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Volume 13, issue 6
Atmos. Chem. Phys., 13, 3133–3147, 2013
https://doi.org/10.5194/acp-13-3133-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 13, 3133–3147, 2013
https://doi.org/10.5194/acp-13-3133-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 15 Mar 2013

Research article | 15 Mar 2013

Quantitative comparison of the variability in observed and simulated shortwave reflectance

Y. L. Roberts1,2,3, P. Pilewskie1,2, B. C. Kindel2, D. R. Feldman4, and W. D. Collins4,5 Y. L. Roberts et al.
  • 1Department of Atmospheric and Oceanic Science, University of Colorado – Boulder, Boulder, CO, USA
  • 2Laboratory of Atmospheric and Space Science, Boulder, CO, USA
  • 3NASA Langley Research Center, Hampton, VA, USA
  • 4Lawrence Berkeley National Lab, Berkeley, CA, USA
  • 5Department of Earth and Planetary Science, University of California – Berkeley, Berkeley, CA, USA

Abstract. The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is a climate observation system that has been designed to monitor the Earth's climate with unprecedented absolute radiometric accuracy and SI traceability. Climate Observation System Simulation Experiments (OSSEs) have been generated to simulate CLARREO hyperspectral shortwave imager measurements to help define the measurement characteristics needed for CLARREO to achieve its objectives. To evaluate how well the OSSE-simulated reflectance spectra reproduce the Earth's climate variability at the beginning of the 21st century, we compared the variability of the OSSE reflectance spectra to that of the reflectance spectra measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY). Principal component analysis (PCA) is a multivariate decomposition technique used to represent and study the variability of hyperspectral radiation measurements. Using PCA, between 99.7% and 99.9% of the total variance the OSSE and SCIAMACHY data sets can be explained by subspaces defined by six principal components (PCs). To quantify how much information is shared between the simulated and observed data sets, we spectrally decomposed the intersection of the two data set subspaces. The results from four cases in 2004 showed that the two data sets share eight (January and October) and seven (April and July) dimensions, which correspond to about 99.9% of the total SCIAMACHY variance for each month. The spectral nature of these shared spaces, understood by examining the transformed eigenvectors calculated from the subspace intersections, exhibit similar physical characteristics to the original PCs calculated from each data set, such as water vapor absorption, vegetation reflectance, and cloud reflectance.

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