Examination of Ice Ridging Methods Using Discrete Particles
The evolution of ice thickness distribution is examined using a number of Monte Carlo simulation strategies. The present paper extends the analysis of Thorndike (2000) to consider different ridging methods. Additionally, the thickness distribution is updated at regular time intervals, and taking int...
| Main Authors: | , , |
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| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
2001
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| Subjects: | |
| Online Access: | https://nrc-publications.canada.ca/eng/view/accepted/?id=9e46a7eb-05d6-41b2-a2e2-cfd360353da7 https://nrc-publications.canada.ca/eng/view/object/?id=9e46a7eb-05d6-41b2-a2e2-cfd360353da7 https://nrc-publications.canada.ca/fra/voir/objet/?id=9e46a7eb-05d6-41b2-a2e2-cfd360353da7 |
| Summary: | The evolution of ice thickness distribution is examined using a number of Monte Carlo simulation strategies. The present paper extends the analysis of Thorndike (2000) to consider different ridging methods. Additionally, the thickness distribution is updated at regular time intervals, and taking into account the influence of strain rates on ridging. The latter aspects are needed in order to adapt the Monte Carlo calculations for use in ice forecasting models. The ice cover is represented here by a large number of discrete particles. Starting from a given initial thickness distribution, ridging is introduced by changing the thickness and area of individual particles at regular time intervals. The results indicate that relatively small changes in ridging strategies may have significant effect on the evolution of the thickness distributions. Ridging (or increasing the thickness) of particles chosen and combined at random produces appropriate thickness distribution characteristics. Ridging the thinnest particles, on the other hand, does not produce such characteristics. NRC publication: Yes |
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