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Activity Number:
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611
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #305147 |
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Title:
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Bayesian Modeling of Wind Fields Using Surface Data Collected Over Land
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Author(s):
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Margaret Short*+ and Javier Fochesatto
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Companies:
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University of Alaska Fairbanks and Geophysical Institute Atmospheric Science Group
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Address:
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P.O. Box 750125, Fairbanks, AK, 99775,
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Keywords:
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wind field ; spatial model ; Markov chain Monte Carlo ; measurement error
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Abstract:
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We propose an approach to modeling wind fields in which the error structure includes the type of instrumentation used to collect such data over land, namely anemometers (for wind speed) and vanes (for wind direction). Thus the model can handle both the periodicity of the wind direction and the non-negativity of the wind speed. Measurement error depends in part on the wind speed and is incorporated in the model using a circular distribution; in particular, the measurements become less reliable at low wind speeds. We use a Bayesian approach and fit the models using Markov chain Monte Carlo. Model performance is illustrated with a Swiss surface wind data set.
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