Temporal variance of the ice navigation conditions within the Northern Sea Route

Recognition of satellite images, composition of them and vectorization is used in AARI for ice charts production. There is discussed methodology and results of the ice charts processing by means of computer programs, which were elaborated by Dr. Tretyakov in Python. The paper demonstrates results of analysis of temporal variance of ice navigation circumstances within the buffer zone of the marine transport system from the Sabetta Port (the Yamal Peninsula, Russia) up to the Bering Strait. There are considered the variance for April and May from 1998 up to 2020. This intra-annual interval is the one with the heaviest ice circumstances for shipping. We used conditional length of various age and age and form gradations of the sea ice for the route as a whole, as an integral parameter for estimation of the navigation hardships of ice navigation. The conditional length of an ice age (thickness) diapason is result of multiplication of the diapason partial concentration at the length of the route leg with homogeneous ice characteristics. There were produced series of the conditional lengths for each ten-day periods during April and May. Then statistical homogeneity of the series was tested by various methods.

Near-Field Route Optimization-Supported Polar Ice Navigation via Maritime Radar Videos

The accurate design of ship routing plans in arctic areas is not easy, considering that navigation conditions (e.g., weather, visibility, and ice thickness) may change frequently. A ship’s crew identifies sea ice in arctic channels with the help of radar echoes, and ship maneuvering decisions are made to avoid navigation interference. Ship officials must manually and consistently change the ship’s route of travel, which is time-consuming and tedious. To address this issue, we propose a near-field route optimization model for the purpose of automatically selecting an optimal route with the help of radar echo images. The ship near-field route optimization model uses a multiobjective optimal strategy considering factors of minimum navigation risk and steaming distance. We verified the model’s performance with the support of the Xuelong voyage dataset. This research finding can help a ship’s crew to design more reasonable navigation routes in polar channels.

Analysis of ice conditions of year-round navigation of Arc7 vessels in the southwestern part of the Kara Sea

Since 2006, a new generation of reinforced ice class Arc7 vessels has been operating on the Northern Sea Route. Safe and efficient sailing of this type of vessels in sea ice demands a detailed study of ice conditions. Accumulation and analysis of data on ice and hydrometeorological conditions for the entire Arctic in comparison with ice conditions along the route of vessels is an essential part of the development of optimal variants and optimal routes for ice navigation.The main aim of the study was to generalize the conditions of ice navigation of Norilskiy Nickel vessels along the optimal navigational routes in the south-western part of the Kara Sea. Based on the reports on sailing obtained from vessels of the “Norilskiy Nickel” type for the 2006–2014 period, we calculated the probability of choosing the optimal route along the Murmansk – Dudinka passage: through the Kara Gate Strait (seaward, central or coastal route) or the north of Cape Zhelaniya. During the year, vessels move predominantly through the Kara Gate. However, for three month per year, from April to June, the most appropriate route lies to the north of the Zhelaniya Cape. In April – May it is, on average, every second navigation, and in June – more than 80 % of all navigation. The features of the ice regime determining the choice of the specific navigation route, are described. The speeds of vessels of the “Norilskiy Nickel” type along various navigation routes in drifting sea ice of the Kara Sea are calculated. The fastest speed in drifting ice was recorded in the winter navigations of 2007–2008 and 2011–2012, in the January-May of these years the average speed was 10.2 and 11.2, correspondingly. The minimum speed in these years, even during the months of maximum ice cover growth, was not less than 4.8 knots. In other years, the average speeds were in the range of 9.2–9.8 knots. During the whole period of study, ice conditions that were extremely difficult for navigation formed three times: at the end of May 2009, at the end of March 2010 and in the middle of March 2011, these are considered in more detail in the present article.

Towards an automatic ice navigation support system in the Arctic area

Conventional ice navigation through sea ice is manually operated by well-trained navigators, whose experiences are heavily relied upon to guarantee the ship's safety. Despite increasingly available ice data and information, little has been done to develop automatic ice navigation systems to better guide ships in sea ice. In this study firstly navigable sea areas for different types of ships were identified according to the navigation codes in northern regions. Secondly, three algorithms of path planning were adopted to automatically compute the safest-and-shortest ship routes based on the concepts of the Voronoi diagram, Visibility graph, and Visibility-Voronoi diagram, respectively. These algorithms and results were compared and evaluated in terms of different application scenarios. Results show that the Visibility-Voronoi approach seems to be the best viable solution in terms of computing performance and navigation safety. The work will provide a basis for further development towards an automatic ice navigation support system

