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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/9/2126 |
| _version_ | 1827671673043681280 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T03:45:10Z |
| format | Article |
| id | doaj.art-a747534ca9aa43a4805b78c520e74bcb |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:45:10Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a747534ca9aa43a4805b78c520e74bcb2023-11-23T09:10:58ZengMDPI AGRemote Sensing2072-42922022-04-01149212610.3390/rs14092126Using Machine Learning Algorithm to Detect Blowing Snow and Fog in Antarctica Based on Ceilometer and Surface Meteorology SystemsJin Ye0Lei Liu1Yi Wu2Wanying Yang3Hong Ren4College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaNanjing Meteorological Bureau, China Meteorological Administration (CMA), Nanjing 210019, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaBlowing 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.https://www.mdpi.com/2072-4292/14/9/2126blowing snowfogceilometersurface meteorology systems |
| spellingShingle | 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 Remote Sensing blowing snow fog ceilometer surface meteorology systems |
| title | 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_short | 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 |
| topic | blowing snow fog ceilometer surface meteorology systems |
| url | https://www.mdpi.com/2072-4292/14/9/2126 |
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