mlsanityData to 14 Oct 2026

Bridging observation, theory and numerical simulation of the ocean using Machine Learning

arXiv 2104.12506 ↗First posted 26 Apr 2021physics.ao-ph stat.MLcited by 15

Not on the map. Why?

Abstract (arXiv)

Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study. The incorporation of machine learning (ML) techniques offers exciting possibilities for advancing the capacity and speed of established methods and also for making substantial and serendipitous discoveries. Beyond vast amounts of complex data ubiquitous in many modern scientific fields, the study of the ocean poses a combination of unique challenges that ML can help address. The observational data available is largely spatially sparse, limited to the surface, and with few time series spanning more than a handful of decades. Important timescales span seconds to millennia, with strong scale interactions and numerical modelling efforts complicated by details such as coastlines. This review covers the current scientific insight offered by applying ML and points to where there is imminent potential. We cover the main three branches of the field: observations, theory, and numerical modelling. Highlighting both challenges and opportunities, we discuss both the historical context and salient ML tools. We focus on the use of ML in situ sampling and satellite observations, and the extent to which ML applications can advance theoretical oceanographic exploration, as well as aid numerical simulations. Applications that are also covered include model error and bias correction and current and potential use within data assimilation. While not without risk, there is great interest in the potential benefits of oceanographic ML applications; this review caters to this interest within the research community.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 46 of its 283 references that are in the map. First posted on arXiv 26 Apr 2021.

Builds on

46 of 283 references.

References · 46

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2018Neural Ordinary Differential Equationscited by 3,779
  4. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
  5. 2020Shortcut Learning in Deep Neural Networkscited by 1,969
  6. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,436
  7. 2014Deep Learning in Neural Networks: An Overviewcited by 770
  8. 2020Underspecification Presents Challenges for Credibility in Modern Machine Learningcited by 496
  9. 2021Machine learning accelerated computational fluid dynamicscited by 471
  10. 2017Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equationscited by 373
  11. 2020Universal Differential Equations for Scientific Machine Learningcited by 353
  12. 2017Methods for Interpreting and Understanding Deep Neural Networkscited by 345
34 more
  1. 2020WeatherBench: A benchmark dataset for data-driven weather forecastingcited by 275
  2. 2019Towards Physics-informed Deep Learning for Turbulent Flow Predictioncited by 204
  3. 2015A Random Forest Guided Tourcited by 168
  4. 2018Deep learning to represent sub-grid processes in climate modelscited by 156
  5. 2017Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledgecited by 96
  6. 2020Improving data-driven global weather prediction using deep convolutional neural networks on a cubed spherecited by 96
  7. 2018Neural networks for post-processing ensemble weather forecastscited by 95
  8. 2020Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 modelcited by 74
  9. 2020Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Networkcited by 70
  10. 2019Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Predictioncited by 67
  11. 2020Deep Learning for Post-Processing Ensemble Weather Forecastscited by 52
  12. 2019Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variabilitycited by 44
  13. 2020Combining data assimilation and machine learning to infer unresolved scale parametrisationcited by 40
  14. 2018Ensemble Kalman Inversion: A Derivative-Free Technique For Machine Learning Taskscited by 37
  15. 2020Learning Variational Data Assimilation Models and Solverscited by 36
  16. 2018Exascale Deep Learning for Climate Analyticscited by 28
  17. 2017Intercomparison of Machine Learning Methods for Statistical Downscaling: The Case of Daily and Extreme Precipitationcited by 24
  18. 2020TRU-NET: A Deep Learning Approach to High Resolution Prediction of Rainfallcited by 19
  19. 2020Climbing down Charney's ladder: Machine Learning and the post-Dennard era of computational climate sciencecited by 18
  20. 2020A Fortran-Keras Deep Learning Bridge for Scientific Computingcited by 16
  21. 2019Applying machine learning to improve simulations of a chaotic dynamical system using empirical error correctioncited by 15
  22. 2021Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilationcited by 15
  23. 2017Machine Learning, Deepest Learning: Statistical Data Assimilation Problemscited by 13
  24. 2018Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learningcited by 13
  25. 2020Analog ensemble data assimilation and a method for constructing analogs with variational autoencoderscited by 12
  26. 2020Machine-Learned Preconditioners for Linear Solvers in Geophysical Fluid Flowscited by 11
  27. 2020Data Assimilation in the Latent Space of a Neural Networkcited by 9
  28. 2020Evaluation, Tuning and Interpretation of Neural Networks for Meteorological Applicationscited by 7
  29. 2021Using Machine Learning at Scale in HPC Simulations with SmartSim: An Application to Ocean Climate Modelingcited by 7
  30. 2017Bilinear residual Neural Network for the identification and forecasting of dynamical systemscited by 5
  31. 2020Machine learning for total cloud cover predictioncited by 4
  32. 2019A parallel Fortran framework for neural networks and deep learningcited by 3
  33. 2019Learning Generalized Quasi-Geostrophic Models Using Deep Neural Numerical Modelscited by 3
  34. 2013Modern Statistical Methods in Oceanography: A Hierarchical Perspectivecited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 6; 2024: 2; 2025: 3; 2026: 2.

15 papers.

Sort:

By area:

  1. 2023AI-GOMS: Large AI-Driven Global Ocean Modeling Systemcited by 27
  2. 2022Dimensionally Consistent Learning with Buckingham Picited by 22
  3. 2023When Geoscience Meets Foundation Models: Towards General Geoscience Artificial Intelligence Systemcited by 13
  4. 2023Gradient-free online learning of subgrid-scale dynamics with neural emulatorscited by 5
  5. 2023Multi-fidelity climate model parameterization for better generalization and extrapolationcited by 4
  6. 2023OceanBench: The Sea Surface Height Editioncited by 4
  7. 2023On the choice of training data for machine learning of geostrophic mesoscale turbulencecited by 2
  8. 2022Semi-automatic tuning of coupled climate models with multiple intrinsic timescales: lessons learned from the Lorenz96 modelcited by 1
  9. 2026OceanCBM: A Concept Bottleneck Model for Mechanistic Interpretability in Ocean Forecastingcited by 1
  10. 2024The Importance of Architecture Choice in Deep Learning for Climate Applicationscited by 0
  11. 2024A Moonshot for AI Oracles in the Sciencescited by 0
  12. 2025Hybrid machine learning data assimilation for marine biogeochemistrycited by 0
3 more
  1. 2025Probabilistic Emulation of the Community Radiative Transfer Model Using Machine Learningcited by 0
  2. 2025Machine Learning Algorithms in Statistical Modelling Bridging Theory and Applicationcited by 0
  3. 2026Weak Dominant Balance for Robust Identification of Dynamically Consistent Fluid Flow Structurecited by 0