Utilizing the Intersection Between Simulated and Observed Hyperspectral Solar Reflectance

1. Utilizing the Intersection Between Simulated and Observed Hyperspectral Solar Reflectance Y. Roberts, P. Pilewskie, B. Kindel Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, CO Collaborators: D. Feldman and W. Collins Lawrence Berkeley National Laboratory

2. SDT Tasks • Trend Detection in Spectral Radiance Task Summary Objective: Extract trends in TOA outgoing shortwave spectral radiance. Method: PCA, examining PC score time series, and SSA/MSSA for trend extraction. Data: Observed SCIAMACHY and simulated radiative transfer (MODTRAN) shortwave spectral radiance Tools: PCA using IDL/ENVI; SSA; MODTRAN. Expected outcomes: Validation of trend detection methods with measured shortwave radiance and modeled simulations with known forcings; improved quantification and refinement of CLARREO requirements.

3. SDT Tasks • Trend Detection in Spectral Radiances Roberts, Y., P. Pilewskie, B. C. Kindel. (2011), Evaluating the Observed Variability in Hyperspectral Earth-reflected Solar Radiance, J. Geophys. Res., 116, D24119, doi:10.1029/2011JD016448.

4. SDT Tasks • Intersection of Spectrally Decomposed Subspaces Task Summary Objective: Use intersection to evaluate modeled reflectances with SCIAMACHY reflectance. Attempt to separate the underlying physical variables that explain the variance in the measurements. Method: Numerical methods to determine the angles between complementary subspaces. Look-up tables to match model input to variance as depicted by measurement eigenvectors. Data: Observed SCIAMACHY and simulated radiative transfer shortwave spectral reflectance from Langley and UC-Berkeley groups. Tools: PCA using IDL/ENVI; MODTRAN; IDL and multivariate numerical methods Expected outcome: Improved attribution techniques that identify physical variables driving spectral variability; improved quantification and refinement of CLARREO requirements.

5. Outline • Why Reflectance? • Quantitative comparison description • Reflectance PCA results • Reflectance subspace comparison • Method to link model inputs to observations • Examples of intersection attribution method using OSSE and SCIA data

6. Why Reflectances for Quantitative Comparison? • Unstandardized PCA needed in quantitative comparison method • Normalizing by the standard deviation removes important information about the data sets and what makes them different. • Without normalizing the data, the spectral shape of the downwelling solar irradiance is still removed through reflectance computation • SCIAMACHY takes solar irradiance reference measurements and nadir Earth-reflected measurements with the same sensors – the division in calculating reflectance cancels out systematic instrument defects

7. Comparing SCIAMACHY and OSSE Reflectances • SCIAMACHY nadir reflectances • Spatial grid: 5.625° (4x the original OSSE output) • Monthly averaged, spatially gridded, 10 nm FWHM • OSSEs all-sky reflectances • Spatially averaged and spectrally resampled over the same spatial grid and with spectral resolution • Limited to locations present in SCIAMACHY data

8. Quantitative Comparison of Subspaces OSSE Reflectances SCIA Reflectances PCA SCIA Eigenvectors OSSE Eigenvectors CalculateIntersection SVD Spectrally Decompose Intersection 2 1 3 The relationship between each pair of transformed eigenvectors. Range = [0,Subspace Dimension] SCIA Transformed Eigenvectors OSSE Transformed Eigenvectors Roberts Y., P. Pilewskie, B. C. Kindel, D. R. Feldman, and W. D. Collins, [In preparation] Quantitative Comparison of the Variability in Observed and Simulated Reflected Shortwave Reflectance.

9. Retain 7 PC dimensions for the comparison.

12. Using similarity significance method found six dimensions to be equivalent.

14. Intersection Look-up Table Method PCA Space 1. For each PC, find the SCIA spectra corresponding to scores more than 3 standard deviations from the mean. SCIA PCA Scores Transformed Space 2. Using the spectra found in (1.), calculate the Euclidean distance between the corresponding Shared Intersection SCIA Scores and all LUT Intersection Scores. LUT Shared Intersection Scores SCIA Shared Intersection Scores This finds LUT spectrum with closest spectral shape to SCIA spectrum of interest. 3. Find the minimum Euclidean distance for each spectrum. Measurement Space 4. Examine LUT inputs used to simulate reflectances to understand which model inputs drive measured variance. SCIA Reflectances LUT Reflectances LUT Physical Inputs

15. To use the October 2004 OSSE Reflectances as a LUT, recalculated PCA using all OSSE spectra without re-gridding to align with SCIAMACHY 5° grid.

16. Four dimensions were used to find the matching spectra between OSSE and SCIA. Using transformed dimensions with correlations greater than 0.95 work best.

18. Extreme Positive Scores Extreme Negative Scores

19. Six BestSpectra Matches from Most Negative PC01 Scores SCIA OSSE

21. Six Best Spectra Matches from Most Positive PC01 Scores SCIA OSSE

23. Summary • Reflectance PCA • OSSE and SCIA share 6 dimensions that explain over 99.5 % total variance • Some physical spectral signals not apparent in standardized or unstandardized radiance PCA • Intersection Look-up Table Method • Use intersection to match the spectral shape of observations to simulated spectra efficiently • Quickly matching the spectral shapes provides link between model physical inputs to observed data variance drivers

24. Future Work • Applying intersection method to actual LUT for improved variance driver attribution • Comparison of SCIA and OSSE decadal trends • Trend detection to study centennial time-scale patterns in OSSEs for different emission scenarios • Quantifying data set differences in addition to similarities