Towards a probabilistic model for predicting ship besetting in ice in Arctic waters

International audienc

On risk management of shipping system in ice-covered waters : Review, analysis and toolbox based on an eight-year polar project

Publisher Copyright: © 2022 The AuthorsWith the climate change, polar sea ice is diminishing. This, on one hand, enables the possibility for e.g., Arctic shipping and relevant resource exploitation activities, but on the other hand brings additional risks induced by these activities. Increasing research focuses have been observed on the relevant topics in the complex and harsh polar environment and its fragile ecosystem. However, from risk management perspective, there is still a lack of holistic analysis and understanding towards safe shipping in the ice-covered waters and its available models applicable for managing risks in the system. Therefore, this paper aims to establish a framework and analysis for better understanding of this gap. The paper targets a comprehensive and long-term project specifically focusing on holistic safe shipping in ice-covered waters as the analysis basis. It firstly creates a holistic framework for the shipping system in ice-covered waters and then implements review and analysis of project publications on their overall features. Quantitative prediction models are selected for a structured applicability analysis. Furthermore, an extensive review outside the project following the elements established for the holistic shipping system is conducted so that this paper provides an overview of models for the shipping system in ice-covered waters, addressing the status of the current toolbox. Moreover, it helps to identify the next scientific steps on risk management of shipping in ice-covered waters.Peer reviewe

A comprehensive approach to scenario-based risk management for Arctic waters

While society benefits from Arctic shipping, it is necessary to recognize that ship operations in Arctic waters pose significant risks to people, the environment, and property. To support the management of those risks, this article presents a comprehensive approach addressing both short-term operational risks, as well as risks related to long-term extreme ice loads. For the management of short-term operational risks, an extended version of the Polar Operational Limit Assessment Risk Indexing System (POLARIS) considering the magnitude of the consequences of potential adverse events is proposed. For the management of risks related to long-term extreme ice loads, guidelines are provided for using existing analytical, numerical, and semi-empirical methods. In addition, to support the design of ice class ship structures, the article proposes a novel approach that can be used in the conceptual design phase for the determination of preliminary scantlings for primary hull structural members.Peer reviewe

Operational risk assessment for shipping in Arctic waters

Arctic navigation has many complexities due to its particular features such as ice, severe weather conditions, remoteness, low temperatures, lack of crew experience, and extended period of darkness or daylight. For these reasons, vessels, such as oil tankers, dry cargo ships, offshore supply vessels, research vessels, and passenger ships operating in the Arctic waters may pose a high risk of collision with ice and other ships causing human casualties, environmental pollution and the loss of assets. This thesis presents a conceptual framework that is focused on collision modelling. In order to understand the process of risk escalation and to attempt a proactive approach in constituting the collision models for Arctic navigation, the present thesis identifies various risk factors that are involved in a collision. Furthermore, the thesis proposes the probabilistic framework tools that are based on the identified risk factors to estimate the risks of collision in the Arctic. The proposed frameworks are used to model the collision based risk scenarios in the region. They are developed with the use of Bayesian Networks, the Nagel-Schreckenberg (NaSch), and Human Factor Analysis and Classification (HFACS) models. In the present thesis, the proposed models are theoretical in nature, but they can be useful in developing a collision monitoring system that provides a real time-estimate of collision probability that could help avoid collisions in the Arctic. Further, the estimated probabilities are also useful in decision making concerning safe independent and convoy operations in the region. The proposed frameworks simplifies maritime accident modeling by developing a practical understanding of the role of physical environment, navigational and operational related aspects of ships, and human errors, such as individual lapses, management failures, organizational failures, and economic factors in the collision related accidents in the Arctic. This research also identifies the macroscopic properties of maritime traffic flow and demonstrates how these properties influence collision properties. The thesis also presents an innovative accident model for ice-covered waters that estimates the collision probability and establishes the relationship between the macroscopic properties of the traffic flow with the contributory accidental risk factors in the region. The main focus of the present thesis is, to better understand, communicate, and incorporate specific risk factors into the maritime risk assessment processes, involve shipping organizations to agree on best practice methodologies and make the data sources easily available, and modify the Arctic risk management processes by implementing effective risk assessment techniques and appropriate risk treatment

Data-driven Regularization and Uncertainty Estimation to Improve Sea Ice Data Assimilation

Accurate estimates of sea ice conditions such as ice thickness and ice concentration in the ice-covered regions are critical for shipping activities, ice operations and weather forecasting. The need for this information has increased due to the recent record of decline in Arctic ice extent and thinning of the ice cover, which has resulted in more shipping activities and climate studies. Despite the extensive studies and progress to improve the quality of sea ice forecasts from prognostic models, there is still significant room for improvement. For example, ice-ocean models have difficulty estimating the ice thickness distribution accurately. To help improve model forecasts, data assimilation is used to combine observational data with model forecasts and produce more accurate estimates. The assimilation of ice thickness observations, compared to other ice parameters such as ice concentration, is still relatively unexplored since the satellite-based ice thickness observations have only recently become common. Also, preserving sharp features of ice cover, such as leads and ridges, can be difficult, due to the spatial correlations in the background error covariance matrices. At the same time, the current ice concentration assimilation systems do not directly assimilate high resolution sea ice information from synthetic aperture radar (SAR), even though they are the main source of information for operational production of ice chart products at the Canadian Ice Service. The key challenge in SAR data assimilation is automating the interpretation of SAR images. To address the problem of assimilating ice thickness observations while preserving sharp features, two different objective functions are studied. One with a conventional l2-norm and one imposing an additional l1-norm on the derivative of the ice thickness state estimate as a sparse regularization. The latter is motivated by analysis of high resolution ice thickness observations derived from an airborne electromagnetic sensor demonstrating the sparsity of the ice thickness in the derivative domain. The data fusion and data assimilation experiments are performed over a wide range of background and observation error correlation length scales. Results demonstrate the superiority of using a combined l1-l2 regularization framework especially when the background error correlation length scale was relatively short (approximately five times the analysis grid spacing). The problem of automated information retrieval from SAR images has been explored in a problem of ice/water classification. The selected classification approach takes advantage of neural networks to produce results comparable to a previous study using logistic regression. The employed dataset in both studies is a comprehensive dataset consisting of 15405 SAR images over a seven year period, covering all months and different locations. In addition, recent neural network uncertainty estimation approaches are employed to estimate the uncertainty associated with the classification of ice/water labels, which was not explored in this problem domain previously. These predicted uncertainties can improve the automated classification process by identifying regions in the predictions that should be checked manually by an analyst

Forgotten Laxdæla poetry : a study and an edition of Tyrfingur Finnsson's Vísur uppá Laxdæla sögu

The paper discusses the metre and the diction of a previously unpublished small poem about characters of Laxdæla saga, composed in 18th century. The stanzas are ostensibly in skaldic dróttkvætt; the analysis shows it to be an imitation of the classical metre, yet a remarkably successful one, implying an extraordinarily good grasp of dróttkvætt poetics on the part of a poet composing several hundred years after the end of the classical dróttkvætt period