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Abstract Details
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Activity Number:
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99
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Type:
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Invited
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Date/Time:
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Monday, August 1, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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ENAR
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| Abstract - #300424 |
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Title:
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Spatio-Temporal Models for Oceanic Data
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Author(s):
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Bruno Sanso*+ and Ricardo Lemos
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Companies:
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University of California at Santa Cruz and NOAA/NMFS Environmental Research Division
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Address:
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Department of Applied Mathematics and Statistics, School of Engineering, Santa Cruz, 95064, Canada
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Keywords:
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Bayesian Hierarchical Models ;
Spatio-temporal models ;
Climatology
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Abstract:
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We present a review of Bayesian hierarchical models for the reconstruction of oceanic properties. By using a hierarchical spatio-temporal model we are able to consider long series of observations irregularly scattered in space and time. Additionally, we account for observational errors and incorporate structural information about the underlying physical processes. Our latest development is HOMER: a Hierarchical Ocean Model for Extended Reconstructions. Its goal is to obtain smooth three dimensional fields of temperature and salinity, as well as long term climatologies, on a monthly time scale. We develop carefully designed Markov chain Monte Carlo algorithms on distributed machines to handle massive datasets that correspond to long time series and large geographical domains.
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