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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Main Author: Wang, Zhien
Language:unknown
Published: 2009
Subjects:
Online Access:http://www.osti.gov/servlets/purl/861985
https://www.osti.gov/biblio/861985
https://doi.org/10.2172/861985
id ftosti:oai:osti.gov:861985
record_format openpolar
spelling 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
institution Open Polar
collection SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy)
op_collection_id ftosti
language unknown
topic 54 ENVIRONMENTAL SCIENCES
ALGORITHMS
CLASSIFICATION
CLIMATES
CLOUDS
OPTICAL RADAR
PRODUCTION
RADAR
RADIOMETERS
TRANSLATORS
WATER
spellingShingle 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
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