Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Stochastic sea ice generator for Monte Carlo modelling of navigation conditions at the Northern Sea Route. / May, Ruslan I.; Guzenko, Roman B.; Tarovik, Oleg V.; Topaj, Alex G.; Yulin, Alexander V.
Proceedings of the 26th International Conference on Port and Ocean Engineering under Arctic Conditions, POAC 2021. Lulea University of Technology, 2021. (Proceedings of the International Conference on Port and Ocean Engineering under Arctic Conditions, POAC; Том 2021-June).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
TY - GEN
T1 - Stochastic sea ice generator for Monte Carlo modelling of navigation conditions at the Northern Sea Route
AU - May, Ruslan I.
AU - Guzenko, Roman B.
AU - Tarovik, Oleg V.
AU - Topaj, Alex G.
AU - Yulin, Alexander V.
N1 - Publisher Copyright: © 2021 Lulea University of Technology. All rights reserved.
PY - 2021
Y1 - 2021
N2 - In this study, we applied a stochastic sea ice generator and automatic ice routing to investigate navigation conditions in the Arctic. The stochastic generator provides long (over 100 years) interconnected spatial-temporal fields of sea ice parameters that were used as a source of information to calculate navigation routes using the optimal ice routing tool. A stochastic sea ice generator is a probabilistic model that reproduces synthetic sea ice data. Temporal connectivity of generated data is provided by Markov chains, while spatial connectivity is achieved using empirical random probability fields. To determine the matrices of transition probabilities for Markov chains and empirical random probability fields of sea ice concentration, we used satellite data from the OSI SAF project for the last 20 years. The grid area of the probabilistic model covers the Barents Sea, Kara Sea, and nearby waters with a spatial resolution of 10 km. Time step of the model is one day. We tested the stochastic generator by comparing its results with statistical characteristics of ice concentration obtained from satellite data of the OSI SAF project. The mean absolute error of the statistical characteristics of ice concentration was found to be less than 5%. As an example of the practical use of the stochastic generator, we used ice routing tool to calculate the probability of navigation start at the line from Murmansk to Pobeda field (Kara Sea) on different calendar dates under the assumption that the vessel can operate in open water or very open ice only. The average and extremely early and late dates were estimated.
AB - In this study, we applied a stochastic sea ice generator and automatic ice routing to investigate navigation conditions in the Arctic. The stochastic generator provides long (over 100 years) interconnected spatial-temporal fields of sea ice parameters that were used as a source of information to calculate navigation routes using the optimal ice routing tool. A stochastic sea ice generator is a probabilistic model that reproduces synthetic sea ice data. Temporal connectivity of generated data is provided by Markov chains, while spatial connectivity is achieved using empirical random probability fields. To determine the matrices of transition probabilities for Markov chains and empirical random probability fields of sea ice concentration, we used satellite data from the OSI SAF project for the last 20 years. The grid area of the probabilistic model covers the Barents Sea, Kara Sea, and nearby waters with a spatial resolution of 10 km. Time step of the model is one day. We tested the stochastic generator by comparing its results with statistical characteristics of ice concentration obtained from satellite data of the OSI SAF project. The mean absolute error of the statistical characteristics of ice concentration was found to be less than 5%. As an example of the practical use of the stochastic generator, we used ice routing tool to calculate the probability of navigation start at the line from Murmansk to Pobeda field (Kara Sea) on different calendar dates under the assumption that the vessel can operate in open water or very open ice only. The average and extremely early and late dates were estimated.
KW - Arctic navigation
KW - Ice routing
KW - Monte Carlo method
KW - Stochastic ice generator
UR - http://www.scopus.com/inward/record.url?scp=85124130483&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85124130483
T3 - Proceedings of the International Conference on Port and Ocean Engineering under Arctic Conditions, POAC
BT - Proceedings of the 26th International Conference on Port and Ocean Engineering under Arctic Conditions, POAC 2021
PB - Lulea University of Technology
T2 - 26th International Conference on Port and Ocean Engineering under Arctic Conditions, POAC 2021
Y2 - 14 June 2021 through 18 June 2021
ER -
ID: 85107716