Building Tangent‐Linear and Adjoint Models for Data Assimilation With Neural Networks
Abstract We assess the ability of neural network emulators of physical parametrization schemes in numerical weather prediction models to aid in the construction of linearized models required by four‐dimensional variational (4D‐Var) data assimilation. Neural networks can be differentiated trivially,...
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American Geophysical Union (AGU)
2021-09-01
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Series: | Journal of Advances in Modeling Earth Systems |
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Online Access: | https://doi.org/10.1029/2021MS002521 |
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doaj-541ee394ec4045ef8f348f8ed98f6e2e2021-09-28T06:35:39ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662021-09-01139n/an/a10.1029/2021MS002521Building Tangent‐Linear and Adjoint Models for Data Assimilation With Neural NetworksSam Hatfield0Matthew Chantry1Peter Dueben2Philippe Lopez3Alan Geer4Tim Palmer5European Centre for Medium‐Range Weather Forecasts Reading UKAtmospheric, Oceanic and Planetary Physics, University of Oxford Oxford UKEuropean Centre for Medium‐Range Weather Forecasts Reading UKEuropean Centre for Medium‐Range Weather Forecasts Reading UKEuropean Centre for Medium‐Range Weather Forecasts Reading UKAtmospheric, Oceanic and Planetary Physics, University of Oxford Oxford UKAbstract We assess the ability of neural network emulators of physical parametrization schemes in numerical weather prediction models to aid in the construction of linearized models required by four‐dimensional variational (4D‐Var) data assimilation. Neural networks can be differentiated trivially, and so if a physical parametrization scheme can be accurately emulated by a neural network then its tangent‐linear and adjoint versions can be obtained with minimal effort, compared with the standard paradigms of manual or automatic differentiation of the model code. Here we apply this idea by emulating the non‐orographic gravity wave drag parametrization scheme in an atmospheric model with a neural network, and deriving its tangent‐linear and adjoint models. We demonstrate that these neural network‐derived tangent‐linear and adjoint models not only pass the standard consistency tests but also can be used successfully to do 4D‐Var data assimilation. This technique holds the promise of significantly easing maintenance of tangent‐linear and adjoint codes in weather forecasting centers, if accurate neural network emulators can be constructed.https://doi.org/10.1029/2021MS002521neural networkdata assimilationtangent‐linearadjoint |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sam Hatfield Matthew Chantry Peter Dueben Philippe Lopez Alan Geer Tim Palmer |
spellingShingle |
Sam Hatfield Matthew Chantry Peter Dueben Philippe Lopez Alan Geer Tim Palmer Building Tangent‐Linear and Adjoint Models for Data Assimilation With Neural Networks Journal of Advances in Modeling Earth Systems neural network data assimilation tangent‐linear adjoint |
author_facet |
Sam Hatfield Matthew Chantry Peter Dueben Philippe Lopez Alan Geer Tim Palmer |
author_sort |
Sam Hatfield |
title |
Building Tangent‐Linear and Adjoint Models for Data Assimilation With Neural Networks |
title_short |
Building Tangent‐Linear and Adjoint Models for Data Assimilation With Neural Networks |
title_full |
Building Tangent‐Linear and Adjoint Models for Data Assimilation With Neural Networks |
title_fullStr |
Building Tangent‐Linear and Adjoint Models for Data Assimilation With Neural Networks |
title_full_unstemmed |
Building Tangent‐Linear and Adjoint Models for Data Assimilation With Neural Networks |
title_sort |
building tangent‐linear and adjoint models for data assimilation with neural networks |
publisher |
American Geophysical Union (AGU) |
series |
Journal of Advances in Modeling Earth Systems |
issn |
1942-2466 |
publishDate |
2021-09-01 |
description |
Abstract We assess the ability of neural network emulators of physical parametrization schemes in numerical weather prediction models to aid in the construction of linearized models required by four‐dimensional variational (4D‐Var) data assimilation. Neural networks can be differentiated trivially, and so if a physical parametrization scheme can be accurately emulated by a neural network then its tangent‐linear and adjoint versions can be obtained with minimal effort, compared with the standard paradigms of manual or automatic differentiation of the model code. Here we apply this idea by emulating the non‐orographic gravity wave drag parametrization scheme in an atmospheric model with a neural network, and deriving its tangent‐linear and adjoint models. We demonstrate that these neural network‐derived tangent‐linear and adjoint models not only pass the standard consistency tests but also can be used successfully to do 4D‐Var data assimilation. This technique holds the promise of significantly easing maintenance of tangent‐linear and adjoint codes in weather forecasting centers, if accurate neural network emulators can be constructed. |
topic |
neural network data assimilation tangent‐linear adjoint |
url |
https://doi.org/10.1029/2021MS002521 |
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