Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 59 tok/s
Gemini 2.5 Pro 52 tok/s Pro
GPT-5 Medium 40 tok/s Pro
GPT-5 High 27 tok/s Pro
GPT-4o 104 tok/s Pro
Kimi K2 195 tok/s Pro
GPT OSS 120B 467 tok/s Pro
Claude Sonnet 4 37 tok/s Pro
2000 character limit reached

Parametric Sensitivities of a Wind-driven Baroclinic Ocean Using Neural Surrogates (2404.09950v1)

Published 15 Apr 2024 in physics.ao-ph

Abstract: Numerical models of the ocean and ice sheets are crucial for understanding and simulating the impact of greenhouse gases on the global climate. Oceanic processes affect phenomena such as hurricanes, extreme precipitation, and droughts. Ocean models rely on subgrid-scale parameterizations that require calibration and often significantly affect model skill. When model sensitivities to parameters can be computed by using approaches such as automatic differentiation, they can be used for such calibration toward reducing the misfit between model output and data. Because the SOMA model code is challenging to differentiate, we have created neural network-based surrogates for estimating the sensitivity of the ocean model to model parameters. We first generated perturbed parameter ensemble data for an idealized ocean model and trained three surrogate neural network models. The neural surrogates accurately predicted the one-step forward ocean dynamics, of which we then computed the parametric sensitivity.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (46)
  1. [n. d.]. ocean/soma test group. https://mpas-dev.github.io/compass/latest/users_guide/ocean/test_groups/soma.html. Accessed: 2023-11-29.
  2. Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.15 User’s Manual. (11 2021). https://doi.org/10.2172/1829573
  3. Automatic Differentiation in Machine Learning: A Survey. J. Mach. Learn. Res. 18, 1 (Jan. 2017), 5595–5637.
  4. Mickael Binois and Nathan Wycoff. 2022. A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization. ACM Transactions on Evolutionary Learning and Optimization 2, 2 (2022), 1–26.
  5. Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere. arXiv preprint arXiv:2306.03838 (2023).
  6. Adjoint-Matching Neural Network Surrogates for Fast 4D-Var Data Assimilation. CoRR abs/2111.08626 (2021). https://arxiv.org/abs/2111.08626
  7. Challenges of Modeling Ocean Basin Ecosystems. Science 304, 5676 (2004), 1463–1466. https://doi.org/10.1126/science.1094858
  8. Ronald M Errico and Tomislava Vukicevic. 1992. Sensitivity analysis using an adjoint of the PSU-NCAR mesoseale model. Monthly Weather Review 120, 8 (1992), 1644–1660. https://doi.org/10.1175/1520-0493(1992)120<1644:SAUAAO>2.0.CO;2
  9. Estimating Eddy Stresses by Fitting Dynamics to Observations Using a Residual-Mean Ocean Circulation Model and Its Adjoint. Journal of Physical Oceanography 35, 10 (2005), 1891 – 1910. https://doi.org/10.1175/JPO2785.1
  10. Peter R. Gent and James C. Mcwilliams. 1990. Isopycnal Mixing in Ocean Circulation Models. Journal of Physical Oceanography 20, 1 (1990), 150–155. https://doi.org/10.1175/1520-0485(1990)020<0150:IMIOCM>2.0.CO;2
  11. Parameterizing Eddy-Induced Tracer Transports in Ocean Circulation Models. Journal of Physical Oceanography 25, 4 (1995), 463 – 474. https://doi.org/10.1175/1520-0485(1995)025<0463:PEITTI>2.0.CO;2
  12. The DOE E3SM Coupled Model Version 1: Overview and Evaluation at Standard Resolution. Journal of Advances in Modeling Earth Systems 11, 7 (2019), 2089–2129. https://doi.org/10.1029/2018MS001603
  13. Andreas Griewank and Andrea Walther. 2008. Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation (2nd ed.). Number 105 in Other Titles in Applied Mathematics. SIAM, Philadelphia, PA. http://bookstore.siam.org/ot105/
  14. Building Tangent-Linear and Adjoint Models for Data Assimilation With Neural Networks. Journal of Advances in Modeling Earth Systems 13, 9 (2021), e2021MS002521. https://doi.org/10.1029/2021MS002521
  15. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778.
  16. Takeyuki Hida and Masuyuki Hitsuda. 1993. Gaussian processes. Vol. 120. American Mathematical Soc.
  17. Kurt Hornik. 1991. Approximation capabilities of multilayer feedforward networks. Neural networks 4, 2 (1991), 251–257.
  18. Understanding Automatic Differentiation Pitfalls. arXiv:2305.07546 [math.NA]
