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Activity Number:
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403
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 9, 2006 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statisticians in Defense and National Security
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| Abstract - #306124 |
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Title:
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Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts
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Author(s):
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Veronica Berrocal*+
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Companies:
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University of Washington
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Address:
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Box 354322, Seattle, WA, 98195,
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Keywords:
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Abstract:
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Probabilistic weather forecasts are obtained by running numerical weather prediction models with varying initial conditions and/or model parameters, resulting in ensembles of deterministic forecasts. However, forecast ensembles are often underdispersive and therefore uncalibrated. We introduce a statistical postprocessing technique, called Spatial Bayesian model averaging (Spatial BMA), to calibrate forecast ensembles of whole weather fields. Spatial BMA provides statistical ensembles of weather field forecasts that take the spatial structure of observed fields into account and honor the information contained in the original ensemble. The technique was applied to 48-h forecasts of surface temperature over the North American Pacific Northwest using the University of Washington mesoscale ensemble with good results.
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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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