An Objective Comparison of Model Output Statistics and “Perfect Prog” Systems in Producing Numerical Weather Element Forecasts

Brunet, N.; Verret, R.; Yacowar, N.

doi:10.1175/1520-0434(1988)003<0273:aocomo>2.0.co;2

Cited by 25 publications

(10 citation statements)

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“…The seasonal variation of BIAS and MAE ( Figure 5) shows that the smallest errors of minimum temperature on REC occur in summer. REC MAE of minimum temperature is generally less than the MOS and Perfect Prog MAE presented in Brunet et al (1988). The REC RMSE (not shown here) is also less than RMSE obtained by Kalman filter presented in Cattani (1994), Though the lead time in Ganalis & Anadranistakis is a bit less than here, the MAE is very similar.…”

Section: Minimum Temperatures Forecastscontrasting

confidence: 44%

A statistical method for forecasting extreme daily temperatures using ECMWF 2‐m temperatures and ground station measurements

Boi¹

2004

Meteorological Applications

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Section: Minimum Temperatures Forecastscontrasting

confidence: 44%

A statistical method for forecasting extreme daily temperatures using ECMWF 2‐m temperatures and ground station measurements

Boi¹

2004

Meteorological Applications

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“…Model output statistics (MOS) is an objective forecasting technique in which a statistical relationship is determined between a predictand and variables forecast by an NWP model (Glahn and Lowry 1972). The primary advantage of MOS is that model biases and local climatology are automatically built into the equations (Klein and Glahn 1974;Brunet et al 1988). Reap (1994a) developed MOS equations predicting the spatial distribution of CG lightning over Florida during different low-level flow regimes using predictors from the Nested Grid Model (NGM).…”

Section: Introductionmentioning

confidence: 99%

A Perfect Prognosis Scheme for Forecasting Warm-Season Lightning over Florida

Shafer

Fuelberg

2008

Monthly Weather Review

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This study develops and evaluates a statistical scheme for forecasting warm-season lightning over Florida. Four warm seasons of analysis data from the Rapid Update Cycle (RUC) and lightning data from the National Lightning Detection Network are used in a perfect prognosis technique to develop a highresolution, gridded forecast guidance product for warm-season cloud-to-ground (CG) lightning over Florida. The most important RUC-derived parameters are used to develop equations producing 3-hourly spatial probability forecasts for one or more CG flashes, as well as the probability of exceeding various flash count percentile thresholds. Binary logistic regression is used to develop the equations for one or more flashes, while a negative binomial model is used to predict the amount of lightning, conditional on one or more flashes occurring. The scheme is applied to output from three mesoscale models during an independent test period (the 2006 warm season). The evaluation is performed using output from the National Centers for Environmental Prediction (NCEP) 13-km RUC (RUC13), the NCEP 12-km North American Mesoscale Model, and local high-resolution runs of the Weather Research and Forecasting (WRF) Model for a domain over south Florida. Forecasts from all three mesoscale models generally show positive skill through the 2100-2359 UTC period with respect to a model containing only climatology and persistence (L-CLIPER) and persistence alone. A forecast example using the high-resolution WRF Model is shown for 16-17 August 2006. Although the exact timing and placement of forecast lightning are not perfect, there generally is good agreement between the forecasts and their verification, with most of the observed lightning occurring within the higher forecast probability contours.

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“…However, perfect prog forecasts are not bias free. Meanwhile, Brunet et al (1988) have shown that perfect prog outperforms MOS in short-term forecasts. Wilson and Vallée (2003) found that their updateable MOS outperforms both perfect prog and MOS, at least in forecasting 2-m temperature, 10-m wind direction and speed, and probability of precipitation.…”

Section: Introductionmentioning

confidence: 99%

MOS, Perfect Prog, and Reanalysis

Marzban

Sandgathe

Kalnay

2006

Monthly Weather Review

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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.

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An Objective Comparison of Model Output Statistics and “Perfect Prog” Systems in Producing Numerical Weather Element Forecasts

Cited by 25 publications

References 0 publications

A statistical method for forecasting extreme daily temperatures using ECMWF 2‐m temperatures and ground station measurements

A statistical method for forecasting extreme daily temperatures using ECMWF 2‐m temperatures and ground station measurements

A Perfect Prognosis Scheme for Forecasting Warm-Season Lightning over Florida

MOS, Perfect Prog, and Reanalysis

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