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Abstract <p>The problem of atmospheric emission source identification from remote sensing data is considered. An algorithm for the problem solution based on a three-dimensional model of atmospheric pollutant transport and a nonlinear measurement model represented as a “differentiable black box” is suggested. The algorithm includes sensitivity operators and ensembles of solutions of adjoint equations. It was tested for a realistic scenario for identifying soot sources for the Baikal region with synthetic satellite Terra/MODIS measurements and showed its efficiency. Additionally, the measurement data decomposition modification of the algorithm is suggested, which reduces the relative error of retrieving the source function by 12% compared to the version without decomposition. The results can be used in the development of remote sensing data processing systems.</p>

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An Algorithm for Identifying Pollution Sources with Non-Linear Measurement Operators

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • A. V. Penenko,
  • E. V. Rusin,
  • M. K. Emelyanov

摘要

Abstract

The problem of atmospheric emission source identification from remote sensing data is considered. An algorithm for the problem solution based on a three-dimensional model of atmospheric pollutant transport and a nonlinear measurement model represented as a “differentiable black box” is suggested. The algorithm includes sensitivity operators and ensembles of solutions of adjoint equations. It was tested for a realistic scenario for identifying soot sources for the Baikal region with synthetic satellite Terra/MODIS measurements and showed its efficiency. Additionally, the measurement data decomposition modification of the algorithm is suggested, which reduces the relative error of retrieving the source function by 12% compared to the version without decomposition. The results can be used in the development of remote sensing data processing systems.