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Radiative Transfer Codes for Atmospheric Correction and Aerosol Retrievals: Intercomparison Study

AEROCENTER Fall Seminar Series, October 2 nd , 2007. Radiative Transfer Codes for Atmospheric Correction and Aerosol Retrievals: Intercomparison Study. Svetlana Y. Kotchenova & Eric F. Vermote. The study is being performed in collaboration with: Robert Levy, Alexei Lyapustin, and Omar Torres.

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Radiative Transfer Codes for Atmospheric Correction and Aerosol Retrievals: Intercomparison Study

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  1. AEROCENTER Fall Seminar Series, October 2nd, 2007 Radiative Transfer Codes for Atmospheric Correction and Aerosol Retrievals:Intercomparison Study Svetlana Y. Kotchenova & Eric F. Vermote The study is being performed in collaboration with: Robert Levy, Alexei Lyapustin, and Omar Torres

  2. VPD(vector) RT3(vector) 6SV1.1(vector) Svetlana & Eric SHARM (scalar) Monte Carlo (benchmark) Robert Omar Svetlana & Eric MODTRAN (scalar) Coulson’s tabulated values (benchmark) Alexei Svetlana Project Description The project is devoted to the comparison and detailed evaluation of five atmospheric RT codes incorporated in different satellite data processing algorithms only molecular atmosphere only molecular atmosphere 2

  3. Applications of the codes • 6SV1.1 (Second Simulation of a Satellite Signal in the Solar Spectrum, Vector, version 1.1): MODIS atmospheric correction and internal aerosol inversion • RT3 (Radiative Transfer 3): MODIS coarse resolution (10-km) aerosol retrieval • VPD (Vector Program D): TOMS (Total Ozone Mapping Spectrometer) aerosol inversion • SHARM (Spherical Harmonics): MAIAC (Multi-Angle Implementation of Atmospheric Correction for MODIS) • MODTRAN (Moderate Resolution Atmospheric Transmittance and Radiance Code): AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) atmospheric correction 3

  4. Spectrum: 350 to 3750 nm Molecular atmosphere: 6 code-embedded & 2 user-defined models Ground surface: homogeneous &non-homogeneous with & without directional effect (10 BRDF + 1 user-defined models) Instruments: • AATSR, ALI, ASTER, AVHRR, ETM, GLI, GOES, HRV, HYPBLUE, MAS, MERIS, METEO, MSS, TM, MODIS, POLDER, SeaWiFS, VIIRS, & VGT – 19 in total Aerosol atmosphere: 6 code-embedded & 4 user-defined models & AERONET Description of the codes: 6SV1.1 Author: E. Vermote (University of Maryland, USA) Modified: E. Vermote et al. Language: Fortran 77, 95 Features: http://6s.ltdri.org Publications + Interface to create input files 4

  5. Description of the codes: RT3 Author: F. Evan (Colorado State University) Language: Fortran 77 Input: Disadvantages: 1) pre-computed sets of output angles (interpolation might be needed) 2) no embedded MIE-code (combination with a MIE-code is needed to simulate aerosols) 5

  6. 1.atmMIE config.par 1L.sfc Description of the codes: SHARM Author: T. Muldashev (Space Research Institute, Kazakhstan) Modified: A. Lyapustin Language: C/C++ Input: Advantages:very fast, simultaneous simulations for multiple geometries and wavelengths 6

  7. card 1 card 1a card 2 tape 5 – molecular atmosphere Description of the codes: MODTRAN Author: Berk et al. (Air Force Research Laboratory) Language: Fortran 77 Modeling Features: molecular atmospheres (a lot of effort is put into gas absorption!), aerosols (with the help of DISORT at 16 Gaussian angles), clouds, surface Input: in the form of formatted “cards” (quite painful!) Output: single geometry but for a range of wavelengths 7

  8. 6SV & & RT3 & SHARM Project History discussions, calculations,Web site creation ... 6SV & VPD MODTRAN VPD & RT3 2005 2006 2007 2008 & SHARM Coulson’s tables MonteCarlo Why do you ignore MODTRAN? 8

