Arctic cloud characteristics as derived from MODIS, CALIPSO, and cloudsat

The Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and CloudSat Cloud Profiling Radar (CPR) set of sensors, all in the Afternoon Constellation (A-Train), has been regarded as among the most powerful tools for characterizing the cloud...

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Main Authors: Chan, Mark Aaron, Comiso, Josefino C.
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Published: Animo Repository 2013
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/4125
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-50062021-10-11T08:24:46Z Arctic cloud characteristics as derived from MODIS, CALIPSO, and cloudsat Chan, Mark Aaron Comiso, Josefino C. The Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and CloudSat Cloud Profiling Radar (CPR) set of sensors, all in the Afternoon Constellation (A-Train), has been regarded as among the most powerful tools for characterizing the cloud cover. While providing good complementary information, the authors also observed that, at least for the Arctic region, the different sensors provide significantly different statistics about cloud cover characteristics. Data in 2007 and 2010 were analyzed, and the annual averages of cloud cover in the Arctic region were found to be 66.8%, 78.4%, and 63.3% as derived from MODIS, CALIOP, and CPR, respectively. A large disagreement between MODIS and CALIOP over sea ice and Greenland is observed, with a cloud percentage difference of 30.9% and 31.5%, respectively. In the entire Arctic, the average disagreement between MODIS and CALIOP increased from 13.1% during daytime to 26.7% during nighttime. Furthermore, the MODIS cloud mask accuracy has a high seasonal dependence, in that MODIS-CALIOP disagreement is the lowest during summertime at 10.7% and worst during winter at 28.0%. During nighttime the magnitude of the bias is higher because cloud detection is limited to the use of infrared bands. The clouds not detected by MODIS are typically low-level (top height <2 >km) and high-level clouds (top height.6 km) and, especially, those that are geometrically thin (<2 >km). Geometrically thin clouds (<2 >km) accounted for about 95.5% of all clouds that CPR misses. As reported in a similar study, very low and thin clouds ( 2013-05-01T07:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/4125 info:doi/10.1175/JCLI-D-12-00204.1 Faculty Research Work Animo Repository Clouds Sea ice Remote-sensing Optical radar Environmental Sciences
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Clouds
Sea ice
Remote-sensing
Optical radar
Environmental Sciences
spellingShingle Clouds
Sea ice
Remote-sensing
Optical radar
Environmental Sciences
Chan, Mark Aaron
Comiso, Josefino C.
Arctic cloud characteristics as derived from MODIS, CALIPSO, and cloudsat
description The Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and CloudSat Cloud Profiling Radar (CPR) set of sensors, all in the Afternoon Constellation (A-Train), has been regarded as among the most powerful tools for characterizing the cloud cover. While providing good complementary information, the authors also observed that, at least for the Arctic region, the different sensors provide significantly different statistics about cloud cover characteristics. Data in 2007 and 2010 were analyzed, and the annual averages of cloud cover in the Arctic region were found to be 66.8%, 78.4%, and 63.3% as derived from MODIS, CALIOP, and CPR, respectively. A large disagreement between MODIS and CALIOP over sea ice and Greenland is observed, with a cloud percentage difference of 30.9% and 31.5%, respectively. In the entire Arctic, the average disagreement between MODIS and CALIOP increased from 13.1% during daytime to 26.7% during nighttime. Furthermore, the MODIS cloud mask accuracy has a high seasonal dependence, in that MODIS-CALIOP disagreement is the lowest during summertime at 10.7% and worst during winter at 28.0%. During nighttime the magnitude of the bias is higher because cloud detection is limited to the use of infrared bands. The clouds not detected by MODIS are typically low-level (top height <2 >km) and high-level clouds (top height.6 km) and, especially, those that are geometrically thin (<2 >km). Geometrically thin clouds (<2 >km) accounted for about 95.5% of all clouds that CPR misses. As reported in a similar study, very low and thin clouds (
format text
author Chan, Mark Aaron
Comiso, Josefino C.
author_facet Chan, Mark Aaron
Comiso, Josefino C.
author_sort Chan, Mark Aaron
title Arctic cloud characteristics as derived from MODIS, CALIPSO, and cloudsat
title_short Arctic cloud characteristics as derived from MODIS, CALIPSO, and cloudsat
title_full Arctic cloud characteristics as derived from MODIS, CALIPSO, and cloudsat
title_fullStr Arctic cloud characteristics as derived from MODIS, CALIPSO, and cloudsat
title_full_unstemmed Arctic cloud characteristics as derived from MODIS, CALIPSO, and cloudsat
title_sort arctic cloud characteristics as derived from modis, calipso, and cloudsat
publisher Animo Repository
publishDate 2013
url https://animorepository.dlsu.edu.ph/faculty_research/4125
_version_ 1767196036794679296