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An Automatic Differentiation Method for Surface Carbon Flux Inversion
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  • Yichen Yao,
  • Guodong Chen,
  • Zhibin Wang,
  • Hao Li
Yichen Yao
Alibaba Group

Corresponding Author:[email protected]

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Guodong Chen
Alibaba Group
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Zhibin Wang
Alibaba Group
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Hao Li
Alibaba Group
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Abstract

We attempt, for the first time, to estimate the surface carbon flux by the method of automatic differentiation. The atmospheric transport model is developed using a deep learning framework and is validated against standard approaches. Depends on the built-in automatic differentiation feature of the deep learning framework, the system derivatives/gradients are readily available without any extra effort. We then formulate the surface carbon flux estimation as an inverse problem using the variational approach, driven by back-propagated objective gradients. The feasibility of the automatic differentiation method is demonstrated in identical-twin observing system simulation experiments (OSSEs). The proposed framework shows favorable accuracy and great efficiency in both fully and partially observable scenarios. The present study establishes a link between machine learning frameworks and general data assimilation or inverse modeling problems, and the promising results encourage more investigations in incorporating machine learning techniques in inverse carbon modeling.