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Climate & Ocean Physics @ NYU
Climate & Ocean Physics @ NYU
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Pavel Perezhogin

Pavel Perezhogin

Postdoctoral researcher

New York University

Interests
  • M²LInES
  • Momentum closures in ocean
  • Large-eddy simulation

Latest

  • Design and implementation of a data-driven parameterization for mesoscale thickness fluxes
  • Addressing Out-of-Sample Issues in Multi-Layer Convolutional Neural-Network Parameterization of Mesoscale Eddies Applied Near Coastlines
  • Generalizable neural-network parameterization of mesoscale eddies in idealized and global ocean models
  • Learning Machine Learning with Lorenz-96
  • An Analysis of Deep Learning Parameterizations for Ocean Subgrid Eddy Forcing
  • Data-driven dimensionality reduction and causal inference for spatiotemporal climate fields
  • Implementation of a data-driven equation-discovery mesoscale parameterization into an ocean model
  • Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model
  • Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization into a Numerical Ocean Circulation Model
  • Causal inference in spatiotemporal climate fields through linear response theory
  • Benchmarking of machine learning ocean subgrid parameterizations in an idealized model

© 2025 Laure Zanna. This work is licensed under CC BY NC ND 4.0

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