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Identifying Absorbing Aerosols Above Clouds From the Spinning Enhanced Visible and Infrared Imager Coupled With NASA A-Train Multiple Sensors
Geostationary satellite data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) in conjunction with A-Train data are used to develop an algorithm for detecting biomass-burning smoke aerosols above closed-cell stratocumulus (Sc) clouds. The detection relies on spectral signatures, textural characteristics, and time-dependent spectral variation of SEVIRI data. A-Train data including the Ozone Monitoring Instrument (OMI) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used as reference data for the SEVIRI algorithm development. The 15-min repeat cycle of SEVIRI provides the capability for identifying smoke above closed-cell Sc with an OMI aerosol index value exceeding 0.5 and a cloud optical thickness greater than 6 at 0.81 \mu\mbox{m}. The user accuracy of this algorithm is ∼49% when using only spectral signature and textural tests. When incorporating the "temporal consistency" tests into the algorithm, the user accuracy increases to ∼65%. The producer accuracy is over ∼77%, implying that the SEVIRI algorithm generally identifies smoke above clouds when CALIOP also identifies the same feature at the collocated pixel. However, CALIOP has the tendency to underestimate the presence of thin smoke aerosols above liquid clouds during daytime. This algorithm can be used to detect and study the daytime variation of smoke above liquid clouds.
Identifying Absorbing Aerosols Above Clouds From the Spinning Enhanced Visible and Infrared Imager Coupled With NASA A-Train Multiple Sensors
Geostationary satellite data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) in conjunction with A-Train data are used to develop an algorithm for detecting biomass-burning smoke aerosols above closed-cell stratocumulus (Sc) clouds. The detection relies on spectral signatures, textural characteristics, and time-dependent spectral variation of SEVIRI data. A-Train data including the Ozone Monitoring Instrument (OMI) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used as reference data for the SEVIRI algorithm development. The 15-min repeat cycle of SEVIRI provides the capability for identifying smoke above closed-cell Sc with an OMI aerosol index value exceeding 0.5 and a cloud optical thickness greater than 6 at 0.81 \mu\mbox{m}. The user accuracy of this algorithm is ∼49% when using only spectral signature and textural tests. When incorporating the "temporal consistency" tests into the algorithm, the user accuracy increases to ∼65%. The producer accuracy is over ∼77%, implying that the SEVIRI algorithm generally identifies smoke above clouds when CALIOP also identifies the same feature at the collocated pixel. However, CALIOP has the tendency to underestimate the presence of thin smoke aerosols above liquid clouds during daytime. This algorithm can be used to detect and study the daytime variation of smoke above liquid clouds.
Identifying Absorbing Aerosols Above Clouds From the Spinning Enhanced Visible and Infrared Imager Coupled With NASA A-Train Multiple Sensors
Chang, Ian (author) / Christopher, Sundar A
2016
Article (Journal)
English
Local classification TIB:
770/3710/5670
BKL:
38.03
Methoden und Techniken der Geowissenschaften
/
74.41
Luftaufnahmen, Photogrammetrie
Clouds - Cloud Statistics Measured With the Infrared Cloud Imager (ICI)
| Online Contents | 2005