The possibility of retrieving, using a neural network, the columnar carbon dioxide concentration profile when sounding from a space orbit of 450 km and from a balloon at altitudes of 23 and 10 km are analyzed. The use of a priori data on temperature, pressure, and reflected and scattered signals is considered. The errors of retrieval of the columnar CO2 are 0.15% and 0.5% at altitudes lower than 2 km for lidar with a telescope diameter of 1 m and laser pulse energy of 50 μJ at a resolution of 60 km.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
G. Ehret, C. Kiemle, M. Wirth, and A. Amediek, “Space-borne remote sensing of CO2, CH4, and N2O by integrated path absorption lidar: A sensitivity analysis,” Appl. Phys. 90, 593–608 (2008).
Jianping Mao, Anand Ramanathan, James B. Abshire, Stephan R. Kawa, Haris Riris, Graham R. Allan, Michael Rodriguez, William E. Hasselbrack, Xiaoli Sun, Kenji Numata, Jeff Chen, Yonghoon Choi, Mei Ying, and Melissa Yang, “Measurement of atmospheric CO2 column concentrations to cloud tops with a pulsed multi-wavelength airborne lidar,” Atmos. Meas. Tech., No. 11, 127–140 (2018).
Ge Han, Xin Ma, Ailin Liang, Tianhao Zhang, Yannan Zhao, Miao Zhang, and Wei Gong, “Performance evaluation for China’s planned CO2-IPDA,” Remote Sens., No. 9, 768–789 (2017).
G. Ehret, P. Bousquet, C. Pierangelo, M. Alpers, B. Millet, J. B. Abshire, H. Bovensmann, J. P. Burrows, F. Chevallier, P. Ciais, C. Crevoisier, A. Fix, P. Flamant, Ch. Frankenberg, F. Gibert, B. Heim, M. Heimann, S. Houweling, H. W. Hubberten, P. Jockel, K. Law, A. Low, J. Marshall, A. Agusti-Panareda, S. Payan, C. Prigent, P. Rairoux, T. Sachs, M. Scholze, and M. Wirth, “MERLIN: A French-German space lidar mission dedicated to atmospheric methane,” Remote Sens, No. 9, 1052–1081 (2017).
D. Sakaizawa, S. Kawakami, M. Nakajima, T. Tanaka, I. Morino, and O. Uchino, “An airborne amplitude-modulated 1.57 μm differential laser absorption spectrometer: Simultaneous measurement of partial column-averaged dry air mixing ratio of CO2 and target range,” Atmos. Meas. Tech, No. 6, 387–396 (2013).
R. T. Menzies, G. D. Spiers, and J. Jacob, “Airborne laser absorption spectrometer measurements of atmospheric CO2 column mole fractions: Source and sink detection and environmental impacts on retrievals,” J. Atmos. Ocean. Tech. 31, 404–421 (2014).
C. Kiemle, G. Ehret, A. Amediek, A. Fix, M. Quatrevalet, and M. Wirth, “Potential of spaceborne lidar measurements of carbon dioxide and methane emissions from strong point sources,” Remote Sens. 9 (11), 1137–1153 (2017).
G. G. Matvienko, G. M. Krekov, and A. Ya. Sukhanov, “Space-borne remote sensing of greenhouse gases by IPDA lidar: A potentialities estimate,” in Proc. of the 25th Intern. Laser Radar Conf. (St. Petersburg, 2010).
S. V. Babchenko, G. G. Matvienko, and A. Ya. Sukhanov, “Assessing the possibilities of sensing CH4 and CO2 greenhouse gases above the underlying surface with satellite-based IDPA lidar,” Atmos. Oceanic Opt. 28 (3), 245–253 (2015).
A. Ya. Sukhanov, “Airborne DIAL-IPDA lidar sensing of carbom dioxide inverse problem solution on basis bionic methods,” Opt. Atmos. Okeana 30 (7), 589–597 (2017).
