Vol. 38, issue 10, article # 11

Abstract:

The problem of atmospheric emission source identification using remote sensing data is considered. An algorithm is proposed for a three-dimensional model of atmospheric pollutant transport and a nonlinear measurement model represented as a “differentiable black box". The algorithm is based on sensitivity operators and ensembles of adjoint equations solutions. It was tested on a realistic scenario for identifying soot sources for the Baikal region with synthetic satellite measurements of the Terra/MODIS platform, which showed its applicability. Additionally, the measurement data decomposition modification of the algorithm is proposed, which made it possible to reduce 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.

Keywords:

remote sensing, nonlinear measurement operator, source identification, advection-diffusion, sensitivity operator, adjoint equations, decomposition, atmospheric aerosol, RTTOV, MODIS

Figures:

References:

1. Rey-Pommier A., Chevallier F., Ciais P., Broquet G., Christoudias T., Kushta J., Hauglustaine D., Sciare J. Quantifying NO_x emissions in Egypt using TROPOMI observations // Chem. Phys. 2022. V. 22, N 17. P. 11505–11527. DOI: 10.5194/acp-22-11505-2022.
2. Savas D., Dufour G., Coman A.,, Siour G., Fortems-Cheiney A., Broquet G., Pison I., Berchet A., Bessagnet B. Anthropogenic NO_x emission estimations over East China for 2015 and 2019 using OMI satellite observations and the new inverse modeling system CIF-CHIMERE // Atmosphere. 2023. V. 14, N 1. P. 154. DOI: 10.3390/atmos14010154.
3. Dammers E., Tokaya J., Mielke C., Hausmann K., Griffin D., McLinden C., Eskes H., Timmermans R. Can TROPOMI NO₂ satellite data be used to track the drop in and resurgence of NO_x emissions in Germany between 2019–2021 using the multi-source plume method (MSPM)? // Geosci. Model Dev. 2024. V. 17, N 12. P. 4983–5007. DOI: 10.5194/gmd-17-4983-2024.
4. Fortems-Cheiney A., Broquet G., Potier E., Plauchu R., Berchet A., Pison I., Denier van der Gon H., Dellaert S. CO anthropogenic emissions in Europe from 2011 to 2021: Insights from Measurement of Pollution in the Troposphere (MOPITT) Satellite Data // Chem. Phys. 2024. V. 24, N 8. P. 4635–4649. DOI: 10.5194/acp-24-4635-2024.
5. Penenko A.V., Penenko V.V., Tsvetova E.A. Algoritmy usvoeniya dannykh dlya modelei khimii atmosfery // Izv. RAN. Fiz. atmosf. i okeana. 2025. V. 61, N 3. P. 388–403. DOI: 10.31857/S0002351525030091.
6. MODIS. Moderate Resolution Imaging Spectroradiometer. URL: https://modis.gsfc.nasa.gov/ (last access: 12.04.2025).
7. Penenko A.V. Convergence analysis of the adjoint ensemble method in inverse source problems for advection-diffusion-reaction models with image-type measurements // Inverse Probl. Imag. 2020. V. 14, N 5. P. 757–782. DOI: 10.3934/ipi.2020035.
8. Penenko A., Penenko V., Tsvetova E., Gochakov A., Pyanova E., Konopleva V. Sensitivity operator framework for analyzing heterogeneous air quality monitoring systems // Atmosphere. 2021. V. 12, N 12. P. 1697. DOI: 10.3390/atmos12121697.
