Multi-sensor data assimilation of aerosol optical depth

As a result of increasing attention paid to aerosols in climate studies, numerous global satellite aerosol products have been generated. There exists, however, an outstanding problem that these satellite products have substantial discrepancies, that must be lowered substantially for narrowing the ra...

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Bibliographic Details
Published in2011 IEEE International Geoscience and Remote Sensing Symposium pp. 3253 - 3256
Main Authors Hui Xu, Yong Xue, Jie Guang, Yingjie Li, Ying Wang, Linlu Mei
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2011
Subjects
Aerosol optical depth
Aerosols
Covariance matrix
Data assimilation
Kalman filter
Meteorology
MISR
MODIS
Optical filters
optimal interpolation
Satellites
three-dimensional variation
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Summary:As a result of increasing attention paid to aerosols in climate studies, numerous global satellite aerosol products have been generated. There exists, however, an outstanding problem that these satellite products have substantial discrepancies, that must be lowered substantially for narrowing the range of the estimates of aerosol's climate effects. In this paper, three different data assimilation methods were used to produce consistent aerosol optical depth (AOD) with four different derived AOD products. The results illustrate that the data assimilation method can produce comprehensive AOD fields with reasonably good data values and acceptable errors. Through comparing, the Kalman filter method is more preferable to the optimal interpolation and three-dimensional variation method.
ISBN:145771003X
9781457710032
ISSN:2153-6996
2153-7003
DOI:10.1109/IGARSS.2011.6049913