  19. IPCC. 2021. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Vol. In Press. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. https://doi.org/10.1017/9781009157896
  20. Physics-informed machine learning. Nature Reviews Physics 3, 6 (2021), 422–440. https://doi.org/10.1038/s42254-021-00314-5
  21. Diederik P. Kingma and Jimmy Ba. 2017. Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs.LG]
  22. Tamara G Kolda and Brett W Bader. 2009. Tensor decompositions and applications. SIAM Rev. 51, 3 (2009), 455–500.
  23. Multi-Grid Tensorized Fourier Neural Operator for High Resolution PDEs. https://openreview.net/forum?id=po-oqRst4Xm
  24. Fourier Neural Operator for Parametric Partial Differential Equations. arXiv:2010.08895 [cs.LG]
  25. Fourier neural operator approach to large eddy simulation of three-dimensional turbulence. Theoretical and Applied Mechanics Letters 12, 6 (2022), 100389.
  26. Physics-Informed Neural Operator for Learning Partial Differential Equations. https://doi.org/10.48550/arXiv.2111.03794
  27. Predicting parametric spatiotemporal dynamics by multi-resolution PDE structure-preserved deep learning. arXiv:2205.03990 [cs.LG]
  28. Adjoint-Based Climate Model Tuning: Application to the Planet Simulator. Journal of Advances in Modeling Earth Systems 10, 1 (2018), 207–222. https://doi.org/10.1002/2017MS001194 arXiv:https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2017MS001194
  29. Fluid Control Using the Adjoint Method. ACM Trans. Graph. 23, 3 (aug 2004), 449––456. https://doi.org/10.1145/1015706.1015744
  30. ClimaX: A foundation model for weather and climate. arXiv:2301.10343 [cs] http://arxiv.org/abs/2301.10343 version: 1.
  31. ClimaX: A foundation model for weather and climate. arXiv preprint arXiv:2301.10343 (2023).
  32. R. C. Pacanowski and S. G. H. Philander. 1981. Parameterization of Vertical Mixing in Numerical Models of Tropical Oceans. Journal of Physical Oceanography 11, 11 (1981), 1443–1451. https://doi.org/10.1175/1520-0485(1981)011<1443:POVMIN>2.0.CO;2
  33. PyTorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems 32 (2019).
  34. FourCastNet: A global data-driven high-resolution weather model using adaptive Fourier neural operators. arXiv preprint arXiv:2202.11214 (2022).
  35. An Evaluation of the Ocean and Sea Ice Climate of E3SM using MPAS and Interannual CORE-II Forcing. Journal of Advances in Modeling Earth Systems 11, 5 (2019), 1438–1458. https://doi.org/10.1029/2018MS001373 arXiv:https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2018MS001373
  36. Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations. https://doi.org/10.48550/arXiv.1711.10561 arXiv:1711.10561 [cs, math, stat]
  37. Learning the stress-strain fields in digital composites using Fourier neural operator. Iscience 25, 11 (2022).
  38. Martha H. Redi. 1982. Oceanic Isopycnal Mixing by Coordinate Rotation. Journal of Physical Oceanography 12, 10 (1982), 1154–1158. https://doi.org/10.1175/1520-0485(1982)012<1154:OIMBCR>2.0.CO;2
  39. A multi-resolution approach to global ocean modeling. Ocean Modelling 69 (2013), 211–232. https://doi.org/10.1016/j.ocemod.2013.04.010
  40. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer, 234–241.
  41. Albert J. Semtner. 1995. Modeling Ocean Circulation. Science 269, 5229 (1995), 1379–1385. https://doi.org/10.1126/science.269.5229.1379
  42. DeepGraphONet: A deep graph operator network to learn and zero-shot transfer the dynamic response of networked systems. IEEE Systems Journal (2023).
  43. Scientific machine learning benchmarks. Nature Reviews Physics 4, 6 (2022), 413–420.
  44. Analysis of anomaly correlations. Geophysics 62, 1 (1997), 342–351.
  45. Diagnosing Isopycnal Diffusivity in an Eddying, Idealized Midlatitude Ocean Basin via Lagrangian, in Situ, Global, High-Performance Particle Tracking (LIGHT). Journal of Physical Oceanography 45, 8 (2015), 2114–2133. https://doi.org/10.1175/JPO-D-14-0260.1
  46. Underestimated AMOC Variability and Implications for AMV and Predictability in CMIP Models. Geophysical Research Letters 45, 9 (2018), 4319–4328. https://doi.org/10.1029/2018GL077378
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
Dice Question Streamline Icon: https://streamlinehq.com

Follow-Up Questions

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets