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| Titel |
Monsoonal variations in aerosol optical properties and estimation of aerosol optical depth using ground-based meteorological and air quality data in Peninsular Malaysia |
| VerfasserIn |
F. Tan, H. S. Lim, K. Abdullah, T. L. Yoon, B. Holben |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 15, no. 7 ; Nr. 15, no. 7 (2015-04-07), S.3755-3771 |
| Datensatznummer |
250119617
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| Publikation (Nr.) |
 copernicus.org/acp-15-3755-2015.pdf |
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| Zusammenfassung |
| Obtaining continuous aerosol-optical-depth (AOD) measurements is a difficult task due to the cloud-cover
problem. With the main motivation of overcoming this
problem, an AOD-predicting model is proposed. In this study, the optical properties of aerosols in
Penang, Malaysia were analyzed for four monsoonal seasons (northeast
monsoon, pre-monsoon, southwest monsoon, and post-monsoon) based on data
from the AErosol RObotic NETwork (AERONET) from February 2012 to
November 2013. The aerosol distribution patterns in Penang for each
monsoonal period were quantitatively identified according to the scattering
plots of the Ångström exponent against the AOD. A new empirical algorithm was proposed to predict the AOD data. Ground-based
measurements (i.e., visibility and air pollutant index) were used in the
model as predictor data to retrieve the missing AOD data from AERONET due to
frequent cloud formation in the equatorial region. The model coefficients
were determined through multiple regression analysis using selected data set
from in situ data. The calibrated model coefficients have a coefficient of
determination, R2, of 0.72. The predicted AOD of the model was generated
based on these calibrated coefficients and compared against the measured
data through standard statistical tests, yielding a R2 of 0.68 as
validation accuracy. The error in weighted mean absolute percentage error
(wMAPE) was less than 0.40% compared with the real data. The results
revealed that the proposed model efficiently predicted the AOD data.
Performance of our model was compared against selected LIDAR data to yield
good correspondence. The predicted AOD can enhance measured short- and
long-term AOD and provide supplementary information for climatological
studies and monitoring aerosol variation. |
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