Study of Antarctic Blowing Snow Storms Using MODIS and CALIOP Observations With a Machine Learning Model
Abstract As a common phenomenon over Antarctica, blowing snow (BLSN), especially the large BLSN storms, play an important role in the Antarctic surface mass balance, radiation budget, and planetary boundary layer processes. This study presents the work on BLSN storm identification and analysis with...
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American Geophysical Union (AGU)
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ftdoajarticles:oai:doaj.org/article:2b07453de2464eaebb7c4fecef9e1a41 2023-05-15T13:34:19+02:00 Study of Antarctic Blowing Snow Storms Using MODIS and CALIOP Observations With a Machine Learning Model Yuekui Yang Adam Anderson Daniel Kiv Justin Germann Maya Fuchs Stephen Palm Tao Wang 2021-01-01T00:00:00Z https://doi.org/10.1029/2020EA001310 https://doaj.org/article/2b07453de2464eaebb7c4fecef9e1a41 EN eng American Geophysical Union (AGU) https://doi.org/10.1029/2020EA001310 https://doaj.org/toc/2333-5084 2333-5084 doi:10.1029/2020EA001310 https://doaj.org/article/2b07453de2464eaebb7c4fecef9e1a41 Earth and Space Science, Vol 8, Iss 1, Pp n/a-n/a (2021) Antarctica Blowing snow CALIPSO Machine Learning MODIS Astronomy QB1-991 Geology QE1-996.5 article 2021 ftdoajarticles https://doi.org/10.1029/2020EA001310 2022-12-31T12:49:59Z Abstract As a common phenomenon over Antarctica, blowing snow (BLSN), especially the large BLSN storms, play an important role in the Antarctic surface mass balance, radiation budget, and planetary boundary layer processes. This study presents the work on BLSN storm identification and analysis with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Spectral analysis shows that BLSN identification is feasible with MODIS daytime data. A random forest machine learning model is developed and observations from the Cloud‐Aerosol Lidar with Orthogonal Polarization are used for training. Model performance results show that machine‐learning based classification can achieve over 90% overall accuracy when classifying MODIS pixels into cloud, clear, and BLSN categories. The machine learning model is applied to MODIS observations during the month of October 2009 for BLSN storm analysis. Results show that the size of BLSN storms has a large spectrum and can reach hundreds of thousands km2. The MODIS based BLSN storm frequency map extends the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations coverage limit from 82°S to the South Pole. A BLSN storm belt, which extends from the South Pole region to the coastal area between 130°E and 160°E along the Transantarctic Mountains, provides a potential pathway of snow transport. These results are important in improving the understanding of BLSN impact on Antarctic surface mass balance and boundary layer processes. Article in Journal/Newspaper Antarc* Antarctic Antarctica South pole South pole Directory of Open Access Journals: DOAJ Articles Antarctic The Antarctic Transantarctic Mountains South Pole Earth and Space Science 8 1 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Antarctica Blowing snow CALIPSO Machine Learning MODIS Astronomy QB1-991 Geology QE1-996.5 |
spellingShingle |
Antarctica Blowing snow CALIPSO Machine Learning MODIS Astronomy QB1-991 Geology QE1-996.5 Yuekui Yang Adam Anderson Daniel Kiv Justin Germann Maya Fuchs Stephen Palm Tao Wang Study of Antarctic Blowing Snow Storms Using MODIS and CALIOP Observations With a Machine Learning Model |
topic_facet |
Antarctica Blowing snow CALIPSO Machine Learning MODIS Astronomy QB1-991 Geology QE1-996.5 |
description |
Abstract As a common phenomenon over Antarctica, blowing snow (BLSN), especially the large BLSN storms, play an important role in the Antarctic surface mass balance, radiation budget, and planetary boundary layer processes. This study presents the work on BLSN storm identification and analysis with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Spectral analysis shows that BLSN identification is feasible with MODIS daytime data. A random forest machine learning model is developed and observations from the Cloud‐Aerosol Lidar with Orthogonal Polarization are used for training. Model performance results show that machine‐learning based classification can achieve over 90% overall accuracy when classifying MODIS pixels into cloud, clear, and BLSN categories. The machine learning model is applied to MODIS observations during the month of October 2009 for BLSN storm analysis. Results show that the size of BLSN storms has a large spectrum and can reach hundreds of thousands km2. The MODIS based BLSN storm frequency map extends the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations coverage limit from 82°S to the South Pole. A BLSN storm belt, which extends from the South Pole region to the coastal area between 130°E and 160°E along the Transantarctic Mountains, provides a potential pathway of snow transport. These results are important in improving the understanding of BLSN impact on Antarctic surface mass balance and boundary layer processes. |
format |
Article in Journal/Newspaper |
author |
Yuekui Yang Adam Anderson Daniel Kiv Justin Germann Maya Fuchs Stephen Palm Tao Wang |
author_facet |
Yuekui Yang Adam Anderson Daniel Kiv Justin Germann Maya Fuchs Stephen Palm Tao Wang |
author_sort |
Yuekui Yang |
title |
Study of Antarctic Blowing Snow Storms Using MODIS and CALIOP Observations With a Machine Learning Model |
title_short |
Study of Antarctic Blowing Snow Storms Using MODIS and CALIOP Observations With a Machine Learning Model |
title_full |
Study of Antarctic Blowing Snow Storms Using MODIS and CALIOP Observations With a Machine Learning Model |
title_fullStr |
Study of Antarctic Blowing Snow Storms Using MODIS and CALIOP Observations With a Machine Learning Model |
title_full_unstemmed |
Study of Antarctic Blowing Snow Storms Using MODIS and CALIOP Observations With a Machine Learning Model |
title_sort |
study of antarctic blowing snow storms using modis and caliop observations with a machine learning model |
publisher |
American Geophysical Union (AGU) |
publishDate |
2021 |
url |
https://doi.org/10.1029/2020EA001310 https://doaj.org/article/2b07453de2464eaebb7c4fecef9e1a41 |
geographic |
Antarctic The Antarctic Transantarctic Mountains South Pole |
geographic_facet |
Antarctic The Antarctic Transantarctic Mountains South Pole |
genre |
Antarc* Antarctic Antarctica South pole South pole |
genre_facet |
Antarc* Antarctic Antarctica South pole South pole |
op_source |
Earth and Space Science, Vol 8, Iss 1, Pp n/a-n/a (2021) |
op_relation |
https://doi.org/10.1029/2020EA001310 https://doaj.org/toc/2333-5084 2333-5084 doi:10.1029/2020EA001310 https://doaj.org/article/2b07453de2464eaebb7c4fecef9e1a41 |
op_doi |
https://doi.org/10.1029/2020EA001310 |
container_title |
Earth and Space Science |
container_volume |
8 |
container_issue |
1 |
_version_ |
1766051589937692672 |