Generative Nowcasting of Marine Fog Visibility in the Grand Banks area and Sable Island in Canada ...
This study presents the application of generative deep learning techniques to evaluate marine fog visibility nowcasting using the FATIMA (Fog and turbulence interactions in the marine atmosphere) campaign observations collected during July 2022 in the North Atlantic in the Grand Banks area and vicin...
Main Authors: | , , , , , , |
---|---|
Format: | Text |
Language: | unknown |
Published: |
arXiv
2024
|
Subjects: | |
Online Access: | https://dx.doi.org/10.48550/arxiv.2402.06800 https://arxiv.org/abs/2402.06800 |
id |
ftdatacite:10.48550/arxiv.2402.06800 |
---|---|
record_format |
openpolar |
spelling |
ftdatacite:10.48550/arxiv.2402.06800 2024-03-31T07:54:17+00:00 Generative Nowcasting of Marine Fog Visibility in the Grand Banks area and Sable Island in Canada ... Gultepe, Eren Wang, Sen Blomquist, Byron Fernando, Harindra J. S. Kreidl, O. Patrick Delene, David J. Gultepe, Ismail 2024 https://dx.doi.org/10.48550/arxiv.2402.06800 https://arxiv.org/abs/2402.06800 unknown arXiv Creative Commons Attribution Non Commercial Share Alike 4.0 International https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode cc-by-nc-sa-4.0 Machine Learning cs.LG Atmospheric and Oceanic Physics physics.ao-ph FOS Computer and information sciences FOS Physical sciences Text article-journal Article ScholarlyArticle 2024 ftdatacite https://doi.org/10.48550/arxiv.2402.06800 2024-03-04T13:07:01Z This study presents the application of generative deep learning techniques to evaluate marine fog visibility nowcasting using the FATIMA (Fog and turbulence interactions in the marine atmosphere) campaign observations collected during July 2022 in the North Atlantic in the Grand Banks area and vicinity of Sable Island (SI), northeast of Canada. The measurements were collected using the Vaisala Forward Scatter Sensor model FD70 and Weather Transmitter model WXT50, and Gill R3A ultrasonic anemometer mounted on the Research Vessel Atlantic Condor. To perform nowcasting, the time series of fog visibility (Vis), wind speed, dew point depression, and relative humidity with respect to water were preprocessed to have lagged time step features. Generative nowcasting of Vis time series for lead times of 30 and 60 minutes were performed using conditional generative adversarial networks (cGAN) regression at visibility thresholds of Vis < 1 km and < 10 km. Extreme gradient boosting (XGBoost) was used as a baseline ... Text North Atlantic DataCite Metadata Store (German National Library of Science and Technology) Canada |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
Machine Learning cs.LG Atmospheric and Oceanic Physics physics.ao-ph FOS Computer and information sciences FOS Physical sciences |
spellingShingle |
Machine Learning cs.LG Atmospheric and Oceanic Physics physics.ao-ph FOS Computer and information sciences FOS Physical sciences Gultepe, Eren Wang, Sen Blomquist, Byron Fernando, Harindra J. S. Kreidl, O. Patrick Delene, David J. Gultepe, Ismail Generative Nowcasting of Marine Fog Visibility in the Grand Banks area and Sable Island in Canada ... |
topic_facet |
Machine Learning cs.LG Atmospheric and Oceanic Physics physics.ao-ph FOS Computer and information sciences FOS Physical sciences |
description |
This study presents the application of generative deep learning techniques to evaluate marine fog visibility nowcasting using the FATIMA (Fog and turbulence interactions in the marine atmosphere) campaign observations collected during July 2022 in the North Atlantic in the Grand Banks area and vicinity of Sable Island (SI), northeast of Canada. The measurements were collected using the Vaisala Forward Scatter Sensor model FD70 and Weather Transmitter model WXT50, and Gill R3A ultrasonic anemometer mounted on the Research Vessel Atlantic Condor. To perform nowcasting, the time series of fog visibility (Vis), wind speed, dew point depression, and relative humidity with respect to water were preprocessed to have lagged time step features. Generative nowcasting of Vis time series for lead times of 30 and 60 minutes were performed using conditional generative adversarial networks (cGAN) regression at visibility thresholds of Vis < 1 km and < 10 km. Extreme gradient boosting (XGBoost) was used as a baseline ... |
format |
Text |
author |
Gultepe, Eren Wang, Sen Blomquist, Byron Fernando, Harindra J. S. Kreidl, O. Patrick Delene, David J. Gultepe, Ismail |
author_facet |
Gultepe, Eren Wang, Sen Blomquist, Byron Fernando, Harindra J. S. Kreidl, O. Patrick Delene, David J. Gultepe, Ismail |
author_sort |
Gultepe, Eren |
title |
Generative Nowcasting of Marine Fog Visibility in the Grand Banks area and Sable Island in Canada ... |
title_short |
Generative Nowcasting of Marine Fog Visibility in the Grand Banks area and Sable Island in Canada ... |
title_full |
Generative Nowcasting of Marine Fog Visibility in the Grand Banks area and Sable Island in Canada ... |
title_fullStr |
Generative Nowcasting of Marine Fog Visibility in the Grand Banks area and Sable Island in Canada ... |
title_full_unstemmed |
Generative Nowcasting of Marine Fog Visibility in the Grand Banks area and Sable Island in Canada ... |
title_sort |
generative nowcasting of marine fog visibility in the grand banks area and sable island in canada ... |
publisher |
arXiv |
publishDate |
2024 |
url |
https://dx.doi.org/10.48550/arxiv.2402.06800 https://arxiv.org/abs/2402.06800 |
geographic |
Canada |
geographic_facet |
Canada |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_rights |
Creative Commons Attribution Non Commercial Share Alike 4.0 International https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode cc-by-nc-sa-4.0 |
op_doi |
https://doi.org/10.48550/arxiv.2402.06800 |
_version_ |
1795035075652354048 |