Identifying Aerosol Subtypes from CALIPSO Lidar Profiles Using Deep Machine Learning

The Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP), on-board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) platform, is an elastic backscatter lidar that has been providing vertical profiles of the spatial, optical, and microphysical properties of clouds and aerosols since June 2006. Distinguishing between feature types (i.e., clouds vs. aerosol) and subtypes (e.g., ice clouds vs. water clouds and dust aerosols from smoke) in the CALIOP measurements is currently accomplished using layer-integrated measurements acquired by co-polarized (parallel) and cross-polarized (perpendicular) 532 nm channels and a single 1064 nm channel. Newly developed deep machine learning (DML) semantic segmentation methods now have the ability to combine observations from multiple channels with texture information to recognize patterns in data. Instead of focusing on a limited set of layer integrated values, our new DML feature classification technique uses the full scope of range-resolved information available in the CALIOP attenuated backscatter profiles. In this paper, one of the convolutional neural networks (CNN), SegNet, a fast and efficient DML model, is used to distinguish aerosol subtypes directly from the CALIOP profiles. The DML method is a 2D range bin-to-range bin aerosol subtype classification algorithm. We compare our new DML results to the classifications generated by CALIOP’s 1D layer-to-layer operational retrieval algorithm. These two methods, which take distinctly different approaches to aerosol classification, agree in over 60% of the comparisons. Higher levels of agreement are found in homogeneous scenes containing only a single aerosol type (i.e., marine, stratospheric aerosols). Disagreement between the two techniques increases in regions containing mixture of different aerosol types. The multi-dimensional texture information leveraged by the DML method shows advantages in differentiating between aerosol types based on their classification scores, as well as in distinguishing vertical distributions of aerosol types within individual layers. However, untangling mixtures of aerosol subtypes is still challenging for both the DML and operational algorithms. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Shan Zeng [verfasserIn] Ali Omar [verfasserIn] Mark Vaughan [verfasserIn] Macarena Ortiz [verfasserIn] Charles Trepte [verfasserIn] Jason Tackett [verfasserIn] Jeremy Yagle [verfasserIn] Patricia Lucker [verfasserIn] Yongxiang Hu [verfasserIn] David Winker [verfasserIn] Sharon Rodier [verfasserIn] Brian Getzewich [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	CALIPSO CALIOP aerosol subtype convolutional neural networks machine learning

Übergeordnetes Werk:	In: Atmosphere - MDPI AG, 2011, 12(2020), 1, p 10
Übergeordnetes Werk:	volume:12 ; year:2020 ; number:1, p 10

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/atmos12010010

Katalog-ID:	DOAJ034173099

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allfields	10.3390/atmos12010010 doi (DE-627)DOAJ034173099 (DE-599)DOAJ89fb78b017494d2a9156949f357289c6 DE-627 ger DE-627 rakwb eng QC851-999 Shan Zeng verfasserin aut Identifying Aerosol Subtypes from CALIPSO Lidar Profiles Using Deep Machine Learning 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP), on-board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) platform, is an elastic backscatter lidar that has been providing vertical profiles of the spatial, optical, and microphysical properties of clouds and aerosols since June 2006. Distinguishing between feature types (i.e., clouds vs. aerosol) and subtypes (e.g., ice clouds vs. water clouds and dust aerosols from smoke) in the CALIOP measurements is currently accomplished using layer-integrated measurements acquired by co-polarized (parallel) and cross-polarized (perpendicular) 532 nm channels and a single 1064 nm channel. Newly developed deep machine learning (DML) semantic segmentation methods now have the ability to combine observations from multiple channels with texture information to recognize patterns in data. Instead of focusing on a limited set of layer integrated values, our new DML feature classification technique uses the full scope of range-resolved information available in the CALIOP attenuated backscatter profiles. In this paper, one of the convolutional neural networks (CNN), SegNet, a fast and efficient DML model, is used to distinguish aerosol subtypes directly from the CALIOP profiles. The DML method is a 2D range bin-to-range bin aerosol subtype classification algorithm. We compare our new DML results to the classifications generated by CALIOP’s 1D layer-to-layer operational retrieval algorithm. These two