MOS, Perfect Prog, and Reanalysis

Caren Marzban Department of Statistics, University of Washington, Seattle, Washington, and Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Scott Sandgathe Applied Physics Laboratory, University of Washington, Seattle, Washington

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Eugenia Kalnay Department of Meteorology, University of Maryland, College Park, College Park, Maryland

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Abstract

Statistical postprocessing methods have been successful in correcting many defects inherent in numerical weather prediction model forecasts. Among them, model output statistics (MOS) and perfect prog have been most common, each with its own strengths and weaknesses. Here, an alternative method (called RAN) is examined that combines the two, while at the same time utilizes the information in reanalysis data. The three methods are examined from a purely formal/mathematical point of view. The results suggest that whereas MOS is expected to outperform perfect prog and RAN in terms of mean squared error, bias, and error variance, the RAN approach is expected to yield more certain and bias-free forecasts. It is suggested therefore that a real-time RAN-based postprocessor be developed for further testing.

Corresponding author address: Caren Marzban, Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, OK 73019. Email: marzban@caps.ou.edu

Abstract

Statistical postprocessing methods have been successful in correcting many defects inherent in numerical weather prediction model forecasts. Among them, model output statistics (MOS) and perfect prog have been most common, each with its own strengths and weaknesses. Here, an alternative method (called RAN) is examined that combines the two, while at the same time utilizes the information in reanalysis data. The three methods are examined from a purely formal/mathematical point of view. The results suggest that whereas MOS is expected to outperform perfect prog and RAN in terms of mean squared error, bias, and error variance, the RAN approach is expected to yield more certain and bias-free forecasts. It is suggested therefore that a real-time RAN-based postprocessor be developed for further testing.

Corresponding author address: Caren Marzban, Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, OK 73019. Email: marzban@caps.ou.edu

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