2006Environmental Science & TechnologyRequires access

Adjoint Sensitivity Analysis for a Three-Dimensional Photochemical Model: Implementation and Method Comparison

P. T. Martien, Robert A. Harley, Dan Gabriel Cacuci

Open publisher page 35 citations

Abstract

Photochemical air pollution forms when emissions of nitrogen oxides (NO(x)) and volatile organic compounds (VOC) react in the atmosphere in the presence of sunlight. The goal of applying three-dimensional photochemical air quality models is usually to conduct sensitivity analysis: for example, to predict changes in an ozone response due to changes in NO(x) and VOC emissions or other model data. Forward sensitivity analysis methods are best suited to investigating sensitivities of many model responses to changes in a few inputs or parameters. Here we develop a continuous adjoint model and demonstrate an adjoint sensitivity analysis procedure that is well-suited to the complementary case of determining sensitivity of a small number of model responses to many parameters. Sensitivities generated using the adjoint method agree with those generated using other methods. Compared to the forward method, the adjoint method had large disk storage requirements but was more efficient in terms of computer processor time for receptor-based investigations focused on a single response at a specified site and time. The adjoint method also generates sensitivity apportionment fields, which reveal when and where model data are important to the target response.

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What this paper is about

Photochemical air pollution forms when emissions of nitrogen oxides (NO(x)) and volatile organic compounds (VOC) react in the atmosphere in the presence of sunlight. The goal of applying three-dimensional photochemical air quality models is usually to conduct sensitivity analysis: for example, to predict changes in an ozone response due to changes in NO(x) and VOC emissions or other model data. Forward sensitivity analysis methods are best suited to investigating sensitivities of many model responses to changes in a few inputs or parameters. Here we develop a continuous adjoint model and demonstrate an adjoint sensitivity analysis procedure that is well-suited to the complementary case of determining sensitivity of a small number of model responses to many parameters. Sensitivities generated using the adjoint method agree with those generated using other methods. Compared to the forward method, the adjoint method had large disk storage requirements but was more efficient in terms of computer processor time for receptor-based investigations focused on a single response at a specified site and time. The adjoint method also generates sensitivity apportionment fields, which reveal when and where model data are important to the target response.

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Available abstract

Photochemical air pollution forms when emissions of nitrogen oxides (NO(x)) and volatile organic compounds (VOC) react in the atmosphere in the presence of sunlight. The goal of applying three-dimensional photochemical air quality models is usually to conduct sensitivity analysis: for example, to predict changes in an ozone response due to changes in NO(x) and VOC emissions or other model data. Forward sensitivity analysis methods are best suited to investigating sensitivities of many model responses to changes in a few inputs or parameters. Here we develop a continuous adjoint model and demonstrate an adjoint sensitivity analysis procedure that is well-suited to the complementary case of determining sensitivity of a small number of model responses to many parameters. Sensitivities generated using the adjoint method agree with those generated using other methods. Compared to the forward method, the adjoint method had large disk storage requirements but was more efficient in terms of computer processor time for receptor-based investigations focused on a single response at a specified site and time. The adjoint method also generates sensitivity apportionment fields, which reveal when and where model data are important to the target response.

Key concepts: Sensitivity (control systems), Adjoint equation, Air quality index, Biological system, Data assimilation, NOx, Environmental science, Chemistry

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