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Radiative Transfer Codes for Atmospheric Correction and Aerosol Retrievals: Intercomparison Study AEROCENTER Fall Seminar Series, October 2 nd, 2007 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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Project Description 2 The project is devoted to the comparison and detailed evaluation of five atmospheric RT codes incorporated in different satellite data processing algorithms Coulsons tabulated values (benchmark) 6SV1.1 (vector) Svetlana & Eric SHARM (scalar) Alexei RT3 (vector) Robert VPD (vector) Omar MODTRAN (scalar) Svetlana Monte Carlo (benchmark) Svetlana & Eric only molecular atmosphere

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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) 3 MODTRAN ( Moderate Resolution Atmospheric Transmittance and Radiance Code ): AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) atmospheric correction

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

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Description of the codes: RT3 5 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)

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

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Description of the codes: MODTRAN 7 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 card 1a card 1 tape 5 – molecular atmosphere card 2

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Project History SV & VPD & RT3 & SHARM Coulsons tables Monte Carlo 6SV & RT3 & SHARM VPD discussions, calculations, Web site creation... Why do you ignore MODTRAN? MODTRAN

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Goals of the project to evaluate the accuracy of each code based on the comparison with standard benchmark references such as Coulsons 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

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Presentation of the results 10 All results will be put on the Internet and summarized in a manuscript titledRadiative Transfer Codes for Atmospheric Correction and Aerosol Retrievals: Intercomparison Study which will be submitted to Applied Optics.

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Characterization of a RT code 11 In regard to remote sensing applications 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

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Code Accuracy 12 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), , 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

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Benchmarks: Coulsons tables 13 Coulsons 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).

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Benchmarks: Monte Carlo 14 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.

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Comparison Procedure larger particles direction of incident light 1. Molecular Atmosphere ( surf = 0.0; 0.25 ) 3. Mixed Atmosphere ( surf = 0.0; 0.25 ) 2. Aerosol Atmosphere ( surf = 0.0 ) surf is the reflectance of a Lambertian surface The same procedure was used in 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),

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Molecular Atmosphere: Conditions mol (, nm) 0.1 (530)0.25 (440)0.5 (360) surf θ s, deg θ v, deg.as in Coulsons tables φ, deg. 0.0; 90.0; 180 * mol is the molecular optical thickness is the wavelength surf is the surface reflectance θ s is the sun zenith angle θ v is the view zenith angle φ is the relative azimuth All RT codes are compared to the Coulsons tabulated values. ** Monte Carlo is used only as an auxiliary means here. 16

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

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Aerosol Atmosphere: Conditions ModelUrban-Industrial and MixedBiomass Burning Location GSFC, Greenbelt, MD (1993–2000) Amazonian forest, Brazil ( ), Bolivia ( ) African savanna, Zambia ( ) Range of τ aer 0.1 (440) (440) (440) 1.5 Values of τ aer selected for this study 0.2; ; 0.8; ; 0.8 Real and imaginary parts of refractive index (440); ; ; SSA at = 412/440/670 nm 0.97/0.98/ /0.94/ /0.88/0.84 r Vf, m (440) (440) (440) f, m r Vc, m (440) (440) (440) c, m C Vf, m 3 / m (440)0.12 (440) C Vc, m 3 / m (440)0.05 (440) 0.09 (440) 6SV1.1 is compared to Monte Carlo and then the other codes are compared to 6SV

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

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

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

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

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

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Accuracy vs. Speed 24 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

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

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Error on AOT Retrieval: Theory 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 Assumption: TOA reflectance is a linear function of AOT The accuracy of 6SV retrievals ? 1% (compared to MC) 26

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Error on AOT retrieval: Results Molecular + Aerosol (African Savanna, aer = 0.2 ): SHARM: aer = 0.2 ± SV: aer = 0.2 ±

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

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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 490 nm 470 nm 443 nm 412 nm AOT Ex.: AERONET site Alta Floresta, day 197 of 2003 Ex.: Part of Arabian Peninsula, day 207 of nm 670 nm 29

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

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

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

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

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

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