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Activity Number:
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49
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Type:
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Contributed
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Date/Time:
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Sunday, August 3, 2003 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistics & the Environment
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| Abstract - #302285 |
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Title:
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Stochastic Modeling of Sea Ice
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Author(s):
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Theodoro Koulis*+
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Companies:
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University of Waterloo
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Address:
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Dept. of Stats. And Act. Sci., Waterloo, ON, N2L 3G1, Canada
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Keywords:
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Markov ; spatial ; modeling ; estimation ; nearest neighbor
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Abstract:
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The role of seasonal sea ice formation at the poles is complex and closely linked to the Earth's climate. It is thought that the amount of sea ice can have a significant effect on the energies transferred between the atmosphere and the ocean. Understanding the seasonal sea ice process at the poles is therefore of great interest to scientists. Sea ice concentration datasets derived from Earth-orbiting satellites are readily available and contain observations that span several decades. This data, which is both spatial and temporal in nature, can be quite difficult to analyze. We present a spatial nearest-neighbor model as a candidate for describing the sea ice process. The model is a Markov process on a lattice and can be controlled through two parameters. These parameters give some insight on the long-term behavior of the process. We will discuss various methods for estimating these parameters. The methods are based on differential equations associated with the biased-voter model. It is hoped that these methods will be helpful in analyzing multitemporal spatial data and to make inferences on global climate change.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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