Prediction of visibility in the Arctic based on dynamic Bayesian network analysis

Shijun Zhao; Yulong Shan; Ismail Gultepe

doi:10.1007/s13131-021-1826-z

Volume 41 Issue 4

Apr. 2022

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Shijun Zhao, Yulong Shan, Ismail Gultepe. Prediction of visibility in the Arctic based on dynamic Bayesian network analysis[J]. Acta Oceanologica Sinica, 2022, 41(4): 57-67. doi: 10.1007/s13131-021-1826-z

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Shijun Zhao, Yulong Shan, Ismail Gultepe. Prediction of visibility in the Arctic based on dynamic Bayesian network analysis[J]. Acta Oceanologica Sinica, 2022, 41(4): 57-67. doi: 10.1007/s13131-021-1826-z

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Prediction of visibility in the Arctic based on dynamic Bayesian network analysis

doi: 10.1007/s13131-021-1826-z

1.
College of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, China
2.
Cloud Physics and Severe Weather Research, Environment and Climate Change Canada, Toronto M4Y1M7, Canada

More Information

Corresponding author: E-mail: yulongshan008@gmail.com
Received Date: 2020-10-12
Accepted Date: 2021-02-06

Available Online: 2022-02-12

Publish Date: 2022-04-01

Abstract

Abstract

With the accelerated warming of the world, the safety and use of Arctic passages is receiving more attention. Predicting visibility in the Arctic has been a hot topic in recent years because of navigation risks and opening of ice-free northern passages. Numerical weather prediction and statistical prediction are two methods for predicting visibility. As microphysical parameterization schemes for visibility are so sophisticated, visibility prediction using numerical weather prediction models includes large uncertainties. With the development of artificial intelligence, statistical prediction methods have received increasing attention. In this study, we constructed a statistical model with a physical basis, to predict visibility in the Arctic based on a dynamic Bayesian network, and tested visibility prediction over a 1°×1° grid area averaged daily. The results show that the mean relative error of the predicted visibility from the dynamic Bayesian network is approximately 14.6% compared with the inferred visibility from the artificial neural network. However, dynamic Bayesian network can predict visibility for only 3 days. Moreover, with an increase in predicted area and period, the uncertainty of the predicted visibility becomes larger. At the same time, the accuracy of the predicted visibility is positively correlated with the time period of the input evidence data. It is concluded that using a dynamic Bayesian network to predict visibility can be useful over Arctic regions for projected climatic changes.
- Arctic,
- visibility prediction,
- artificial neural network,
- dynamic Bayesian network

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References(32)