methods, which take distinctly different approaches to aerosol classification, agree in over 60% of the comparisons. Higher levels of agreement are found in homogeneous scenes containing only a single aerosol type (i.e., marine, stratospheric aerosols). Disagreement between the two techniques increases in regions containing mixture of different aerosol types. The multi-dimensional texture information leveraged by the DML method shows advantages in differentiating between aerosol types based on their classification scores, as well as in distinguishing vertical distributions of aerosol types within individual layers. However, untangling mixtures of aerosol subtypes is still challenging for both the DML and operational algorithms. CALIPSO CALIOP aerosol subtype convolutional neural networks machine learning Meteorology. Climatology Ali Omar verfasserin aut Mark Vaughan verfasserin aut Macarena Ortiz verfasserin aut Charles Trepte verfasserin aut Jason Tackett verfasserin aut Jeremy Yagle verfasserin aut Patricia Lucker verfasserin aut Yongxiang Hu verfasserin aut David Winker verfasserin aut Sharon Rodier verfasserin aut Brian Getzewich verfasserin aut In Atmosphere MDPI AG, 2011 12(2020), 1, p 10 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:12 year:2020 number:1, p 10 https://doi.org/10.3390/atmos12010010 kostenfrei https://doaj.org/article/89fb78b017494d2a9156949f357289c6 kostenfrei https://www.mdpi.com/2073-4433/12/1/10 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2020 1, p 10
spelling	10.3390/atmos12010010 doi (DE-627)DOAJ034173099 (DE-599)DOAJ89fb78b017494d2a9156949f357289c6 DE-627 ger DE-627 rakwb eng QC851-999 Shan Zeng verfasserin aut Identifying Aerosol Subtypes from CALIPSO Lidar Profiles Using Deep Machine Learning 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP), on-board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) platform, is an elastic backscatter lidar that has been providing vertical profiles of the spatial, optical, and microphysical properties of clouds and aerosols since June 2006. Distinguishing between feature types (i.e., clouds vs. aerosol) and subtypes (e.g., ice clouds vs. water clouds and dust aerosols from smoke) in the CALIOP measurements is currently accomplished using layer-integrated measurements acquired by co-polarized (parallel) and cross-polarized (perpendicular) 532 nm channels and a single 1064 nm channel. Newly developed deep machine learning (DML) semantic segmentation methods now have the ability to combine observations from multiple channels with texture information to recognize patterns in data. Instead of focusing on a limited set of layer integrated values, our new DML feature classification technique uses the full scope of range-resolved information available in the CALIOP attenuated backscatter profiles. In this paper, one of the convolutional neural networks (CNN), SegNet, a fast and efficient DML model, is used to distinguish aerosol subtypes directly from the CALIOP profiles. The DML method is a 2D range bin-to-range bin aerosol subtype classification algorithm. We compare our new DML results to the classifications generated by CALIOP’s 1D layer-to-layer operational retrieval algorithm. These two methods, which take distinctly different approaches to aerosol classification, agree in over 60% of the comparisons. Higher levels of agreement are found in homogeneous scenes containing only a single aerosol type (i.e., marine, stratospheric aerosols). Disagreement between the two techniques increases in regions containing mixture of different aerosol types. The multi-dimensional texture information leveraged by the DML method shows advantages in differentiating between aerosol types based on their classification scores, as well as in distinguishing vertical distributions of aerosol types within individual layers. However, untangling mixtures of aerosol subtypes is still challenging for both the DML and operational algorithms. CALIPSO CALIOP aerosol subtype convolutional neural networks machine learning Meteorology. Climatology Ali Omar verfasserin aut Mark Vaughan verfasserin aut Macarena Ortiz verfasserin aut Charles Trepte verfasserin aut Jason Tackett verfasserin aut Jeremy Yagle verfasserin aut Patricia Lucker verfasserin aut Yongxiang Hu verfasserin aut David Winker verfasserin aut Sharon Rodier verfasserin aut Brian Getzewich verfasserin aut In Atmosphere MDPI AG, 2011 12(2020), 1, p 10 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:12 year:2020 number:1, p 10 https://doi.org/10.3390/atmos12010010 kostenfrei https://doaj.org/article/89fb78b017494d2a9156949f357289c6 kostenfrei https://www.mdpi.com/2073-4433/12/1/10 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2020 1, p 10