  9. Goals of the project • to evaluate the accuracy of each code based on the comparison with standard benchmark references such as Coulson’s tabulated values and a Monte Carlo approach • to illustrate differences between individual simulations of the code • to determine how the revealed differences influence on the accuracy of aerosol optical thickness and surface reflectance retrievals • to create reference (benchmark) data sets that can be used in future code comparison studies 9

  10. Presentation of the results All results will be put on the Internet and summarized in a manuscript titled “Radiative Transfer Codes for Atmospheric Correction and Aerosol Retrievals: Intercomparison Study” which will be submitted to Applied Optics. http://rtcodes.ltdri.org 10

  11. Characterization of a RT code In regard to remote sensing applications... 1. Versatility 2. Accuracy 3. User-friendliness 4. Speed 6SV1.1, SHARM, MODTRAN, VPD & RT3 (RT3 needs to be combined with a MIE-code) to be determined 6SV1.1 & SHARM, RT3, MODTRAN, VPD (VPD is not publicly available) to be determined 11

  12. Code Accuracy The general atmospheric RT code accuracy requirement for pure simulation studies is 1%. Reference: Muldashev et al., Spherical harmonics method in the problem of radiative transfer in the atmosphere-surface system, Journal of Quantitative Spectroscopy and Radiative Transfer, 61(3), 393-404, 1999. Will violation of this requirement have a significant effect on the resulting satellite product? Step 1: comparison with benchmarks to see if there is violation Step 2: evaluation of the impact of violation 12

  13. Benchmarks: Coulson’s tables Coulson’s tabulated values represent the complete solution of the Rayleigh problem for a molecular atmosphere. Reference: Coulson et al., Tables related to radiation emerging from a planetary atmosphere with Rayleigh scattering (1960). 13

  14. Benchmarks: Monte Carlo The code is written by F.M. Bréon (le Laboratoire des Sciences du Climat et de l'Environnement, France) based on the Stokes vector approach. Languages: Fortran, C. Limitations: large amounts of calculation time and angular space discretization. 14

  15. direction of incident light larger particles direction of incident light Comparison Procedure 1. Molecular Atmosphere (surf = 0.0; 0.25) The same procedure was usedin the previous comparison study: A. Lyapustin “Radiative transfer code SHARM-3D for radiance simulations over a non-Lambertian nonhomogeneous surface: intercomparison study”, Applied Optics, 41(27), 5607-5615. 2. Aerosol Atmosphere (surf = 0.0) 3. Mixed Atmosphere (surf = 0.0; 0.25) surf is the reflectance of a Lambertian surface 15

  16. Molecular Atmosphere: Conditions All RT codes are compared to the Coulson’s tabulated values. * mol is the molecular optical thickness  is the wavelength surf is the surface reflectance θs is the sun zenith angle θv is the view zenith angle φ is the relative azimuth ** Monte Carlo is used only as an auxiliary means here. 16

  17. Molecular Atmosphere: Results We calculate the absolute values of average relative differences: 17

  18. Aerosol Atmosphere: Conditions 6SV1.1 is compared to Monte Carlo and then the other codes are compared to 6SV1.1... 18

  19. Aerosol Atmosphere: Results (compared to MC) ... 6SV1.1 can be used as benchmark because it demonstrates good agreement with MC θs = {0.0°, 23.0°, 50.0°} black soil 19

  20. Aerosol Atmosphere: Results (compared to 6SV) We calculate the absolute values of average relative differences: * maer is the selected aerosol model 20

  21. Ozone, Stratospheric Aerosols 20 Km O2, CO2 Trace Gases 8 Km Molecules (Rayleigh Scattering) 2-3 Km H2O, Tropospheric Aerosol Ground Surface Mixed Atmosphere: Conditions We simply added a molecular atmosphere to all considered aerosol models. Profiles: Mixture: Molecular optical thickness:  = 412 nm - mol = 0.303  = 440 nm - mol = 0.232  = 670 nm - mol = 0.042 21