References

[1]	Boneh T, Weymouth G T, Newham P, et al. 2015. Fog forecasting for Melbourne Airport using a Bayesian decision network. Weather and Forecasting, 30(5): 1218–1233. doi: 10.1175/WAF-D-15-0005.1
[2]	Bremnes J B, Michaelides S C. 2007. Probabilistic visibility forecasting using neural networks. Pure and Applied Geophysics, 164(6): 1365–1381. doi: 10.1007/s00024-007-0223-6
[3]	Chmielecki R M, Raftery A E. 2011. Probabilistic visibility forecasting using Bayesian model averaging. Monthly Weather Review, 139(5): 1626–1636. doi: 10.1175/2010MWR3516.1
[4]	Dee D P, Uppala S M, Simmons A J, et al. 2011. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656): 553–597. doi: 10.1002/qj.828
[5]	Freeman E, Woodruff S D, Worley S J, et al. 2017. ICOADS Release 3.0: a major update to the historical marine climate record. International Journal of Climatology, 37(5): 2211–2232. doi: 10.1002/joc.4775
[6]	Gultepe I, Isaac G A. 2006. Visibility versus precipitation rate and relative humidity. In: 12th Conference on Cloud Physics. Madison, WI: American Meteorological Society, 2: 1161–1178
[7]	Gultepe I, Isaac G A, Rasmussen R, et al. 2011. A freezing fog/drizzle event during the FRAM-S project. In: SAE International/AIAA, International Conference on Aircraft and Engine and Ground-De-Icing. Chicago: SAE
[8]	Gultepe I, Milbrandt J A, Zhou Binbin. 2017. Marine fog: A review on microphysics and visibility prediction. In: Koračin D, Dorman C, eds. Marine Fog: Challenges and Advancements in Observations. Cham: Springer, 345–394
[9]	Gultepe I, Müller M D, Boybeyi Z. 2006. A new visibility parameterization for warm-fog applications in numerical weather prediction models. Journal of Applied Meteorology and Climatology, 45(11): 1469–1480. doi: 10.1175/JAM2423.1
[10]	Gultepe I, Pavolonis M, Zhou Binbin, et al. 2015. Freezing fog and drizzle observations. In: SAE 2015 International Conference on Icing of Aircraft, Engines, and Structures. Prague, Czech Republic: SAE,
[11]	Gultepe J, Pearson G, Milbrandt J A, et al. 2009. The fog remote sensing and modeling field project. Bulletin of the American Meteorological Society, 90(3): 341–360. doi: 10.1175/2008BAMS2354.1
[12]	Gultepe I, Sharman R, Williams P D, et al. 2019. A review of high impact weather for aviation meteorology. Pure and Applied Geophysics, 176(5): 1869–1921. doi: 10.1007/s00024-019-02168-6
[13]	Hong Quan. 2003. The trend of visibility and its affecting factors in Chongqing. Journal of Chongqing University, 26(5): 151–154. doi: 10.3969/j.issn.1000-582X.2003.05.037
[14]	Jin W, Li Z J, Wei L S, et al. 2000. The improvements of BP neural network learning algorithm. In: 2000 5th International Conference on Signal Processing Proceedings, 16th World Computer Congress 2000. Piscataway, NJ: IEEE,1647–1649
[15]	Kutiel H, Hirsch-Eshkol T R, Türkeş M. 2001. Sea level pressure patterns associated with dry or wet monthly rainfall conditions in Turkey. Theoretical and Applied Climatology, 69(1−2): 39–67. doi: 10.1007/s007040170034
[16]	Li Ming, Liu Kefeng. 2018. Application of intelligent dynamic Bayesian network with wavelet analysis for probabilistic prediction of storm track intensity index. Atmosphere, 9(6): 224. doi: 10.3390/atmos9060224
[17]	Li Hongxi, Zheng Zhongyi, Li Meng. 2011. A method based on rough sets to assess significance of factors influencing navigation safety in harbor waters. In: 11th International Conference of Chinese Transportation Professionals (ICCTP). Nanjing: American Society of Civil Engineers
[18]	Marzban C, Leyton S, Colman B. 2007. Ceiling and visibility forecasts via neural networks. Weather and Forecasting, 22(3): 466–479. doi: 10.1175/WAF994.1
[19]	McClelland J L, Rumelhart D E, PDP Research Group. 1987. Parallel Distributed Processing, vol. 2: Psychological and Biological Models. Cambridge: ‎ A Bradford Book
[20]	Otto H, Jukka J, Vesa N. 2007. Climatological tools for low visibility forecasting. Pure and Applied Geophysics, 164(6−7): 1383–1396. doi: 10.1007/s00024-007-0224-5
[21]	Pasini A, Pelino V, Potestà S. 2001. A neural network model for visibility nowcasting from surface observations: Results and sensitivity to physical input variables. Journal of Geophysical Research: Atmospheres, 106(D14): 14951–14959. doi: 10.1029/2001JD900134
[22]	Pearl J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo: Morgan Kaufmann Publishers
[23]	Qu Ping, Xie Yiyang, Liu Lili, et al. 2014. Character Analysis of Sea Fog in Bohai Bay from 1 to 2010. Plateau Meteorology, 33(1): 285–293
[24]	Roquelaure S, Bergot T. 2008. A local ensemble prediction system for fog and low clouds: Construction, Bayesian model averaging calibration, and validation. Journal of Applied Meteorology and Climatology, 47(12): 3072–3088. doi: 10.1175/2008JAMC1783.1
[25]	Roquelaure S, Bergot T. 2009. Contributions from a Local Ensemble Prediction System (LEPS) for improving fog and low cloud forecasts at airports. Weather and Forecasting, 24(1): 39–52. doi: 10.1175/2008waf2222124.1
[26]	Roquelaure S, Tardif R, Remy S, et al. 2009. Skill of a ceiling and visibility local ensemble prediction system (LEPS) according to fog-type prediction at Paris-Charles de Gaulle Airport. Weather and Forecasting, 24(6): 1511–1523. doi: 10.1175/2009WAF2222213.1
[27]	Shan Yulong, Zhang Ren, Li Ming, et al. 2019. Generation and analysis of gridded visibility data in the Arctic. Atmosphere, 10(6): 314. doi: 10.3390/atmos10060314
[28]	Shi Zhifu, Zhang An. 2012. Bayesian Network Theory and Its Application in Military System. Beijing: National Defense Industry Press, 110
[29]	Xu Chenfeng. 2016. Visibility change and applicable rules of action for ships. World Shipping, 39(1): 35–37
[30]	Xue Dan, Li Chengfan, Liu Qian. 2015. Visibility characteristics and the impacts of air pollutants and meteorological conditions over Shanghai, China. Environmental Monitoring and Assessment, 187(6): 363. doi: 10.1007/s10661-015-4581-8
[31]	Zhao Ping, Zhu Yani, Zhang Qin. 2012. A summer weather index in the East Asian pressure field and associated atmospheric circulation and rainfall. International Journal of Climatology, 32(3): 375–386. doi: 10.1002/joc.2276
[32]	Zhou Binbin, Du Jun, Gultepe I, et al. 2012. Forecast of low visibility and fog from NCEP: Current status and efforts. Pure and Applied Geophysics, 169(5-6): 895–909. doi: 10.1007/s00024-011-0327-x