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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ftmdpi:oai:mdpi.com:/2072-4292/14/9/2126/ 2023-08-20T04:02:01+02:00 Using Machine Learning Algorithm to Detect Blowing Snow and Fog in Antarctica Based on Ceilometer and Surface Meteorology Systems Jin Ye Lei Liu Yi Wu Wanying Yang Hong Ren 2022-04-28 application/pdf https://doi.org/10.3390/rs14092126 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing Image Processing https://dx.doi.org/10.3390/rs14092126 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 14; Issue 9; Pages: 2126 blowing snow fog ceilometer surface meteorology systems Text 2022 ftmdpi https://doi.org/10.3390/rs14092126 2023-08-01T04:54:20Z 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. Text Antarc* Antarctica Ice Sheet MDPI Open Access Publishing Remote Sensing 14 9 2126 |
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Open Polar |
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MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
blowing snow fog ceilometer surface meteorology systems |
spellingShingle |
blowing snow fog ceilometer surface meteorology systems Jin Ye Lei Liu Yi Wu Wanying Yang Hong Ren Using Machine Learning Algorithm to Detect Blowing Snow and Fog in Antarctica Based on Ceilometer and Surface Meteorology Systems |
topic_facet |
blowing snow fog ceilometer surface meteorology systems |
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. |
format |
Text |
author |
Jin Ye Lei Liu Yi Wu Wanying Yang Hong Ren |
author_facet |
Jin Ye Lei Liu Yi Wu Wanying Yang Hong Ren |
author_sort |
Jin Ye |
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 |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14092126 |
genre |
Antarc* Antarctica Ice Sheet |
genre_facet |
Antarc* Antarctica Ice Sheet |
op_source |
Remote Sensing; Volume 14; Issue 9; Pages: 2126 |
op_relation |
Remote Sensing Image Processing https://dx.doi.org/10.3390/rs14092126 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs14092126 |
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Remote Sensing |
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14 |
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9 |
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2126 |
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1774712403222593536 |