  22. Mixed Atmosphere: Results (compared to MC) 6SV1.1 demonstrates relatively good agreement with MC (within 0.85%) Molecular + Urban-industrial aerosol aer = 0.2, θs = 0.0° θs = {0.0°, 23.0°, 50.0°} mol = 0.303 black soil aer = 0.8, θs = 0.0° 22

  23. Mixed Atmosphere: Results (compared to 6SV) Again, we calculate the absolute values of average relative differences: 23

  24. Accuracy vs. Speed Time for 1 run (the case of a mixed atmosphere (λ = 440 nm, AF, aer = 0.8) + surface): SHARM: ≈ 5.6 s (7.3 s for a number of angles 6 x 16 x 3) 6SV1.1: ≈ 3 s (this time x number of SZA) Monte Carlo: ≈ 45 min (for one SZA) Time is important: code comparison like this one Time is not that important: calculation of LUTs Accuracy depends on many factors: SHARM: the number of harmonics 6SV1: the number of Legendre coefficients, calculation layers and angles 24

  25. Aerosol atmosphere by MODTRAN: VPD for a molecular atmosphere Molecular atmosphere by VPD: Good results for a molecular atmosphere do not mean that the accuracy of aerosol simulations will be satisfactory! 25

  26. 1) is the TOA reflectance of a vector code, is the TOA reflectance of a scalar code, is the error of a scalar code, 2) From (1) and (2) we can calculate the AOT retrieval error: , where Error on AOT Retrieval: Theory The accuracy of 6SV retrievals ? 1% (compared to MC) Assumption: TOA reflectance is a linear function of AOT 26

  27. Error on AOT retrieval: Results Molecular + Aerosol (African Savanna,aer = 0.2): SHARM:aer = 0.2 ± 0.14 6SV:aer = 0.2 ± 0.01 27

  28. Error on AOT retrieval: Results (Cont.) Molecular + Aerosol (African Savanna,aer = 0.8): SHARM:aer = 0.8 ± 0.15 6SV:aer = 0.8 ± 0.05 28

  29. 490 nm 470 nm 412 nm 443 nm 470 nm 670 nm 0.5 0.2 0.4 AOT Ex.: Part of Arabian Peninsula, day 207 of 2005 Ex.: AERONET site Alta Floresta, day 197 of 2003 AOT Retrieval from MODIS data MODIS Land Surface Reflectance algorithm by Vermote et al. Multi-Angle Implementation of Atmospheric Correction for MODIS by Lyapustin & Wang 29

  30. 1) 2) The SR retrieval error: , where Error on SR Retrieval: Theory The same procedure as for AOT retrievals, but is replaced by surface reflectance L (L = 0.05, dL = 0.01) 30

  31. Error on SR Retrieval: Results Molecular + Aerosol (Amazonian Forest, aer = 0.2) + Surface (Lambertian,surf = 0.05): SHARM:surf = 0.05 ± 0.01 6SV:surf = 0.05 ± 0.002 31

  32. Error on SR Retrieval: Results (Cont.) Molecular + Aerosol (Amazonian Forest, aer = 0.8) + Surface (Lambertian,surf = 0.05): SHARM:surf = 0.05 ± 0.01 6SV:surf = 0.05 ± 0.003 32

  33. pre-assigned set of aerosol models: Smoke LABS Smoke HABS Urban POLU Urban CLEAN + Vector ? Scalar for Remote Sensing Is it important to use a vector code? 1) AOT (+ other aerosol properties) retrievals: important, from a theoretical point of view 2) surface reflectance retrievals: important The accuracy of LUTs directly depends on the RT code simulations. The best solution is to calculate a product error budget. 33

  34. Reference data set The goal is to use 6SV1 to create a reference data set for further code comparison studies. 34

  35. ... Thank you for your attention! Questions?.. skotchen@ltdri.org 35

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