Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows
Abstract
In the past couple of years, there has been a proliferation in the use of machine learning approaches to represent subgrid-scale processes in geophysical flows with an aim to improve the forecasting capability and to accelerate numerical simulations of these flows. Despite its success for different types of flow, the online deployment of a data-driven closure model can cause instabilities and biases in modeling the overall effect of subgrid-scale processes, which in turn leads to inaccurate prediction. To tackle this issue, we exploit the data assimilation technique to correct the physics-based model coupled with the neural network as a surrogate for unresolved flow dynamics in multiscale systems. In particular, we use a set of neural network architectures to learn the correlation between resolved flow variables and the parametrizations of unresolved flow dynamics and formulate a data assimilation approach to correct the hybrid model during their online deployment. We illustrate our framework in a set of applications of the multiscale Lorenz 96 system for which the parametrization model for unresolved scales is exactly known, and the two-dimensional Kraichnan turbulence system for which the parametrization model for unresolved scales is not known a priori. Our analysis, therefore, comprises a predictive dynamical coremore »
- Authors:
-
- Oklahoma State Univ., Stillwater, OK (United States)
- Publication Date:
- Research Org.:
- Oklahoma State Univ., Stillwater, OK (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1853026
- Grant/Contract Number:
- SC0019290
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Physical Review Fluids (Online)
- Additional Journal Information:
- Journal Name: Physical Review Fluids (Online); Journal Volume: 6; Journal Issue: 5; Journal ID: ISSN 2469-990X
- Publisher:
- American Physical Society (APS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING; Physics; Chaos; Control & applications of chaos; Geophysical fluid dynamics; Mixing in geophysical flows; Spatiotemporal chaos
Citation Formats
Pawar, Suraj, and San, Omer. Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows. United States: N. p., 2021.
Web. doi:10.1103/physrevfluids.6.050501.
Pawar, Suraj, & San, Omer. Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows. United States. https://doi.org/10.1103/physrevfluids.6.050501
Pawar, Suraj, and San, Omer. Wed .
"Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows". United States. https://doi.org/10.1103/physrevfluids.6.050501. https://www.osti.gov/servlets/purl/1853026.
@article{osti_1853026,
title = {Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows},
author = {Pawar, Suraj and San, Omer},
abstractNote = {In the past couple of years, there has been a proliferation in the use of machine learning approaches to represent subgrid-scale processes in geophysical flows with an aim to improve the forecasting capability and to accelerate numerical simulations of these flows. Despite its success for different types of flow, the online deployment of a data-driven closure model can cause instabilities and biases in modeling the overall effect of subgrid-scale processes, which in turn leads to inaccurate prediction. To tackle this issue, we exploit the data assimilation technique to correct the physics-based model coupled with the neural network as a surrogate for unresolved flow dynamics in multiscale systems. In particular, we use a set of neural network architectures to learn the correlation between resolved flow variables and the parametrizations of unresolved flow dynamics and formulate a data assimilation approach to correct the hybrid model during their online deployment. We illustrate our framework in a set of applications of the multiscale Lorenz 96 system for which the parametrization model for unresolved scales is exactly known, and the two-dimensional Kraichnan turbulence system for which the parametrization model for unresolved scales is not known a priori. Our analysis, therefore, comprises a predictive dynamical core empowered by (i) a data-driven closure model for subgrid-scale processes, (ii) a data assimilation approach for forecast error correction, and (iii) both data-driven closure and data assimilation procedures. We show significant improvement in the long-term prediction of the underlying chaotic dynamics with our framework compared to using only neural network parametrizations for future prediction. Moreover, we demonstrate that these data-driven parametrization models can handle the non-Gaussian statistics of subgrid-scale processes, and effectively improve the accuracy of outer data assimilation workflow loops in a modular nonintrusive way.},
doi = {10.1103/physrevfluids.6.050501},
journal = {Physical Review Fluids (Online)},
number = 5,
volume = 6,
place = {United States},
year = {Wed May 12 00:00:00 EDT 2021},
month = {Wed May 12 00:00:00 EDT 2021}
}
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