allfields_unstemmed	10.3390/atmos12010010 doi (DE-627)DOAJ034173099 (DE-599)DOAJ89fb78b017494d2a9156949f357289c6 DE-627 ger DE-627 rakwb eng QC851-999 Shan Zeng verfasserin aut Identifying Aerosol Subtypes from CALIPSO Lidar Profiles Using Deep Machine Learning 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP), on-board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) platform, is an elastic backscatter lidar that has been providing vertical profiles of the spatial, optical, and microphysical properties of clouds and aerosols since June 2006. Distinguishing between feature types (i.e., clouds vs. aerosol) and subtypes (e.g., ice clouds vs. water clouds and dust aerosols from smoke) in the CALIOP measurements is currently accomplished using layer-integrated measurements acquired by co-polarized (parallel) and cross-polarized (perpendicular) 532 nm channels and a single 1064 nm channel. Newly developed deep machine learning (DML) semantic segmentation methods now have the ability to combine observations from multiple channels with texture information to recognize patterns in data. Instead of focusing on a limited set of layer integrated values, our new DML feature classification technique uses the full scope of range-resolved information available in the CALIOP attenuated backscatter profiles. In this paper, one of the convolutional neural networks (CNN), SegNet, a fast and efficient DML model, is used to distinguish aerosol subtypes directly from the CALIOP profiles. The DML method is a 2D range bin-to-range bin aerosol subtype classification algorithm. We compare our new DML results to the classifications generated by CALIOP’s 1D layer-to-layer operational retrieval algorithm. These two methods, which take distinctly different approaches to aerosol classification, agree in over 60% of the comparisons. Higher levels of agreement are found in homogeneous scenes containing only a single aerosol type (i.e., marine, stratospheric aerosols). Disagreement between the two techniques increases in regions containing mixture of different aerosol types. The multi-dimensional texture information leveraged by the DML method shows advantages in differentiating between aerosol types based on their classification scores, as well as in distinguishing vertical distributions of aerosol types within individual layers. However, untangling mixtures of aerosol subtypes is still challenging for both the DML and operational algorithms. CALIPSO CALIOP aerosol subtype convolutional neural networks machine learning Meteorology. Climatology Ali Omar verfasserin aut Mark Vaughan verfasserin aut Macarena Ortiz verfasserin aut Charles Trepte verfasserin aut Jason Tackett verfasserin aut Jeremy Yagle verfasserin aut Patricia Lucker verfasserin aut Yongxiang Hu verfasserin aut David Winker verfasserin aut Sharon Rodier verfasserin aut Brian Getzewich verfasserin aut In Atmosphere MDPI AG, 2011 12(2020), 1, p 10 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:12 year:2020 number:1, p 10 https://doi.org/10.3390/atmos12010010 kostenfrei https://doaj.org/article/89fb78b017494d2a9156949f357289c6 kostenfrei https://www.mdpi.com/2073-4433/12/1/10 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2020 1, p 10
allfieldsGer	10.3390/atmos12010010 doi (DE-627)DOAJ034173099 (DE-599)DOAJ89fb78b017494d2a9156949f357289c6 DE-627 ger DE-627 rakwb eng QC851-999 Shan Zeng verfasserin aut Identifying Aerosol Subtypes from CALIPSO Lidar Profiles Using Deep Machine Learning 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP), on-board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) platform, is an elastic backscatter lidar that has been providing vertical profiles of the spatial, optical, and microphysical properties of clouds and aerosols since June 2006. Distinguishing between feature types (i.e., clouds vs. aerosol) and subtypes (e.g., ice clouds vs. water clouds and dust aerosols from smoke) in the CALIOP measurements is currently accomplished using layer-integrated measurements acquired by co-polarized (parallel) and cross-polarized (perpendicular) 532 nm channels and a single 1064 nm channel. Newly developed deep machine learning (DML) semantic segmentation methods now have the ability to combine observations from multiple channels with texture information to recognize patterns in data. Instead of focusing on a limited set of layer integrated values, our new DML feature classification technique uses the full scope of range-resolved information available in the CALIOP attenuated backscatter profiles. In this paper, one of the convolutional neural networks (CNN), SegNet, a fast and efficient DML model, is used to distinguish aerosol subtypes directly from the CALIOP profiles. The DML method is a 2D range bin-to-range bin aerosol subtype classification algorithm. We compare our