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Open AccessArticle

Generation and Analysis of Gridded Visibility Data in the Arctic

1
College of Meteorology and Oceanography, National University of Defense Technology, Nanjing 210000, China
2
Transportation Branch, Shanghai Beian Industrial Co., Ltd., Shanghai 200000, China
*
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(6), 314; https://doi.org/10.3390/atmos10060314
Received: 29 April 2019 / Revised: 17 May 2019 / Accepted: 27 May 2019 / Published: 6 June 2019
(This article belongs to the Section Climatology and Meteorology)
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. View Full-Text
Keywords: the Arctic passage; back propagation neural network; visibility; spatial and temporal features the Arctic passage; back propagation neural network; visibility; spatial and temporal features
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MDPI and ACS Style

Shan, Y.; Zhang, R.; Li, M.; Wang, Y.; Li, Q.; Li, L. Generation and Analysis of Gridded Visibility Data in the Arctic. Atmosphere 2019, 10, 314.

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