Statistical Correction of a Deterministic Numerical Weather Prediction Model

David J Nott David J. Nott is Lecturer, Department of Statistics, University of New South Wales, Sydney NSW 2052, Australia . William T. M. Dunsmuir is Professor, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455 . Robert Kohn is Professor, Australian Graduate School of Management, University of New South Wales, Sydney NSW 2052, Australia . Frank Woodcock is Senior Professional Officer, Bureau of Meteorology Research Centre, Melbourne VIC 3001, Australia . The authors thank the staff of the Sydney regional office of the Bureau of Meterology for their time and patience in answering questions. The authors thank Graham Mills and John McBride of the Bureau of Meteorology Research Centre for their comments on an earlier version of this paper, and the referees, associate editor, and editor for helpful comments. The research of the first two authors was supported by SPIRT grant number C19700144 from the Australian Research Council.

William T. M Dunsmuir David J. Nott is Lecturer, Department of Statistics, University of New South Wales, Sydney NSW 2052, Australia . William T. M. Dunsmuir is Professor, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455 . Robert Kohn is Professor, Australian Graduate School of Management, University of New South Wales, Sydney NSW 2052, Australia . Frank Woodcock is Senior Professional Officer, Bureau of Meteorology Research Centre, Melbourne VIC 3001, Australia . The authors thank the staff of the Sydney regional office of the Bureau of Meterology for their time and patience in answering questions. The authors thank Graham Mills and John McBride of the Bureau of Meteorology Research Centre for their comments on an earlier version of this paper, and the referees, associate editor, and editor for helpful comments. The research of the first two authors was supported by SPIRT grant number C19700144 from the Australian Research Council.

Robert Kohn David J. Nott is Lecturer, Department of Statistics, University of New South Wales, Sydney NSW 2052, Australia . William T. M. Dunsmuir is Professor, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455 . Robert Kohn is Professor, Australian Graduate School of Management, University of New South Wales, Sydney NSW 2052, Australia . Frank Woodcock is Senior Professional Officer, Bureau of Meteorology Research Centre, Melbourne VIC 3001, Australia . The authors thank the staff of the Sydney regional office of the Bureau of Meterology for their time and patience in answering questions. The authors thank Graham Mills and John McBride of the Bureau of Meteorology Research Centre for their comments on an earlier version of this paper, and the referees, associate editor, and editor for helpful comments. The research of the first two authors was supported by SPIRT grant number C19700144 from the Australian Research Council.

Frank Woodcock David J. Nott is Lecturer, Department of Statistics, University of New South Wales, Sydney NSW 2052, Australia . William T. M. Dunsmuir is Professor, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455 . Robert Kohn is Professor, Australian Graduate School of Management, University of New South Wales, Sydney NSW 2052, Australia . Frank Woodcock is Senior Professional Officer, Bureau of Meteorology Research Centre, Melbourne VIC 3001, Australia . The authors thank the staff of the Sydney regional office of the Bureau of Meterology for their time and patience in answering questions. The authors thank Graham Mills and John McBride of the Bureau of Meteorology Research Centre for their comments on an earlier version of this paper, and the referees, associate editor, and editor for helpful comments. The research of the first two authors was supported by SPIRT grant number C19700144 from the Australian Research Council.

Abstract

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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Statistical Correction of a Deterministic Numerical Weather Prediction Model

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