Cover Image

Interpretable structural model error discovery from sparse assimilation increments using spectral bias-reduced neural networks: A quasi-geostrophic turbulence test case

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Mojgani, Rambod, Chattopadhyay, Ashesh, Hassanzadeh, Pedram
Format:	Preprint
Published:	2023
Subjects:	Computational Physics Atmospheric and Oceanic Physics Data Analysis, Statistics and Probability Computation
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers
by: Chattopadhyay, Ashesh, et al.
Published: (2021)

Fourier analysis of the physics of transfer learning for data-driven subgrid-scale models of ocean turbulence
by: Darman, Moein, et al.
Published: (2025)

Deep learning the sources of MJO predictability: a spectral view of learned features
by: Yao, Lin, et al.
Published: (2025)

Can AI weather models predict out-of-distribution gray swan tropical cyclones?
by: Sun, Y. Qiang, et al.
Published: (2024)

Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges
by: Jakhar, Karan, et al.
Published: (2023)

Machine Learning for the Physics of Climate
by: Bracco, Annalisa, et al.
Published: (2024)

LUCIE: A Lightweight Uncoupled ClImate Emulator with long-term stability and physical consistency for O(1000)-member ensembles
by: Guan, Haiwen, et al.
Published: (2024)

LUCIE-3D: A three-dimensional climate emulator for forced responses
by: Guan, Haiwen, et al.
Published: (2025)

Challenges of learning multi-scale dynamics with AI weather models: Implications for stability and one solution
by: Chattopadhyay, Ashesh, et al.
Published: (2023)

Tailoring data assimilation to discontinuous Galerkin models
by: Pasmans, Ivo, et al.
Published: (2023)

Physics-informed neural networks for tsunami inundation modeling
by: Brecht, Rüdiger, et al.
Published: (2024)

A unified neural background-error covariance model for midlatitude and tropical atmospheric data assimilation
by: Melinc, Boštjan, et al.
Published: (2025)

A neural network-based observation operator for weather radar data assimilation
by: Stefanelli, Marco, et al.
Published: (2025)

Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime
by: Pahlavan, Hamid A., et al.
Published: (2023)

Lazy Diffusion: Mitigating spectral collapse in generative diffusion-based stable autoregressive emulation of turbulent flows
by: Sambamurthy, Anish, et al.
Published: (2025)

Atmospheric chemistry surrogate modeling with sparse identification of nonlinear dynamics
by: Yang, Xiaokai, et al.
Published: (2024)

Predicting Beyond Training Data via Extrapolation versus Translocation: AI Weather Models and Dubai's Unprecedented 2024 Rainfall
by: Sun, Y. Qiang, et al.
Published: (2025)

An Analytical and AI-discovered Stable, Accurate, and Generalizable Subgrid-scale Closure for Geophysical Turbulence
by: Jakhar, Karan, et al.
Published: (2025)

AI-boosted rare event sampling to characterize extreme weather
by: Lancelin, Amaury, et al.
Published: (2025)

Adjoint-based online learning of two-layer quasi-geostrophic baroclinic turbulence
by: Yan, Fei Er, et al.
Published: (2024)

Benchmarking Regional Thermodynamic Trends in an AI emulator, ACE2, and a hybrid model, NeuralGCM
by: Rucker, Katharine, et al.
Published: (2025)

Enhanced parallelization of the incremental 4D-Var data assimilation algorithm using the Randomized Incremental Optimal Technique (RIOT)
by: Bousserez, Nicolas, et al.
Published: (2019)

Critical latitude in global quasi-geostrophic flow on a rotating sphere
by: Franken, Arnout, et al.
Published: (2024)

Hybrid ensemble forecasting combining physics-based and machine-learning predictions through spectral nudging
by: Polichtchouk, Inna, et al.
Published: (2026)

On the importance of learning non-local dynamics for stable data-driven climate modeling: A 1D gravity wave-QBO testbed
by: Pahlavan, Hamid A., et al.
Published: (2024)

Application of a spectral scheme to simulate horizontally slowly varying three-dimensional ocean acoustic propagation
by: Tu, Houwang, et al.
Published: (2024)

Redox evolution of the crystallizing terrestrial magma ocean and its influence on atmosphere outgassing
by: Maurice, Maxime, et al.
Published: (2023)

Forecast error diagnostics in neural weather models
by: Perkan, Uros, et al.
Published: (2025)

Quantitatively mapping the Eady model onto a two-layer quasi-geostrophic model
by: Meunier, Julie, et al.
Published: (2025)

Calibration of a neural network ocean closure for improved mean state and variability
by: Perezhogin, Pavel, et al.
Published: (2026)

High-resolution probabilistic estimation of three-dimensional regional ocean dynamics from sparse surface observations
by: Asefi, Niloofar, et al.
Published: (2026)

Machine learning for climate physics and simulations
by: Lai, Ching-Yao, et al.
Published: (2024)

Benchmarking atmospheric circulation variability in an AI emulator, ACE2, and a hybrid model, NeuralGCM
by: Baxter, Ian, et al.
Published: (2025)

MieAI: A neural network for calculating optical properties of internally mixed aerosol in atmospheric models
by: Kumar, Pankaj, et al.
Published: (2023)

Nonlocal gravity wave turbulence in presence of condensate
by: Korotkevich, A. O., et al.
Published: (2023)

Reconstruction of Antarctic sea ice thickness from sparse satellite laser altimetry data using a partial convolutional neural network
by: Ma, Ziqi, et al.
Published: (2025)

Using data assimilation tools to dissect GraphDOP
by: Laloyaux, Patrick, et al.
Published: (2025)

Coupling between sub-mesoscale eddies, internal waves, and turbulence in the deep Mediterranean: A spectral investigation
by: van Haren, Hans
Published: (2026)

AI-based data assimilation: Learning the functional of analysis estimation
by: Keller, Jan D., et al.
Published: (2024)

Estimation of temperature and precipitation uncertainties using quantile neural networks
by: Brettin, Andrew, et al.
Published: (2026)