utilizing the intersection between simulated and observed hyperspectral solar reflectance y....
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Utilizing the Intersection Between Simulated and Observed Hyperspectral
Solar Reflectance
Y. Roberts, P. Pilewskie, B. KindelLaboratory for Atmospheric and Space Physics,
University of Colorado, Boulder, CO
Collaborators: D. Feldman and W. Collins Lawrence Berkeley National Laboratory
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SDT Tasks1. Trend Detection in Spectral RadianceTask SummaryObjective: 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 radianceTools: 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.
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SDT Tasks1. 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.
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SDT Tasks2. Intersection of Spectrally Decomposed SubspacesTask SummaryObjective: 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 methodsExpected outcome: Improved attribution techniques that identify physical
variables driving spectral variability; improved quantification and refinement of CLARREO requirements.
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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
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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
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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
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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.
Quantitative Comparison of SubspacesSCIA Reflectances OSSE Reflectances
SCIA Eigenvectors Calculate Intersection
Spectrally Decompose Intersection
The relationship between each pair of transformed eigenvectors. Range = [0,Subspace Dimension]
OSSE Eigenvectors
PCA
SCIA Transformed Eigenvectors
OSSE Transformed Eigenvectors
1 2 3
SVD
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Retain 7 PC dimensions for the comparison.
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Using similarity significance method found six dimensions to be equivalent.
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Intersection Look-up Table Method
SCIA PCA Scores
SCIA Shared Intersection Scores
LUT Shared Intersection Scores
1. For each PC, find the SCIA spectra corresponding to scores more than 3 standard deviations from the mean.
2. Using the spectra found in (1.), calculate the Euclidean distance between the corresponding Shared Intersection SCIA Scores and all LUT Intersection Scores.
4. Examine LUT inputs used to simulate reflectances to understand which model inputs drive measured variance.
3. Find the minimum Euclidean distance for each spectrum.
This finds LUT spectrum with closest spectral shape to SCIA spectrum of interest.
SCIA Reflectances LUT Reflectances
LUT Physical Inputs
PCA Space
Transformed Space
Measurement Space
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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.
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Four dimensions were used to find the matching spectra between OSSE and SCIA.
Using transformed dimensions with correlations greater than 0.95 work best.
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Extreme Positive ScoresExtreme Negative Scores
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Six Best Spectra Matches from Most Negative PC01 Scores
SCIAOSSE
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Six Best Spectra Matches from Most Positive PC01 Scores
SCIAOSSE
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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
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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