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23 February 2004 Application of neural networks for retrieving atmospheric gases concentration profile for lidar sounding data
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Proceedings Volume 5397, Tenth Joint International Symposium on Atmospheric and Ocean Optics/Atmospheric Physics. Part II: Laser Sensing and Atmospheric Physics; (2004) https://doi.org/10.1117/12.548590
Event: Tenth Joint International Symposium on Atmospheric and Ocean Optics/Atmospheric Physics, 2003, Tomsk, Russian Federation
Abstract
In the report a method of ozone profile concentration retrieving from the lidar data sounding on the basis of neural networks (NN) is description. Application of neural networks in inverse tasks is connected with solving some important stages. In the first, it is necessary to carry out training of NN on the basis of the big data set (measurement - decision). In the second, basing on the results of the first stage to generate optimum NN (number of layers, transfer functions). Results of simulation inverse task of ozone profile concentration retrieving from the lidar data sounding have shown reliability of work in NN, speed of the inverse tasks solving and accuracy of retrieving ozone profile comparable to traditional methods.
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Mikhail Yu. Kataev and A. Ya. Sykhanov "Application of neural networks for retrieving atmospheric gases concentration profile for lidar sounding data", Proc. SPIE 5397, Tenth Joint International Symposium on Atmospheric and Ocean Optics/Atmospheric Physics. Part II: Laser Sensing and Atmospheric Physics, (23 February 2004); https://doi.org/10.1117/12.548590
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