new DML results to the classifications generated by CALIOP’s 1D layer-to-layer operational retrieval algorithm. These two methods, which take distinctly different approaches to aerosol classification, agree in over 60% of the comparisons. Higher levels of agreement are found in homogeneous scenes containing only a single aerosol type (i.e., marine, stratospheric aerosols). Disagreement between the two techniques increases in regions containing mixture of different aerosol types. The multi-dimensional texture information leveraged by the DML method shows advantages in differentiating between aerosol types based on their classification scores, as well as in distinguishing vertical distributions of aerosol types within individual layers. However, untangling mixtures of aerosol subtypes is still challenging for both the DML and operational algorithms. CALIPSO CALIOP aerosol subtype convolutional neural networks machine learning Meteorology. Climatology Ali Omar verfasserin aut Mark Vaughan verfasserin aut Macarena Ortiz verfasserin aut Charles Trepte verfasserin aut Jason Tackett verfasserin aut Jeremy Yagle verfasserin aut Patricia Lucker verfasserin aut Yongxiang Hu verfasserin aut David Winker verfasserin aut Sharon Rodier verfasserin aut Brian Getzewich verfasserin aut In Atmosphere MDPI AG, 2011 12(2020), 1, p 10 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:12 year:2020 number:1, p 10 https://doi.org/10.3390/atmos12010010 kostenfrei https://doaj.org/article/89fb78b017494d2a9156949f357289c6 kostenfrei https://www.mdpi.com/2073-4433/12/1/10 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2020 1, p 10
allfieldsSound	10.3390/atmos12010010 doi (DE-627)DOAJ034173099 (DE-599)DOAJ89fb78b017494d2a9156949f357289c6 DE-627 ger DE-627 rakwb eng QC851-999 Shan Zeng verfasserin aut Identifying Aerosol Subtypes from CALIPSO Lidar Profiles Using Deep Machine Learning 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP), on-board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) platform, is an elastic backscatter lidar that has been providing vertical profiles of the spatial, optical, and microphysical properties of clouds and aerosols since June 2006. Distinguishing between feature types (i.e., clouds vs. aerosol) and subtypes (e.g., ice clouds vs. water clouds and dust aerosols from smoke) in the CALIOP measurements is currently accomplished using layer-integrated measurements acquired by co-polarized (parallel) and cross-polarized (perpendicular) 532 nm channels and a single 1064 nm channel. Newly developed deep machine learning (DML) semantic segmentation methods now have the ability to combine observations from multiple channels with texture information to recognize patterns in data. Instead of focusing on a limited set of layer integrated values, our new DML feature classification technique uses the full scope of range-resolved information available in the CALIOP attenuated backscatter profiles. In this paper, one of the convolutional neural networks (CNN), SegNet, a fast and efficient DML model, is used to distinguish aerosol subtypes directly from the CALIOP profiles. The DML method is a 2D range bin-to-range bin aerosol subtype classification algorithm. We compare our new DML results to the classifications generated by CALIOP’s 1D layer-to-layer operational retrieval algorithm. These two methods, which take distinctly different approaches to aerosol classification, agree in over 60% of the comparisons. Higher levels of agreement are found in homogeneous scenes containing only a single aerosol type (i.e., marine, stratospheric aerosols). Disagreement between the two techniques increases in regions containing mixture of different aerosol types. The multi-dimensional texture information leveraged by the DML method shows advantages in differentiating between aerosol types based on their classification scores, as well as in distinguishing vertical distributions of aerosol types within individual layers. However, untangling mixtures of aerosol subtypes is still challenging for both the DML and operational algorithms. CALIPSO CALIOP aerosol subtype convolutional neural networks machine learning Meteorology. Climatology Ali Omar verfasserin aut Mark Vaughan verfasserin aut Macarena Ortiz verfasserin aut Charles Trepte verfasserin aut Jason Tackett verfasserin aut Jeremy Yagle verfasserin aut Patricia Lucker verfasserin aut Yongxiang Hu verfasserin aut David Winker verfasserin aut Sharon Rodier verfasserin aut Brian Getzewich verfasserin aut In Atmosphere MDPI AG, 2011 12(2020), 1, p 10 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:12 year:2020 number:1, p 10 https://doi.org/10.3390/atmos12010010 kostenfrei https://doaj.org/article/89fb78b017494d2a9156949f357289c6 kostenfrei https://www.mdpi.com/2073-4433/12/1/10 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2020 1, p 10
