgy E. Manucharyan, Patrice Klein · 18 de agosto de 2026

  Mesoscale eddies are fundamental to the ocean circulation, yet the extent to which submesoscale motions, a few kilometers across, influence mesoscale eddy energetics through a kinetic energy cascade remains uncertain. High-resolution simulations predict that submesoscale fronts are key regulators of…

• OceanDepths: A Global Dataset of Paired Subsurface and Surface Ocean Observations

  Simon Donike, Ruben Cartuyvels, Antonino Ian Ferola, Elisa Carli, Diego Fernandez Prieto, Marie-Helene Rio · 18 de agosto de 2026

  Despite comprising over 70% of its surface, the world's oceans are critically underobserved compared to the land surface or the atmosphere. Understanding the global ocean requires jointly observing its surface and subsurface structure, yet no standardized, high-resolution dataset couples satellite s…

• Transferable Dual-Stream Representations for Mesoscale-Preserving Sea Surface Temperature Downscaling

  Parth Doshi, Priyanka Aravindan, Vaishnav Vaidheeswaran, Md Mahbub Alam, Gabriel Spadon · 6 de agosto de 2026

  Deep learning models for scientific spatio-temporal downscaling often minimize reconstruction error while failing to preserve physically meaningful multi-scale structure. For sea surface temperature prediction, this can yield outputs that are numerically plausible yet overly smooth, missing mesoscal…

• Skillful forecasting of offshore winds from satellite scatterometer constellations

  Francesco Pinto, Luca Lanzilao, Paco Lopez Dekker, Angela Meyer · 30 de julio de 2026

  Accurate intraday forecasts of offshore wind are becoming increasingly important for power system operation and the integration of growing shares of offshore wind energy. Operational forecasts rely predominantly on numerical weather prediction (NWP), which is not optimized for lead times of minutes …

• BG4Sea: Biogeochemical Seasonal Forecastability via Progressive Information Scaling

  Gabriela Martinez Balbontin, Anastase Charantonis, Dominique Bereziat, Stefano Ciavatta · 21 de julio de 2026

  Marine biogeochemical forecasting is increasingly important for managing marine ecosystems and the carbon cycle, yet global, seasonal forecast products lag far behind physical oceanography, held back by the complexity of the processes involved and by data scarcity. We introduce BG4Sea, which to our …

• Learning effective Sargassum transport dynamics from limited drifter observations

  F. J. Beron-VEra, M. J. Olascoaga, J. Morell, E. Cruz · 1 de junio de 2026

  Floating-material transport is influenced by unresolved processes that are often absent from available circulation products. We develop a data-driven transport-learning framework for learning effective transport corrections from limited Lagrangian observations using physically motivated ocean--atmos…

• Take It or Leave It: Intent-Controlled Partial Optimal Transport

  Salil Parth Tripathi, Bertrand Chapron, Fabrice Collard, Nicolas Courty, Ronan Fablet · 20 de mayo de 2026

  While optimal transport (OT) enforces a rigid constraint by requiring two measures to be matched exactly, partial optimal transport relaxes this requirement by allowing mass to remain unmatched through a global budget, scalar rebate, or uniform rejection rule. However, many applications call for mor…

• Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting

  Daniel Holmberg, Joel Oskarsson, Erik Larsson, Fredrik Lindsten, Teemu Roos · 18 de mayo de 2026

  Ocean dynamics are inherently chaotic, yet existing machine learning ocean models produce only deterministic forecasts. We introduce Njord, a probabilistic data-driven model for ocean forecasting, applicable to both global and regional domains. Njord combines a deep latent variable framework with a …

• OceanCBM: A Concept Bottleneck Model for Mechanistic Interpretability in Ocean Forecasting

  Sanah Suri, Kieran Ringel, Maike Sonnewald · 14 de mayo de 2026

  Extreme ocean phenomena are challenging not only to predict but to diagnose, as accurate forecasts alone do not reveal the underlying physical drivers. While recent machine learning approaches achieve strong predictive skill, they remain largely opaque and provide limited guarantees of fidelity to g…

• An Adaptive Spatiotemporal Clustering Framework for 3D Ocean Subsurface Temperature Reconstruction

  Ming Shan Loo, Wengen Li, Xudong Jiang, Hailiang Cheng, Zhifei Zhang, Jihong Guan, Yichao Zhang · 5 de mayo de 2026

  The reconstruction of ocean subsurface temperature (OST) using satellite remote sensing data holds significant scientific value for advancing the understanding of ocean dynamics and climate variability. However, the scarcity of subsurface observations, combined with the high degree of nonlinearity a…

• Calibration of a neural network ocean closure for improved mean state and variability

  Pavel Perezhogin, Alistair Adcroft, Laure Zanna · 9 de abril de 2026

  Global ocean models exhibit biases in the mean state and variability, particularly at coarse resolution, where mesoscale eddies are unresolved. To address these biases, parameterization coefficients are typically tuned ad hoc. Here, we formulate parameter tuning as a calibration problem using Ensemb…

• Impact of geophysical fields on Deep Learning-based Lagrangian drift simulations

