 |
| Titel |
Application of spectral analysis techniques to the intercomparison of aerosol data – Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis |
| VerfasserIn |
J. Li, B. E. Carlson, A. A. Lacis |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1867-1381
|
| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 7, no. 8 ; Nr. 7, no. 8 (2014-08-14), S.2531-2549 |
| Datensatznummer |
250115872
|
| Publikation (Nr.) |
 copernicus.org/amt-7-2531-2014.pdf |
|
|
|
|
|
| Zusammenfassung |
| In this paper, we introduce the usage of a newly developed spectral
decomposition technique – combined maximum covariance analysis (CMCA) – in
the spatiotemporal comparison of four satellite data sets and ground-based
observations of aerosol optical depth (AOD). This technique is based on
commonly used principal component analysis (PCA) and maximum covariance
analysis (MCA). By decomposing the cross-covariance matrix between the joint
satellite data field and Aerosol Robotic Network (AERONET) station data,
both parallel comparison across different satellite data sets and the
evaluation of the satellite data against the AERONET measurements are
simultaneously realized. We show that this new method not only confirms the
seasonal and interannual variability of aerosol optical depth, aerosol-source regions and events represented by different satellite data sets, but
also identifies the strengths and weaknesses of each data set in capturing
the variability associated with sources, events or aerosol types.
Furthermore, by examining the spread of the spatial modes of different
satellite fields, regions with the largest uncertainties in aerosol
observation are identified. We also present two regional case studies that
respectively demonstrate the capability of the CMCA technique in assessing
the representation of an extreme event in different data sets, and in
evaluating the performance of different data sets on seasonal and interannual
timescales. Global results indicate that different data sets agree
qualitatively for major aerosol-source regions. Discrepancies are mostly
found over the Sahel, India, eastern and southeastern Asia. Results for eastern
Europe suggest that the intense wildfire event in Russia during summer 2010
was less well-represented by SeaWiFS (Sea-viewing Wide Field-of-view Sensor) and OMI (Ozone Monitoring Instrument), which might be due to
misclassification of smoke plumes as clouds. Analysis for the Indian
subcontinent shows that here SeaWiFS agrees best with AERONET in terms of
seasonality for both the Gangetic Basin and southern India, while on
interannual timescales it has the poorest agreement. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|