language	English
source	In Atmosphere 12(2020), 1, p 10 volume:12 year:2020 number:1, p 10
sourceStr	In Atmosphere 12(2020), 1, p 10 volume:12 year:2020 number:1, p 10
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institution	findex.gbv.de
topic_facet	CALIPSO CALIOP aerosol subtype convolutional neural networks machine learning Meteorology. Climatology
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container_title	Atmosphere
authorswithroles_txt_mv	Shan Zeng @@aut@@ Ali Omar @@aut@@ Mark Vaughan @@aut@@ Macarena Ortiz @@aut@@ Charles Trepte @@aut@@ Jason Tackett @@aut@@ Jeremy Yagle @@aut@@ Patricia Lucker @@aut@@ Yongxiang Hu @@aut@@ David Winker @@aut@@ Sharon Rodier @@aut@@ Brian Getzewich @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
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author	Shan Zeng
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topic_title	QC851-999 Identifying Aerosol Subtypes from CALIPSO Lidar Profiles Using Deep Machine Learning CALIPSO CALIOP aerosol subtype convolutional neural networks machine learning
topic	misc QC851-999 misc CALIPSO misc CALIOP misc aerosol subtype misc convolutional neural networks misc machine learning misc Meteorology. Climatology
topic_unstemmed	misc QC851-999 misc CALIPSO misc CALIOP misc aerosol subtype misc convolutional neural networks misc machine learning misc Meteorology. Climatology
topic_browse	misc QC851-999 misc CALIPSO misc CALIOP misc aerosol subtype misc convolutional neural networks misc machine learning misc Meteorology. Climatology
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title	Identifying Aerosol Subtypes from CALIPSO Lidar Profiles Using Deep Machine Learning
ctrlnum	(DE-627)DOAJ034173099 (DE-599)DOAJ89fb78b017494d2a9156949f357289c6
title_full	Identifying Aerosol Subtypes from CALIPSO Lidar Profiles Using Deep Machine Learning
author_sort	Shan Zeng
journal	Atmosphere
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author_browse	Shan Zeng Ali Omar Mark Vaughan Macarena Ortiz Charles Trepte Jason Tackett Jeremy Yagle Patricia Lucker Yongxiang Hu David Winker Sharon Rodier Brian Getzewich
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doi_str_mv	10.3390/atmos12010010
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title_sort	identifying aerosol subtypes from calipso lidar profiles using deep machine learning
callnumber	QC851-999
title_auth	Identifying Aerosol Subtypes from CALIPSO Lidar Profiles Using Deep Machine Learning
abstract	The Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP), on-board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) platform, is an elastic backscatter lidar that has been providing vertical profiles of the spatial, optical, and microphysical properties of clouds and aerosols since June 2006. Distinguishing between feature types (i.e., clouds vs. aerosol) and subtypes (e.g., ice clouds vs. water clouds and dust aerosols from smoke) in the CALIOP measurements is currently accomplished using layer-integrated measurements acquired by co-polarized (parallel) and cross-polarized (perpendicular) 532 nm channels and a single 1064 nm channel. Newly developed deep machine learning (DML) semantic segmentation methods now have the ability to combine observations from multiple channels with texture information to recognize patterns in data. Instead of focusing on a limited set of layer integrated values, our new DML feature classification technique uses the full scope of range-resolved information available in the CALIOP attenuated backscatter profiles. In this paper, one of the convolutional neural networks (CNN), SegNet, a fast and efficient DML model, is used to distinguish aerosol subtypes directly from the CALIOP profiles. The DML method is a 2D range bin-to-range bin aerosol subtype classification algorithm. We compare our new DML results to the classifications generated by CALIOP’s 1D layer-to-layer operational retrieval algorithm. These two methods, which take distinctly different approaches to aerosol classification, agree in over 60% of the comparisons. Higher levels of agreement are found in homogeneous scenes containing only a single aerosol type (i.e., marine, stratospheric aerosols). Disagreement between the two techniques increases in regions containing mixture of different aerosol types. The multi-dimensional texture information leveraged by the DML method shows advantages in differentiating between aerosol types based on their classification scores, as well as in distinguishing vertical distributions of aerosol types within individual layers. However, untangling mixtures of aerosol subtypes is still challenging for both the DML and operational algorithms.