  Daria Botvynko (Lab-STICC_OSE, IMT Atlantique - MEE, IMT Atlantique), Carlos Granero-Belinchon (ODYSSEY, IMT Atlantique - MEE, Lab-STICC_OSE), Simon Van Gennip (MOi), Abdesslam Benzinou (ENIB), Ronan Fablet (IMT Atlantique - MEE, Lab-STICC_OSE, ODYSSEY) · 7 de abril de 2026

  We assess the influence of different Eulerian geophysical input fields on Lagrangian drift simulations using DriftNet, a learning-based method designed to simulate Lagrangian drift on the sea surface. Two experiments are conducted: a fully numerical experiment (Benchmark B1) and a real-world drifter…

• High-resolution probabilistic estimation of three-dimensional regional ocean dynamics from sparse surface observations

  Niloofar Asefi, Tianning Wu, Ruoying He, Ashesh Chattopadhyay · 6 de abril de 2026

  The ocean interior regulates Earth's climate but remains sparsely observed due to limited in situ measurements, while satellite observations are restricted to the surface. We present a depth-aware generative framework for reconstructing high-resolution three-dimensional ocean states from extremely s…

• Data-driven ensemble prediction of the global ocean

  Qiusheng Huang, Xiaohui Zhong, Anboyu Guo, Ziyi Peng, Lei Chen, Hao Li · 23 de marzo de 2026

  Data-driven models have advanced deterministic ocean forecasting, but extending machine learning to probabilistic global ocean prediction remains an open challenge. Here we introduce FuXi-ONS, the first machine-learning ensemble forecasting system for the global ocean, providing 5-day forecasts on a…

• Ensemble Graph Neural Networks for Probabilistic Sea Surface Temperature Forecasting via Input Perturbations

  Alejandro J. Gonz\'alez-Santana, Giovanny A. Cuervo-Londo\~no, Javier S\'anchez · 9 de marzo de 2026

  Accurate regional ocean forecasting requires models that are both computationally efficient and capable of representing predictive uncertainty. This work investigates ensemble learning strategies for sea surface temperature (SST) forecasting using Graph Neural Networks (GNNs), with a focus on how in…

• Benchmarking Artificial Intelligence Models for Daily Coastal Hypoxia Forecasting

  Magesh Rajasekaran, Md Saiful Sajol, Chris Alvin, Supratik Mukhopadhyay, Yanda Ou, Z. George Xue · 6 de febrero de 2026

  Coastal hypoxia, especially in the northern part of Gulf of Mexico, presents a persistent ecological and economic concern. Seasonal models offer coarse forecasts that miss the fine-scale variability needed for daily, responsive ecosystem management. We present study that compares four deep learning …

• Static and auto-regressive neural emulation of phytoplankton biomass dynamics from physical predictors in the global ocean

  Mahima Lakra, Ronan Fablet, Lucas Drumetz, Etienne Pauthenet, Elodie Martinez · 5 de febrero de 2026

  Phytoplankton is the basis of marine food webs, driving both ecological processes and global biogeochemical cycles. Despite their ecological and climatic significance, accurately simulating phytoplankton dynamics remains a major challenge for biogeochemical numerical models due to limited parameteri…

• BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields

  Rui-Yang Zhang, Henry B. Moss, Lachlan Astfalck, Edward Cripps, David S. Leslie · 3 de febrero de 2026

  We introduce a formal active learning methodology for guiding the placement of Lagrangian observers to infer time-dependent vector fields -- a key task in oceanography, marine science, and ocean engineering -- using a physics-informed spatio-temporal Gaussian process surrogate model. The majority of…

• Eddy-Resolving Global Ocean Forecasting with Multi-Scale Graph Neural Networks

  Yuta Hirabayashi, Daisuke Matusoka, Konobu Kimura · 21 de enero de 2026

  Research on data-driven ocean models has progressed rapidly in recent years; however, the application of these models to global eddy-resolving ocean forecasting remains limited. The accurate representation of ocean dynamics across a wide range of spatial scales remains a major challenge in such appl…

• Neural ocean forecasting from sparse satellite-derived observations: a case-study for SSH dynamics and altimetry data

  Daria Botvynko (Lab-STICC\_OSE, IMT Atlantique - MEE, IMT Atlantique), Pierre Hasl\'ee (Lab-STICC\_OSE, IMT Atlantique - MEE, IMT Atlantique), Lucile Gaultier (ODL), Bertrand Chapron (LOPS), Clement de Boyer Mont\'egut (LOPS), Anass El Aouni (MOi), Julien Le Sommer (IGE), Ronan Fablet (IMT Atlantique - MEE, Lab-STICC\_OSE, ODYSSEY) · 30 de diciembre de 2025

  We present an end-to-end deep learning framework for short-term forecasting of global sea surface dynamics based on sparse satellite altimetry data. Building on two state-of-the-art architectures: U-Net and 4DVarNet, originally developed for image segmentation and spatiotemporal interpolation respec…

• Observation-driven correction of numerical weather prediction for marine winds

  Matteo Peduto, Qidong Yang, Jonathan Giezendanner, Devis Tuia, Sherrie Wang · 4 de diciembre de 2025

  Accurate marine wind forecasts are essential for safe navigation, ship routing, and energy operations, yet they remain challenging because observations over the ocean are sparse, heterogeneous, and temporally variable. We reformulate wind forecasting as observation-informed correction of a global nu…