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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ftosti:oai:osti.gov:861985 2023-07-30T04:01:22+02:00 Using Radar, Lidar, and Radiometer measurements to Classify Cloud Type and Study Middle-Level Cloud Properties Wang, Zhien 2009-12-10 application/pdf http://www.osti.gov/servlets/purl/861985 https://www.osti.gov/biblio/861985 https://doi.org/10.2172/861985 unknown http://www.osti.gov/servlets/purl/861985 https://www.osti.gov/biblio/861985 https://doi.org/10.2172/861985 doi:10.2172/861985 54 ENVIRONMENTAL SCIENCES ALGORITHMS CLASSIFICATION CLIMATES CLOUDS OPTICAL RADAR PRODUCTION RADAR RADIOMETERS TRANSLATORS WATER 2009 ftosti https://doi.org/10.2172/861985 2023-07-11T08:40:58Z 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. Other/Unknown Material Arctic SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Arctic |
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SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
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54 ENVIRONMENTAL SCIENCES ALGORITHMS CLASSIFICATION CLIMATES CLOUDS OPTICAL RADAR PRODUCTION RADAR RADIOMETERS TRANSLATORS WATER |
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54 ENVIRONMENTAL SCIENCES ALGORITHMS CLASSIFICATION CLIMATES CLOUDS OPTICAL RADAR PRODUCTION RADAR RADIOMETERS TRANSLATORS WATER Wang, Zhien Using Radar, Lidar, and Radiometer measurements to Classify Cloud Type and Study Middle-Level Cloud Properties |
topic_facet |
54 ENVIRONMENTAL SCIENCES ALGORITHMS CLASSIFICATION CLIMATES CLOUDS OPTICAL RADAR PRODUCTION RADAR RADIOMETERS TRANSLATORS WATER |
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. |
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 |
publishDate |
2009 |
url |
http://www.osti.gov/servlets/purl/861985 https://www.osti.gov/biblio/861985 https://doi.org/10.2172/861985 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
http://www.osti.gov/servlets/purl/861985 https://www.osti.gov/biblio/861985 https://doi.org/10.2172/861985 doi:10.2172/861985 |
op_doi |
https://doi.org/10.2172/861985 |
_version_ |
1772812101380734976 |