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Extracting Atmospheric and Surface Information from AVIRIS Spectra Vijay Natraj, Daniel Feldman, Xun Jiang, Jack Margolis and Yuk Yung California Institute.

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Presentation on theme: "Extracting Atmospheric and Surface Information from AVIRIS Spectra Vijay Natraj, Daniel Feldman, Xun Jiang, Jack Margolis and Yuk Yung California Institute."— Presentation transcript:

1 Extracting Atmospheric and Surface Information from AVIRIS Spectra Vijay Natraj, Daniel Feldman, Xun Jiang, Jack Margolis and Yuk Yung California Institute of Technology May 24, 2005

2 Objectives  Calculate information content of trace gas concentration  Investigate effect of signal-to-noise ratio (SNR) on retrieval precision  Compare clear and aerosol-laden scenes  Assess impact of varying surface types  Analyze benefits of physics-based retrieval

3 Retrieval Technique  Forward Model Description of radiative transfer in the atmosphere Simulation of instrument response  Inverse Method Iteration to best match the observed spectrum Optimal Estimation Theory (Rodgers, 2000)

4 Physics of Radiative Transfer  Fundamental equation of radiative transfer  Multiple scattering algorithm: Isaacs 2-stream, DISORT 16- stream  Radiative transport algorithm: standard band model with Curtis-Godson path averaging  Band model resolution: 5 cm -1 ; 33 absorption coefficients per spectral bin

5 Optimal Estimation  Measurement Description  Minimization of Cost Function

6 Need for a priori Information  Ill-posed, nonlinear inversion problem  A priori make problem well-posed  Based on what we already know about the atmospheric or surface state  Obtained from climatological data, radiosonde measurements, or ad hoc descriptions  Care needed in selecting a priori

7 Weighting Functions: CO 2 Pressure (mbar) Weighting Function (  W/m 2 /sr/nm/ppmv)

8 Weighting Functions: H 2 O Weighting Function (  W/m 2 /sr/nm/ppmv) Pressure (mbar)

9 Weighting Functions: CH 4 Weighting Function (  W/m 2 /sr/nm/ppmv) Pressure (mbar)

10 Wavelength (nm) Δ Radiance (  W/m 2 /sr/nm) Column Weighting Function: CO 2

11 Wavelength (nm) Δ Radiance (  W/m 2 /sr/nm) Column Weighting Function: H 2 O

12 Wavelength (nm) Δ Radiance (  W/m 2 /sr/nm) Column Weighting Function: CH 4

13 Wavelength (nm) Δ Radiance (  W/m 2 /sr/nm) Weighting Function: Surface Pressure

14 Weighting Function: Surface Type I Wavelength (nm) Δ Radiance (  W/m 2 /sr/nm)

15 Weighting Function: Surface Type II Wavelength (nm) Δ Radiance (  W/m 2 /sr/nm)

16 Weighting Function: Surface Type III Wavelength (nm) Δ Radiance (  W/m 2 /sr/nm)

17 Retrieval Parameters  CO 2 column scaling factor  CH 4 column scaling factor  H 2 O column scaling factor  Surface pressure  Fractions of three surface types (1: conifer, 2: cropland, 3: desert)

18 Model Parameters  11-layer atmosphere (5 in stratosphere)  Solar Zenith Angle = 50 degrees  Nadir Viewing  Aerosol "Rural" (bottom 2 layers), "Tropospheric" (next 4), "Background Stratospheric" (rest) Visibility = 2 km, 20 km, 200 km (clear sky)

19 SNR Retrieval Precision: H 2 O Column H 2 O column scaling factor-1 Black: a priori uncertainty Green: clear sky Red: aerosol (20 km visibility) Blue: aerosol (2 km visibility)

20 SNR Retrieval Precision: CO 2 Column CO 2 column scaling factor-1 Black: a priori uncertainty Green: clear sky Red: aerosol (20 km visibility) Blue: aerosol (2 km visibility)

21 SNR Retrieval Precision: CH 4 Column CH 4 column scaling factor-1 Black: a priori uncertainty Green: clear sky Red: aerosol (20 km visibility) Blue: aerosol (2 km visibility)

22 SNR Retrieval Precision: Surface Pressure Surface Pressure Uncertainty (mbar) Black: a priori uncertainty Green: clear sky Red: aerosol (20 km visibility) Blue: aerosol (2 km visibility)

23 SNR Retrieval Precision: Surface Type I Uncertainty in Fraction of Surface Type I Black: a priori uncertainty Green: clear sky Red: aerosol (20 km visibility) Blue: aerosol (2 km visibility)

24 SNR Retrieval Precision: Surface Type II Uncertainty in Fraction of Surface Type II Black: a priori uncertainty Green: clear sky Red: aerosol (20 km visibility) Blue: aerosol (2 km visibility)

25 SNR Retrieval Precision: Surface Type III Uncertainty in Fraction of Surface Type III Black: a priori uncertainty Green: clear sky Red: aerosol (20 km visibility) Blue: aerosol (2 km visibility)

26 Full Retrieval (SNR = 2000) Wavelength (nm) Radiance (  W/m 2 /sr/nm) Black: true Red: retrieved Blue: a priori Red: retrieved - true Blue: a priori - true Residual (  W/m 2 /sr/nm)

27 Retrieval of Surface Types Only (SNR = 2000) Wavelength (nm) Radiance (  W/m 2 /sr/nm) Residual (  W/m 2 /sr/nm) Black: true Red: retrieved Blue: a priori Red: retrieved - true Blue: a priori - true

28 Surface Retrieval Precision FractionFull Retrieval (%)Only Surface Retrieval (%) Surface Type 1 0.461.52 Surface Type 2 06.09 Surface Type 3 0.9925.2

29 Computer Specifications  Dual Processor Intel Xeon CPU  Clock speed: 2.2 GHz  RAM: 2 GB  8-9 minutes per retrieval

30 Conclusions  Different AVIRIS channels have different sensitivity to atmospheric/surface parameters  Retrieval precisions improve with increasing SNR  Even cloudy scenes can be retrieved  Error in atmospheric correction can lead to significant error in surface retrieval

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