A. Ya. Sukhanov, Candidate’s Dissertation in Engineering (TUSUR, Tomsk, 2006).
M. M. Mamun and D. Mȕller, “Retrieval of intensive aerosol microphysical parameters from multiwavelength Raman/HSRL lidar: Feasibility study with artificial neural networks,” J. Atmos. Meas. Tech. Discuss. 46 (2016). https://doi.org/10.5194/amt-2016-7
V. V. Berdnik and V. A. Loiko, “Neural networks for aerosol particles characterization,” J. Quant. Spectrosc. Radiat. Transfer 184, 135–145 (2016).
G. M. Krekov, M. M. Krekova, A. A. Lisenko, and A. Ya. Sukhanov, “Identification of trace atmospheric gases using artificial neural networks,” in Abstr. of the XV Workshop “Siberian Aerosols” (Publishing House of IAO SB RAS, Tomsk, 2008), p. 41 [in Russian].
A. Ya. Sukhanov, “Neural network pretraining algorithm for a series of lidar sounding inverse problems,” in Proc. of the XXII Intern. Symp. “Atmospheric and Ocean Optics. Atmospheric Physics” (Publishing House of IAO SB RAS, Tomsk, 2016), p. C45–C48 [in Russian].
A. Ya. Sukhanov and G. M. Krekov, “Recognition of the fluorescence spectra of bacteria and polyaromatic hydrocarbons,” in Proc. of the All-Russian Conf. “Mathematical Methods for Image Recognition”, Petrozavodsk, 2011 (MAKS Press, Moscow, 2011), p. 514–517 [in Russian].
A. Ya. Sukhanov and M. Yu. Kataev, “Capabilities of the neural network method for retrieval of the ozone profile from lidar data,” Atmos. Ocean. Opt. 16 (12), 1020–1023 (2003).
M. Yu. Arshinov, B. D. Belan, D. K. Davydov, G. M. Krekov, A. V. Fofonov, S. V. Babchenko, Gen. Inoue, Toshinobu Machida, Sh. Sh. Maksutov, Motoki Sasakawa, and Ko Shimoyama, “The dynamics in vertical distribution of greenhouse gases in the atmosphere,” Opt. Atmos. Okeana 25 (12), 1051–1061 (2012).
K. Labitzke, J. J. Barnett, and B. Edwards, Handbook MAP 16 (SCOSTEP, 1985).
A. E. Hedin, “Extension of the MSIS thermospheric model into the middle and lower atmosphere,” J. Geophys. Res. 96 (A2), 1159–1172 (1991).
V. S. Komarov, Statistical Models of the Temperature and Atmospheric Gases (Gidrometeoizdat, Leningrad, 1986) [in Russian].
Yu. S. Balin, A. G. Borovoi, V. D. Burlakov, S. I. Dolgii, M. G. Klemasheva, A. V. Konoshonkin, G. P. Kokhanenko, N. V. Kustova, V. N. Marichev, G. G. Matvienko, A. A. Nevzorov, A. V. Nevzorov, I. E. Penner, O. A. Romanovskii, S. V. Samoilova, A. Ya. Sukhanov, O. V. Kharchenko, and V. A. Shishko, Lidar Monitoring of Cloud and Aerosol Fileds, Trace Atmospheric Gases, and Weather Parameters, Ed. by G.G. Matvienko (Publishing House of IAO SB RAS, Tomsk, 2015) [in Russian].
Translated by O. Ponomareva
About this article
Cite this article
Matvienko, G.G., Sukhanov, A.Y. & Babchenko, S.V. The Analysis of Capabilities of Neural Networks in CO2 Sounding with Spaceborne IPDA-Lidar with the Use of Different A Priori Data. Atmos Ocean Opt 32, 165–170 (2019). https://doi.org/10.1134/S102485601902009X
- spaceborne lidar
- carbon dioxide
- greenhouse gas
- neural network