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Probabilistic Forecasts from the National Digital Forecast Database
- Article from:
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Weather and Forecasting
- Article date:
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April 1, 2008
- Author:
- Krzysztofowicz, Roman; Evans, W Britt
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Copyright informationCopyright American Meteorological Society Apr 2008. Provided by ProQuest LLC. (Hide copyright information)
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ABSTRACT
The Bayesian processor of forecast (BPF) is developed for a continuous predictand. Its purpose is to process a deterministic forecast (a point estimate of the predictand) into a probabilistic forecast (a distribution function, a density function, and a quantile function). The quantification of uncertainty is accomplished via Bayes theorem by extracting and fusing two kinds of information from two different sources: (i) a long sample of the predictand from the National Climatic Data Center, and (ii) a short sample of the official National Weather Service forecast from the National Digital Forecast Database. The official forecast is deterministic and hence deficient: it contains no ...