A Novel Machine Learning Algorithm for Cloud Detection Using AERI Measurement Data
Infrared hyperspectral remote sensing has been widely used in the field of meteorology. Many scientists have carried out research on inversion methods of meteorological elements such as thermodynamic profile, boundary layer height, cloud base height, etc. In this study, a method based on machine lea...
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Online Access: | http://www.osti.gov/servlets/purl/1872922 https://www.osti.gov/biblio/1872922 https://doi.org/10.3390/rs14112589 |
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ftosti:oai:osti.gov:1872922 2023-07-30T03:58:25+02:00 A Novel Machine Learning Algorithm for Cloud Detection Using AERI Measurement Data Liu, Lei Ye, Jin Li, Shulei Hu, Shuai Wang, Qi 2022-08-23 application/pdf http://www.osti.gov/servlets/purl/1872922 https://www.osti.gov/biblio/1872922 https://doi.org/10.3390/rs14112589 unknown http://www.osti.gov/servlets/purl/1872922 https://www.osti.gov/biblio/1872922 https://doi.org/10.3390/rs14112589 doi:10.3390/rs14112589 54 Environmental Sciences 47 OTHER INSTRUMENTATION 2022 ftosti https://doi.org/10.3390/rs14112589 2023-07-11T10:13:01Z Infrared hyperspectral remote sensing has been widely used in the field of meteorology. Many scientists have carried out research on inversion methods of meteorological elements such as thermodynamic profile, boundary layer height, cloud base height, etc. In this study, a method based on machine learning for cloud detection using ground-based infrared hyperspectral radiation data is proposed. The features of outliers, the cloudy and cloud-free data of Atmospheric Emitted Radiance Interferometer (AERI) radiation are extracted. The “reference values” of cloudy and cloud-free are determined based on the observation data of Vaisala CL31 ceilometer within the time range of 8 min before the corresponding time of AERI. A support vector machine (SVM) algorithm is used for training. The dataset comes from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site and North Slope Alaska (NSA) site from 2015 to 2017, and the ARM West Antarctic Radiation Experiment (AWARE) site in 2016 is also analyzed. The instruments used in this paper include AERI, ceilometer, etc. The experimental results reveal that the agreement of cloud detection results between the proposed algorithm and ceilometer is about 93% at each site. However, for high clouds or optically thin clouds, the agreement will decrease. Other/Unknown Material Antarc* Antarctic north slope Alaska SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Antarctic Remote Sensing 14 11 2589 |
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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 47 OTHER INSTRUMENTATION |
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54 Environmental Sciences 47 OTHER INSTRUMENTATION Liu, Lei Ye, Jin Li, Shulei Hu, Shuai Wang, Qi A Novel Machine Learning Algorithm for Cloud Detection Using AERI Measurement Data |
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54 Environmental Sciences 47 OTHER INSTRUMENTATION |
description |
Infrared hyperspectral remote sensing has been widely used in the field of meteorology. Many scientists have carried out research on inversion methods of meteorological elements such as thermodynamic profile, boundary layer height, cloud base height, etc. In this study, a method based on machine learning for cloud detection using ground-based infrared hyperspectral radiation data is proposed. The features of outliers, the cloudy and cloud-free data of Atmospheric Emitted Radiance Interferometer (AERI) radiation are extracted. The “reference values” of cloudy and cloud-free are determined based on the observation data of Vaisala CL31 ceilometer within the time range of 8 min before the corresponding time of AERI. A support vector machine (SVM) algorithm is used for training. The dataset comes from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site and North Slope Alaska (NSA) site from 2015 to 2017, and the ARM West Antarctic Radiation Experiment (AWARE) site in 2016 is also analyzed. The instruments used in this paper include AERI, ceilometer, etc. The experimental results reveal that the agreement of cloud detection results between the proposed algorithm and ceilometer is about 93% at each site. However, for high clouds or optically thin clouds, the agreement will decrease. |
author |
Liu, Lei Ye, Jin Li, Shulei Hu, Shuai Wang, Qi |
author_facet |
Liu, Lei Ye, Jin Li, Shulei Hu, Shuai Wang, Qi |
author_sort |
Liu, Lei |
title |
A Novel Machine Learning Algorithm for Cloud Detection Using AERI Measurement Data |
title_short |
A Novel Machine Learning Algorithm for Cloud Detection Using AERI Measurement Data |
title_full |
A Novel Machine Learning Algorithm for Cloud Detection Using AERI Measurement Data |
title_fullStr |
A Novel Machine Learning Algorithm for Cloud Detection Using AERI Measurement Data |
title_full_unstemmed |
A Novel Machine Learning Algorithm for Cloud Detection Using AERI Measurement Data |
title_sort |
novel machine learning algorithm for cloud detection using aeri measurement data |
publishDate |
2022 |
url |
http://www.osti.gov/servlets/purl/1872922 https://www.osti.gov/biblio/1872922 https://doi.org/10.3390/rs14112589 |
geographic |
Antarctic |
geographic_facet |
Antarctic |
genre |
Antarc* Antarctic north slope Alaska |
genre_facet |
Antarc* Antarctic north slope Alaska |
op_relation |
http://www.osti.gov/servlets/purl/1872922 https://www.osti.gov/biblio/1872922 https://doi.org/10.3390/rs14112589 doi:10.3390/rs14112589 |
op_doi |
https://doi.org/10.3390/rs14112589 |
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Remote Sensing |
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14 |
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11 |
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2589 |
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