 |
| Titel |
Quantitative comparison of the variability in observed and simulated shortwave reflectance |
| VerfasserIn |
Y. L. Roberts, P. Pilewskie, B. C. Kindel, D. R. Feldman, W. D. Collins |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1680-7316
|
| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 13, no. 6 ; Nr. 13, no. 6 (2013-03-15), S.3133-3147 |
| Datensatznummer |
250018523
|
| Publikation (Nr.) |
 copernicus.org/acp-13-3133-2013.pdf |
|
|
|
|
|
| Zusammenfassung |
| 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. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|