Author(s): Madis-Jaak Lilover; Tarmo Kouts; Kaimo Vahter
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Keywords: No Keywords
Abstract: Ice compression is a serious navigational risk for all ships proceeding in dynamic ice. In many cases, no obvious threat can be observed on the ice surface, yet ships get stuck in, or damaged by, compressive ice. Ship collisions are especially dangerous. Ice compression as a phenomenon is still not very well described in physical terms because it activates and develops very fast but can be very local at the same time. Classical methods for ice dynamics observations and modeling are insufficient to estimate the level of threat of ice compression in certain sea areas. Therefore, it is probably meaningful to describe the compression with one physical parameter (measurable) or with an integrated (composed) parameter (not directly measurable), divided into classes. We suggest the use of a fuzzy logic approach based on parameters obtained from numerical ice dynamic models and other information sources for ice compression in a ship-scale occurrence. Firstly, we take into account parameters which make up the potential (or power) of compression. These parameters include ice thickness, ice compactness, and ice categories, among others. Secondly, we consider parameters which trigger the potential of a compression threat. For the simplest model, such triggering parameters could include change in wind forcing, change in ice concentration, ridged ice growth rate, and other estimates that can be found from model output or remote sensing products. A fuzzy logic model considers both the potential and triggering measures and forecasts the hazard of ice compression for a given grid point. The model output is appropriately divided into classes such as very high, high, medium, low, and very low. For model calibration and validation, data from ocean circulation models -- including the ice submodel (e. g. HIROMB -- High Resolution Operational Model of the Baltic Sea) and pure ice dynamics models (e. g. HELMI -- HELsinki Multicategory Ice model) -- can be used together with relevant data from shipping (AIS), as well as direct observations from ship bridges, classical ice maps, satellite images, ice drifters, and marine radars.
Year: 2012