Regression model for reconstruction of the total methane content according to the data from AIRS hyperspectrometer and chemical transport model MOZART-4

A. A. Lagutin; E. Yu. Mordvin; N. V. Volkov

doi:10.1117/12.2690784

17 October 2023 Regression model for reconstruction of the total methane content according to the data from AIRS hyperspectrometer and chemical transport model MOZART-4

A. A. Lagutin, E. Yu. Mordvin, N. V. Volkov

Author Affiliations +

Proceedings Volume 12780, 29th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics; 1278062 (2023) https://doi.org/10.1117/12.2690784
Event: XXIX International Symposium "Atmospheric and Ocean Optics, Atmospheric Physics", 2023, Moscow, Russian Federation

Abstract

The paper presents the results of the development of a regression model for reconstruction of the total methane content [CH₄] in the atmosphere according to the data from AIRS hyperspectrometer of the Aqua satellite. The information basis of the study is the results of the chemical transport model MOZART-4 modified by the authors for the period 2001-2020. It is shown that the proposed model, which accepts only AIRS L2 products as input, provides reconstruction with a correlation coefficient R² = 0.94 and a standard deviation σ = 6.2 ppb. A comparison of the reconstruction results with the observations of the TCCON network of ground-based stations in Europe showed a satisfactory agreement between the results of the developed model and measurements. Using the proposed method, the rate of change of the total methane content in the atmosphere of Western Siberia in 2003-2022 was estimated, which is equal to 5.5 ± 0.2 ppb/year.

Citation Download Citation

A. A. Lagutin, E. Yu. Mordvin, and N. V. Volkov "Regression model for reconstruction of the total methane content according to the data from AIRS hyperspectrometer and chemical transport model MOZART-4", Proc. SPIE 12780, 29th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, 1278062 (17 October 2023); https://doi.org/10.1117/12.2690784

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KEYWORDS

Methane

Data modeling

Atmospheric modeling

Satellites

Earth's atmosphere

Atmospheric monitoring

Correlation coefficients

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