Using Radar, Lidar, and Radiometer measurements to Classify Cloud Type and Study Middle-Level Cloud Properties
The project is concerned with the characterization of cloud macrophysical and microphysical properties by combining radar, lidar, and radiometer measurements available from the U.S. Department of Energy's ARM Climate Research Facility (ACRF). To facilitate the production of integrated cloud pro...
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ftunivnotexas:info:ark/67531/metadc794273 2023-05-15T14:55:40+02:00 Using Radar, Lidar, and Radiometer measurements to Classify Cloud Type and Study Middle-Level Cloud Properties Wang, Zhien United States. Department of Energy. Office of Science. 2006-01-04 768000 Text https://doi.org/10.2172/861985 http://digital.library.unt.edu/ark:/67531/metadc794273/ English eng University of Maryland, Baltimore County rep-no: DOE/ER63536 grantno: FG02-03ER63536 doi:10.2172/861985 osti: 861985 http://digital.library.unt.edu/ark:/67531/metadc794273/ ark: ark:/67531/metadc794273 Clouds Classification Cloud Property Retrieval Algorithm Mixed-Phase Clouds Cloud Type Radiometers Production Translators Algorithms Climates Radar Water Cloud Property Optical Radar 54 Environmental Sciences Report 2006 ftunivnotexas https://doi.org/10.2172/861985 2016-08-13T22:11:25Z The project is concerned with the characterization of cloud macrophysical and microphysical properties by combining radar, lidar, and radiometer measurements available from the U.S. Department of Energy's ARM Climate Research Facility (ACRF). To facilitate the production of integrated cloud product by applying different algorithms to the ARM data streams, an advanced cloud classification algorithm was developed to classified clouds into eight types at the SGP site based on ground-based active and passive measurements. Cloud type then can be used as a guidance to select an optimal retrieval algorithm for cloud microphysical property retrieval. The ultimate goal of the effort is to develop an operational cloud classification algorithm for ARM data streams. The vision 1 IDL code of the cloud classification algorithm based on the SGP ACRF site observations was delivered to the ARM cloud translator during 2004 ARM science team meeting. Another goal of the project is to study midlevel clouds, especially mixed-phase clouds, by developing new retrieval algorithms using integrated observations at the ACRF sites. Mixed-phase clouds play a particular role in the Arctic climate system. A multiple remote sensor based algorithm, which can provide ice water content and effective size profiles, liquid water path, and layer-mean effective radius of water droplet, was developed to study arctic mixed-phase clouds. The algorithm is applied to long-term ARM observations at the NSA ACRF site. Based on these retrieval results, we are studying seasonal and interannual variations of arctic mixed-phase cloud macro- and micro-physical properties. Report Arctic University of North Texas: UNT Digital Library Arctic |
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Open Polar |
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University of North Texas: UNT Digital Library |
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ftunivnotexas |
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English |
topic |
Clouds Classification Cloud Property Retrieval Algorithm Mixed-Phase Clouds Cloud Type Radiometers Production Translators Algorithms Climates Radar Water Cloud Property Optical Radar 54 Environmental Sciences |
spellingShingle |
Clouds Classification Cloud Property Retrieval Algorithm Mixed-Phase Clouds Cloud Type Radiometers Production Translators Algorithms Climates Radar Water Cloud Property Optical Radar 54 Environmental Sciences Wang, Zhien Using Radar, Lidar, and Radiometer measurements to Classify Cloud Type and Study Middle-Level Cloud Properties |
topic_facet |
Clouds Classification Cloud Property Retrieval Algorithm Mixed-Phase Clouds Cloud Type Radiometers Production Translators Algorithms Climates Radar Water Cloud Property Optical Radar 54 Environmental Sciences |
description |
The project is concerned with the characterization of cloud macrophysical and microphysical properties by combining radar, lidar, and radiometer measurements available from the U.S. Department of Energy's ARM Climate Research Facility (ACRF). To facilitate the production of integrated cloud product by applying different algorithms to the ARM data streams, an advanced cloud classification algorithm was developed to classified clouds into eight types at the SGP site based on ground-based active and passive measurements. Cloud type then can be used as a guidance to select an optimal retrieval algorithm for cloud microphysical property retrieval. The ultimate goal of the effort is to develop an operational cloud classification algorithm for ARM data streams. The vision 1 IDL code of the cloud classification algorithm based on the SGP ACRF site observations was delivered to the ARM cloud translator during 2004 ARM science team meeting. Another goal of the project is to study midlevel clouds, especially mixed-phase clouds, by developing new retrieval algorithms using integrated observations at the ACRF sites. Mixed-phase clouds play a particular role in the Arctic climate system. A multiple remote sensor based algorithm, which can provide ice water content and effective size profiles, liquid water path, and layer-mean effective radius of water droplet, was developed to study arctic mixed-phase clouds. The algorithm is applied to long-term ARM observations at the NSA ACRF site. Based on these retrieval results, we are studying seasonal and interannual variations of arctic mixed-phase cloud macro- and micro-physical properties. |
author2 |
United States. Department of Energy. Office of Science. |
format |
Report |
author |
Wang, Zhien |
author_facet |
Wang, Zhien |
author_sort |
Wang, Zhien |
title |
Using Radar, Lidar, and Radiometer measurements to Classify Cloud Type and Study Middle-Level Cloud Properties |
title_short |
Using Radar, Lidar, and Radiometer measurements to Classify Cloud Type and Study Middle-Level Cloud Properties |
title_full |
Using Radar, Lidar, and Radiometer measurements to Classify Cloud Type and Study Middle-Level Cloud Properties |
title_fullStr |
Using Radar, Lidar, and Radiometer measurements to Classify Cloud Type and Study Middle-Level Cloud Properties |
title_full_unstemmed |
Using Radar, Lidar, and Radiometer measurements to Classify Cloud Type and Study Middle-Level Cloud Properties |
title_sort |
using radar, lidar, and radiometer measurements to classify cloud type and study middle-level cloud properties |
publisher |
University of Maryland, Baltimore County |
publishDate |
2006 |
url |
https://doi.org/10.2172/861985 http://digital.library.unt.edu/ark:/67531/metadc794273/ |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
rep-no: DOE/ER63536 grantno: FG02-03ER63536 doi:10.2172/861985 osti: 861985 http://digital.library.unt.edu/ark:/67531/metadc794273/ ark: ark:/67531/metadc794273 |
op_doi |
https://doi.org/10.2172/861985 |
_version_ |
1766327685647171584 |