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L . P . Bass 1 , O . V . Nikolaeva 1 V . S . Kuznetsov 2 A . A . Kokhanovsky 3,4

The code RADUGA-5.1(P) as applied to the solution of selected atmospheric optics problems. L . P . Bass 1 , O . V . Nikolaeva 1 V . S . Kuznetsov 2 A . A . Kokhanovsky 3,4 1 Keldysh Institute of Applied Mathematics, Russian Academy of Science, Moscow , Russia

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L . P . Bass 1 , O . V . Nikolaeva 1 V . S . Kuznetsov 2 A . A . Kokhanovsky 3,4

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  1. The code RADUGA-5.1(P) as applied to the solution of selected atmospheric optics problems. L.P.Bass1, O.V.Nikolaeva1V.S.Kuznetsov2A.A.Kokhanovsky3,4 1 Keldysh Institute of Applied Mathematics, Russian Academy of Science, Moscow, Russia 2Research Scientific Center "Kurchatov Institute", Moscow, Russia 3Institute of Remote Sensing, Bremen University, Bremen, Germany 4 Institute of Physics, Academy of Sciences of Belarus, Minsk, Belarus

  2. Abstract Effective methods to solve the atmospheric optics inverse problems rely on accurate and fast algorithms of the direct problem solution [1]. The code RADUGA-5.1(P) has been developed to solve the transport equation by means of the discrete ordinate method and parallel computers under weak restrictions to sections and sources [2]. The latest version of this code permits to define phase functions in the discrete-angular form rather than in the Legandre expansion form. This opportunity permits to increase the accuracy results obtained. The code performance was checked using Monte-Carlo calculations in our earlier work [2]. The results of the well – known 3D test [3], obtained by RADUGA-5.1(P), are presented. Also the main features of the code are discussed.

  3. We address the possibility to solve inverse atmospheric optics problems using neural networks [4]. To overcome major difficulties common to remote sensing problems, neuron nets can be applied. The calculations with neural networks are much faster than those using the method of steepest descent. H.W.Barker et all. Assessing 1D atmospheric solar radiative transfer models: interpretation and handling of unresolved clouds. J. of Climate. V. 16. PP. 2676-2699. 2003. O.V.Nikolaeva, L.P.Bass, T.A.Germogenova, A.A.Kokhanovsky, V.S.Kuznetsov, B.Mayer. The influence of neighbouring clouds on the clear sky reflectance studied with the 3–D transport code RADUGA. JQSRT. V. 94. PP. 405-424. 2005. http://i3rc.gsfc.nasa.gov T.J.Farrell, B.C.Wilson, M.S.Patterson. The use of a neural network to determine tissue optical properties from spatially resolved diffuse reflectance measurements. Phys. Med. Biol. V. 37, No 12, PP. 2281-2286, 1992.

  4. I3RC test. Phase I. Case 3.HG phase function, g=0.85.Albedo of single-collided radiationl=0.99Vacuum condition on the bottom boundary.Periodic condition on the vertical boundaries

  5. Nadir radiance fields

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