Generation and Analysis of Gridded Visibility Data in the Arctic
With the accelerated warming of the arctic and the gradual opening of the Arctic passages, more and more attention has been paid to assessing the risk of the navigation environment in the Arctic. Due to the scarcity of visibility data in the Arctic, this study proposes a model for referring visibili...
Published in: | Atmosphere |
---|---|
Main Authors: | , , , , , |
Format: | Text |
Language: | English |
Published: |
Multidisciplinary Digital Publishing Institute
2019
|
Subjects: | |
Online Access: | https://doi.org/10.3390/atmos10060314 |
id |
ftmdpi:oai:mdpi.com:/2073-4433/10/6/314/ |
---|---|
record_format |
openpolar |
spelling |
ftmdpi:oai:mdpi.com:/2073-4433/10/6/314/ 2023-08-20T04:03:24+02:00 Generation and Analysis of Gridded Visibility Data in the Arctic Yulong Shan Ren Zhang Ming Li Yangjun Wang Qiuhan Li Lifeng Li agris 2019-06-06 application/pdf https://doi.org/10.3390/atmos10060314 EN eng Multidisciplinary Digital Publishing Institute Meteorology https://dx.doi.org/10.3390/atmos10060314 https://creativecommons.org/licenses/by/4.0/ Atmosphere; Volume 10; Issue 6; Pages: 314 the Arctic passage back propagation neural network visibility spatial and temporal features Text 2019 ftmdpi https://doi.org/10.3390/atmos10060314 2023-07-31T22:20:20Z With the accelerated warming of the arctic and the gradual opening of the Arctic passages, more and more attention has been paid to assessing the risk of the navigation environment in the Arctic. Due to the scarcity of visibility data in the Arctic, this study proposes a model for referring visibility based on a back propagation (BP) neural network. The reliability of the model is validated and the gridded atmospheric visibility data in the Arctic from 2009 to 2018 was obtained. At the same time, this study analyzed the spatial and temporal features of visibility in the Arctic. The results show that the mean relative error is less than 20% under the different sample forms and it is more accurate to infer the visibility in a specific month using the multiple-year data of that month as training samples. Furthermore, the amount of sample data has a positive effect on the accuracy of inferred visibility, but the effect decreases with data quantity increasing. Visibility changes quickly in the south of 80° N in August, but slowly in the north in that time. At the same time, visibility in July and August is lower than that in other months but higher in March and May. Text Arctic MDPI Open Access Publishing Arctic Atmosphere 10 6 314 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
the Arctic passage back propagation neural network visibility spatial and temporal features |
spellingShingle |
the Arctic passage back propagation neural network visibility spatial and temporal features Yulong Shan Ren Zhang Ming Li Yangjun Wang Qiuhan Li Lifeng Li Generation and Analysis of Gridded Visibility Data in the Arctic |
topic_facet |
the Arctic passage back propagation neural network visibility spatial and temporal features |
description |
With the accelerated warming of the arctic and the gradual opening of the Arctic passages, more and more attention has been paid to assessing the risk of the navigation environment in the Arctic. Due to the scarcity of visibility data in the Arctic, this study proposes a model for referring visibility based on a back propagation (BP) neural network. The reliability of the model is validated and the gridded atmospheric visibility data in the Arctic from 2009 to 2018 was obtained. At the same time, this study analyzed the spatial and temporal features of visibility in the Arctic. The results show that the mean relative error is less than 20% under the different sample forms and it is more accurate to infer the visibility in a specific month using the multiple-year data of that month as training samples. Furthermore, the amount of sample data has a positive effect on the accuracy of inferred visibility, but the effect decreases with data quantity increasing. Visibility changes quickly in the south of 80° N in August, but slowly in the north in that time. At the same time, visibility in July and August is lower than that in other months but higher in March and May. |
format |
Text |
author |
Yulong Shan Ren Zhang Ming Li Yangjun Wang Qiuhan Li Lifeng Li |
author_facet |
Yulong Shan Ren Zhang Ming Li Yangjun Wang Qiuhan Li Lifeng Li |
author_sort |
Yulong Shan |
title |
Generation and Analysis of Gridded Visibility Data in the Arctic |
title_short |
Generation and Analysis of Gridded Visibility Data in the Arctic |
title_full |
Generation and Analysis of Gridded Visibility Data in the Arctic |
title_fullStr |
Generation and Analysis of Gridded Visibility Data in the Arctic |
title_full_unstemmed |
Generation and Analysis of Gridded Visibility Data in the Arctic |
title_sort |
generation and analysis of gridded visibility data in the arctic |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2019 |
url |
https://doi.org/10.3390/atmos10060314 |
op_coverage |
agris |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Atmosphere; Volume 10; Issue 6; Pages: 314 |
op_relation |
Meteorology https://dx.doi.org/10.3390/atmos10060314 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/atmos10060314 |
container_title |
Atmosphere |
container_volume |
10 |
container_issue |
6 |
container_start_page |
314 |
_version_ |
1774713764834181120 |