abstractGer	The Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP), on-board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) platform, is an elastic backscatter lidar that has been providing vertical profiles of the spatial, optical, and microphysical properties of clouds and aerosols since June 2006. Distinguishing between feature types (i.e., clouds vs. aerosol) and subtypes (e.g., ice clouds vs. water clouds and dust aerosols from smoke) in the CALIOP measurements is currently accomplished using layer-integrated measurements acquired by co-polarized (parallel) and cross-polarized (perpendicular) 532 nm channels and a single 1064 nm channel. Newly developed deep machine learning (DML) semantic segmentation methods now have the ability to combine observations from multiple channels with texture information to recognize patterns in data. Instead of focusing on a limited set of layer integrated values, our new DML feature classification technique uses the full scope of range-resolved information available in the CALIOP attenuated backscatter profiles. In this paper, one of the convolutional neural networks (CNN), SegNet, a fast and efficient DML model, is used to distinguish aerosol subtypes directly from the CALIOP profiles. The DML method is a 2D range bin-to-range bin aerosol subtype classification algorithm. We compare our new DML results to the classifications generated by CALIOP’s 1D layer-to-layer operational retrieval algorithm. These two methods, which take distinctly different approaches to aerosol classification, agree in over 60% of the comparisons. Higher levels of agreement are found in homogeneous scenes containing only a single aerosol type (i.e., marine, stratospheric aerosols). Disagreement between the two techniques increases in regions containing mixture of different aerosol types. The multi-dimensional texture information leveraged by the DML method shows advantages in differentiating between aerosol types based on their classification scores, as well as in distinguishing vertical distributions of aerosol types within individual layers. However, untangling mixtures of aerosol subtypes is still challenging for both the DML and operational algorithms.
abstract_unstemmed	The Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP), on-board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) platform, is an elastic backscatter lidar that has been providing vertical profiles of the spatial, optical, and microphysical properties of clouds and aerosols since June 2006. Distinguishing between feature types (i.e., clouds vs. aerosol) and subtypes (e.g., ice clouds vs. water clouds and dust aerosols from smoke) in the CALIOP measurements is currently accomplished using layer-integrated measurements acquired by co-polarized (parallel) and cross-polarized (perpendicular) 532 nm channels and a single 1064 nm channel. Newly developed deep machine learning (DML) semantic segmentation methods now have the ability to combine observations from multiple channels with texture information to recognize patterns in data. Instead of focusing on a limited set of layer integrated values, our new DML feature classification technique uses the full scope of range-resolved information available in the CALIOP attenuated backscatter profiles. In this paper, one of the convolutional neural networks (CNN), SegNet, a fast and efficient DML model, is used to distinguish aerosol subtypes directly from the CALIOP profiles. The DML method is a 2D range bin-to-range bin aerosol subtype classification algorithm. We compare our new DML results to the classifications generated by CALIOP’s 1D layer-to-layer operational retrieval algorithm. These two methods, which take distinctly different approaches to aerosol classification, agree in over 60% of the comparisons. Higher levels of agreement are found in homogeneous scenes containing only a single aerosol type (i.e., marine, stratospheric aerosols). Disagreement between the two techniques increases in regions containing mixture of different aerosol types. The multi-dimensional texture information leveraged by the DML method shows advantages in differentiating between aerosol types based on their classification scores, as well as in distinguishing vertical distributions of aerosol types within individual layers. However, untangling mixtures of aerosol subtypes is still challenging for both the DML and operational algorithms.
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container_issue	1, p 10
title_short	Identifying Aerosol Subtypes from CALIPSO Lidar Profiles Using Deep Machine Learning
url	https://doi.org/10.3390/atmos12010010 https://doaj.org/article/89fb78b017494d2a9156949f357289c6 https://www.mdpi.com/2073-4433/12/1/10 https://doaj.org/toc/2073-4433
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author2	Ali Omar Mark Vaughan Macarena Ortiz Charles Trepte Jason Tackett Jeremy Yagle Patricia Lucker Yongxiang Hu David Winker Sharon Rodier Brian Getzewich
author2Str	Ali Omar Mark Vaughan Macarena Ortiz Charles Trepte Jason Tackett Jeremy Yagle Patricia Lucker Yongxiang Hu David Winker Sharon Rodier Brian Getzewich
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