Statistical Correction of a Deterministic Numerical Weather Prediction Model
The forecasting skill of meteorologists is determined largely by their ability to interpret output from deterministic numerical weather prediction (NWP) models in the light of local conditions. Biases in the deterministic model may arise for many reasons, including the inability to account for physi...
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Veröffentlicht in: | Journal of the American Statistical Association 2001-09, Vol.96 (455), p.794-804 |
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Sprache: | eng |
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Zusammenfassung: | The forecasting skill of meteorologists is determined largely by their ability to interpret output from deterministic numerical weather prediction (NWP) models in the light of local conditions. Biases in the deterministic model may arise for many reasons, including the inability to account for physical processes at a scale smaller than the grid used in the numerical solution of the model equations. A statistical method for correction and interpretation of NWP model output is the model output statistics technique (MOS), where regression analysis is used to relate observations and NWP model predictions. We describe a Bayesian hierarchical approach to MOS that was motivated by the need to develop sensible statistical corrections for recently opened stations with a short historical data record. The strength of the Bayesian hierarchical formulation is its ability to combine information from stations with short and long observational records in a sensible way. Markov chain Monte Carlo methods are used for computation, and our approach is illustrated by using daily maximum temperature data from a network of 29 stations in the Sydney area. |
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ISSN: | 0162-1459 1537-274X |
DOI: | 10.1198/016214501753208825 |