9. Penenko A.V., Gochakov A.V., Antokhin P.N. Algoritm usvoeniya dannykh na osnove operatora chuvstvitel'nosti dlya trekhmernoi modeli perenosa i transformatsii primesei v atmosfere // Optika atmosf. i okeana. 2024. V. 37, N 9. P. 719–728. DOI: 10.1134/s1024856024701082; Penenko A.V., Gochakov A.V., Antokhin P.N. Data assimilation algorithm based on the sensitivity operator for a three-dimensional model of transport and transformation of atmospheric contaminants // Atmos. Ocean. Opt. 2024. V. 37, N 6. P. 822–832.
10. Le Dimet F.-X., Souopgui I., Titaud O., Shutyaev V., Hussaini M.Y. Toward the assimilation of images // Nonlin. Proc. Geophys. 2015. V. 22, N 1. P. 15–32. DOI: 10.5194/npg-22-15-2015.
11. Rose D., Wehner B., Ketzel M., Engler C., Voigtländer J., Tuch T., Wiedensohler A. Atmospheric number size distributions of soot particles and estimation of emission factors // Atmos. Chem. Phys. 2005. V. 6, N 4. DOI: 10.5194/acpd-5-10125-2005.
12. Baldauf M., Seifert A., Forstner J., Majewski D., Raschendorfer M., Reinhardt T. Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities // Mon. Weather Rev. 2011. V. 139. P. 3887–3905. DOI: 10.1175/MWR-D-10-05013.1.
13. Operator izmerenii MODIS dlya freimvorka IMDAF. Rusin E.V., Penenko A.V. Svidetel'stvo o gosudarstvennoi registratsii programmy dlya EVM RU 2024612862. Patentnoe vedomstvo: Rossiya. Data registratsii: 25.01.2024 year. Data publikatsii: 06.02.2024 year. Pravoobladateli: Federal'noe gosudarstvennoe byudzhetnoe obrazovatel'noe uchrezhdenie vysshego obrazovaniya «YUgorskii gosudarstvennyi universitet».
14. Levy R., Hsu C., Sayer A., Mattoo S., Lee J. MODIS Atmosphere L2 Aerosol Product. 2017. NASA MODIS Adaptive Processing System, Goddard Space Flight Center. DOI: 10.5067/MODIS/MOD04_L2.061. URL: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MOD04_L2.
15. Clough S.A., Shephard M.W., Mlawer E.J., Delamere J.S., Iacono M.J., Cady-Pereira K., Boukabara S., Brown P.D. Atmospheric radiative transfer modeling: A summary of the AER codes, short communication // J. Quant. Spectrosc. Radiat. Transfer. 2005. V. 91. P. 233–244. DOI: 10.1016/j.jqsrt.2004.05.058.
16. Saunders R., Matricardi M., Brunel P. An improved fast radiative transfer model for assimilation of satellite radiance observations // Q. J. Roy. Meteorol. Soc. 1999. V. 125, N 556. P. 1407–1425. DOI: 10.1002/qj.1999.49712555615.
17. RTTOV-13. Science and validation report. URL: https://nwp-saf.eumetsat.int/site/download/documentation/rtm/docs_rttov13/rttov13_svr.pdf (last access: 12.04.2025).
18. Hess M., Koepke P., Schult I. Optical Properties of Aerosols and Clouds: The software package OPAC // Bull. Am. Meteorol. Soc. 1998. V. 79, N 5. P. 831–844. DOI: 10.1175/1520-0477(1998)079<0831:OPOAAC>2.0.CO;2.
19. Peleg S., Werman M., Rom H. A unified approach to the change of resolution: Space and gray-level // IEEE Tran. Pattern Anal. Mach. Intell. 1989. V. 11, N 7. P. 739–742. DOI: 10.1109/34.192468.
20. Rubner Y. The Earth mover's distance as a metric for image retrieval // Int. J. Comput. Vision. 2000. V. 40, N 2. P. 99–121. DOI: 10.1023/a:1026543900054.
21. Flamary R., Courty N., Gramfort A., Alaya M.Z., Boisbunon A., Chambon S., Chapel L., Corenflos A., Fatras A., Fournier K., Gautheron N., Gayraud L., Janati H., Rakotomamonjy T.H., Redko I., Rolet A., Schutz A., Seguy V., Sutherland D.J., Tavenard R., Tong A., Vayer T. POT: Python Optimal Transport // J. Machine Learn. Res. 2021. V. 22, N 78. P. 1–8.