Towards an automatic ice navigation support system in the Arctic area

Conventional ice navigation through sea ice is manually operated by well-trained navigators, whose experiences are heavily relied upon to guarantee the ship's safety. Despite increasingly available ice data and information, little has been done to develop automatic ice navigation systems to better guide ships in sea ice. In this study firstly navigable sea areas for different types of ships were identified according to the navigation codes in northern regions. Secondly, three algorithms of path planning were adopted to automatically compute the safest-and-shortest ship routes based on the concepts of the Voronoi diagram, Visibility graph, and Visibility-Voronoi diagram, respectively. These algorithms and results were compared and evaluated in terms of different application scenarios. Results show that the Visibility-Voronoi approach seems to be the best viable solution in terms of computing performance and navigation safety. The work will provide a basis for further development towards an automatic ice navigation support system

Towards an Automatic Ice Navigation Support System in the Arctic Sea

Conventional ice navigation in the sea is manually operated by well-trained navigators, whose experiences are heavily relied upon to guarantee the ship’s safety. Despite the increasingly available ice data and information, little has been done to develop an automatic ice navigation support system to better guide ships in the sea. In this study, using the vector-formatted ice data and navigation codes in northern regions, we calculate ice numeral and divide sea area into two parts: continuous navigable area and the counterpart numerous separate unnavigable area. We generate Voronoi Diagrams for the obstacle areas and build a road network-like graph for connections in the sea. Based on such a network, we design and develop a geographic information system (GIS) package to automatically compute the safest-and-shortest routes for different types of ships between origin and destination (OD) pairs. A visibility tool, Isovist, is also implemented to help automatically identify safe navigable areas in emergency situations. The developed GIS package is shared online as an open source project called NavSpace, available for validation and extension, e.g., indoor navigation service. This work would promote the development of ice navigation support system and potentially enhance the safety of ice navigation in the Arctic sea.

Towards an Automatic Ice Navigation Support System in the Arctic Sea

Conventional ice navigation in the sea is manually operated by well-trained navigators, whose experiences are heavily relied upon to guarantee the ship’s safety. Despite the increasingly available ice data and information, little has been done to develop an automatic ice navigation support system to better guide ships in the sea. In this study, using the vector-formatted ice data and navigation codes in northern regions, we calculate ice numeral and divide sea area into two parts: continuous navigable area and the counterpart numerous separate unnavigable area. We generate Voronoi Diagrams for the obstacle areas and build a road network-like graph for connections in the sea. Based on such a network, we design and develop a geographic information system (GIS) package to automatically compute the safest-and-shortest routes for different types of ships between origin and destination (OD) pairs. A visibility tool, Isovist, is also implemented to help automatically identify safe navigable areas in emergency situations. The developed GIS package is shared online as an open source project called NavSpace, available for validation and extension, e.g., indoor navigation service. This work would promote the development of ice navigation support system and potentially enhance the safety of ice navigation in the Arctic sea.

Exploring the Pacific Arctic Seasonal Ice Zone With Saildrone USVs

More high-quality, in situ observations of essential marine variables are needed over the seasonal ice zone to better understand Arctic (or Antarctic) weather, climate, and ecosystems. To better assess the potential for arrays of uncrewed surface vehicles (USVs) to provide such observations, five wind-driven and solar-powered saildrones were sailed into the Chukchi and Beaufort Seas following the 2019 seasonal retreat of sea ice. They were equipped to observe the surface oceanic and atmospheric variables required to estimate air-sea fluxes of heat, momentum and carbon dioxide. Some of these variables were made available to weather forecast centers in real time. Our objective here is to analyze the effectiveness of existing remote ice navigation products and highlight the challenges and opportunities for improving remote ice navigation strategies with USVs. We examine the sources of navigational sea-ice distribution information based on post-mission tabulation of the sea-ice conditions encountered by the vehicles. The satellite-based ice-concentration analyses consulted during the mission exhibited large disagreements when the sea ice was retreating fastest (e.g., the 10% concentration contours differed between analyses by up to ∼175 km). Attempts to use saildrone observations to detect the ice edge revealed that in situ temperature and salinity measurements varied sufficiently in ice bands and open water that it is difficult to use these variables alone as a reliable ice-edge indicator. Devising robust strategies for remote ice zone navigation may depend on developing the capability to recognize sea ice and initiate navigational maneuvers with cameras and processing capability onboard the vehicles.