Using Machine Learning Algorithm to Detect Blowing Snow and Fog in Antarctica Based on Ceilometer and Surface Meteorology Systems
Blowing snow is a common weather phenomenon in Antarctica and plays an important role in the water vapor cycle and ice sheet mass balance. Although it has a significant impact on the climate of Antarctica, people do not know much about this process. Fog events are difficult to distinguish from blowi...
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ftosti:oai:osti.gov:1870382 2023-07-30T03:59:25+02:00 Using Machine Learning Algorithm to Detect Blowing Snow and Fog in Antarctica Based on Ceilometer and Surface Meteorology Systems Ye, Jin Liu, Lei Wu, Yi Yang, Wanying Ren, Hong 2022-09-14 application/pdf http://www.osti.gov/servlets/purl/1870382 https://www.osti.gov/biblio/1870382 https://doi.org/10.3390/rs14092126 unknown http://www.osti.gov/servlets/purl/1870382 https://www.osti.gov/biblio/1870382 https://doi.org/10.3390/rs14092126 doi:10.3390/rs14092126 54 ENVIRONMENTAL SCIENCES 2022 ftosti https://doi.org/10.3390/rs14092126 2023-07-11T10:12:43Z Blowing snow is a common weather phenomenon in Antarctica and plays an important role in the water vapor cycle and ice sheet mass balance. Although it has a significant impact on the climate of Antarctica, people do not know much about this process. Fog events are difficult to distinguish from blowing snow events using existing detection algorithms by a ceilometer. In this study, based on ceilometer, the meteorological parameters observed by surface meteorology systems are further combined to detect blowing snow and fog using the AdaBoost algorithm. The weather phenomena recorded by human observers are ‘true’. The dataset is collected from 1 January 2016 to 31 December 2016 at the AWARE site. Among them, three-quarters of the data are used as the training set and the rest of the data as the testing set. The classification accuracy of the proposed algorithm for the testing set is about 94%. Compared with the Loeb method, the proposed algorithm can detect 89.12% of blowing snow events and 76.10% of fog events, while the Loeb method can only identify 64.29% of blowing snow events and 31.87% of fog events. Other/Unknown Material Antarc* Antarctica Ice Sheet SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Remote Sensing 14 9 2126 |
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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 |
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54 ENVIRONMENTAL SCIENCES Ye, Jin Liu, Lei Wu, Yi Yang, Wanying Ren, Hong Using Machine Learning Algorithm to Detect Blowing Snow and Fog in Antarctica Based on Ceilometer and Surface Meteorology Systems |
topic_facet |
54 ENVIRONMENTAL SCIENCES |
description |
Blowing snow is a common weather phenomenon in Antarctica and plays an important role in the water vapor cycle and ice sheet mass balance. Although it has a significant impact on the climate of Antarctica, people do not know much about this process. Fog events are difficult to distinguish from blowing snow events using existing detection algorithms by a ceilometer. In this study, based on ceilometer, the meteorological parameters observed by surface meteorology systems are further combined to detect blowing snow and fog using the AdaBoost algorithm. The weather phenomena recorded by human observers are ‘true’. The dataset is collected from 1 January 2016 to 31 December 2016 at the AWARE site. Among them, three-quarters of the data are used as the training set and the rest of the data as the testing set. The classification accuracy of the proposed algorithm for the testing set is about 94%. Compared with the Loeb method, the proposed algorithm can detect 89.12% of blowing snow events and 76.10% of fog events, while the Loeb method can only identify 64.29% of blowing snow events and 31.87% of fog events. |
author |
Ye, Jin Liu, Lei Wu, Yi Yang, Wanying Ren, Hong |
author_facet |
Ye, Jin Liu, Lei Wu, Yi Yang, Wanying Ren, Hong |
author_sort |
Ye, Jin |
title |
Using Machine Learning Algorithm to Detect Blowing Snow and Fog in Antarctica Based on Ceilometer and Surface Meteorology Systems |
title_short |
Using Machine Learning Algorithm to Detect Blowing Snow and Fog in Antarctica Based on Ceilometer and Surface Meteorology Systems |
title_full |
Using Machine Learning Algorithm to Detect Blowing Snow and Fog in Antarctica Based on Ceilometer and Surface Meteorology Systems |
title_fullStr |
Using Machine Learning Algorithm to Detect Blowing Snow and Fog in Antarctica Based on Ceilometer and Surface Meteorology Systems |
title_full_unstemmed |
Using Machine Learning Algorithm to Detect Blowing Snow and Fog in Antarctica Based on Ceilometer and Surface Meteorology Systems |
title_sort |
using machine learning algorithm to detect blowing snow and fog in antarctica based on ceilometer and surface meteorology systems |
publishDate |
2022 |
url |
http://www.osti.gov/servlets/purl/1870382 https://www.osti.gov/biblio/1870382 https://doi.org/10.3390/rs14092126 |
genre |
Antarc* Antarctica Ice Sheet |
genre_facet |
Antarc* Antarctica Ice Sheet |
op_relation |
http://www.osti.gov/servlets/purl/1870382 https://www.osti.gov/biblio/1870382 https://doi.org/10.3390/rs14092126 doi:10.3390/rs14092126 |
op_doi |
https://doi.org/10.3390/rs14092126 |
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Remote Sensing |
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2126 |
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