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S. Vannitsem and C. Nicolis

1. Introduction Operational numerical weather (or climate) predictions deteriorate as a function of lead time because of the presence of modeling and initial condition errors. To partly correct this decrease of skill postprocessors are commonly used based on (linear or nonlinear) statistical methods (see, e.g., Casaioli et al. 2003 ; Kalnay 2003 ; Marzban 2003 ; Wilks 2006 ). These are usually referred to as model output statistics (MOS) techniques. One of the most popular approaches

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Piet Termonia and Alex Deckmyn

1. Introduction Model output statistics (MOS) ( Glahn and Lowry 1972 ) provides a practical tool to improve the skill scores of raw NWP model output. Such scores increasingly play a decisive role in determining the economical value of weather forecasts ( Katz and Murphy 1997 ) in social and commercial applications. MOS provides a simple yet powerful tool to increase their competitiveness. Since its introduction in operational weather forecasting, the skill of combined systems of models and MOS

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Nina Schuhen, Thordis L. Thorarinsdottir, and Tilmann Gneiting

technique, the BMA approach of Sloughter (2009) and Sloughter et al. (2012) , ensemble copula coupling (ECC; Schefzik 2011 ), and the postprocessing method proposed by Pinson (2012) , all of which are directed at the bivariate postprocessing of ensemble forecasts of wind vectors. The appendix describes our verification methods. 2. Ensemble model output statistics for wind vectors A wind vector is determined by wind speed and wind direction, or by its zonal (west–east) and meridional (north

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Jonathan M. Eden and Martin Widmann

simulated precipitation. This approach, known as model output statistics (MOS), requires the statistical link to be derived between precipitation observations and precipitation from some historical simulation; in application, as with PP downscaling, the statistical link is applied to the simulated predictor (in this case, precipitation) for a future scenario. In principle, MOS offers a number of potential benefits over PP. Although only applicable to the numerical model upon which it has been calibrated

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Bryan P. Holman, Steven M. Lazarus, and Michael E. Splitt

averaging (BMA; Raftery et al. 2005 ), and ensemble model output statistics (EMOS; Gneiting et al. 2005 ). These approaches convert ensemble forecasts into predictive probability density functions (PDF) using training data comprising recent forecast errors. The predictive PDF parameters, its mean, and variance, represent bias- and dispersion-corrected functions of the ensemble mean and ensemble variance, respectively ( Gneiting 2014 ). In this study we focus on calibration methods for ensemble wind

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Geraldine Wong, Douglas Maraun, Mathieu Vrac, Martin Widmann, Jonathan M. Eden, and Thomas Kent

statistical link (usually as some form of regression model) between large-scale and local-scale weather observations and transfers this relationship to a global climate simulation for future simulations ( Maraun et al. 2010b ). Model output statistics (MOS) approaches, originally developed to correct systematic biases in weather forecasts ( Glahn and Lowry 1972 ), statistically “correct” climate model biases ( Maraun et al. 2010b ). For a chosen climate model, MOS infers a correction function between a

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Maxime Taillardat, Olivier Mestre, Michaël Zamo, and Philippe Naveau

was employed for probabilistic forecasts of precipitation ( Hamill et al. 2008 ; Wilks 2009 ; Ben Bouallègue 2013 ). Two approaches are baseline in postprocessing techniques: the Bayesian model averaging (BMA; Raftery et al. 2005 ) and the ensemble model output statistics (EMOS; Gneiting et al. 2005 ). Whereas the BMA predictive distribution is a mixture of PDF depending on the variable to calibrate, the EMOS technique fits a single PDF from a raw ensemble. All parameters of theses PDFs are

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David E. Rudack and Judy E. Ghirardelli

(NWS) four times daily, with amendments as necessary. TAFs consist of forecasts of critical weather elements, such as CIG and VIS, which are expected to impact an airport over a specific time period. This time period is usually 24 h, with selected airports requiring TAFs out to 30 h, the first 6 h being recognized as the critical TAF period ( NWS 2008 ). The NWS’s Meteorological Development Laboratory (MDL) has been producing objective statistical guidance in the form of model output statistics

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Judy E. Ghirardelli and Bob Glahn

) showed a general improvement in local NWS aviation forecasts, a later study ( Dallavalle and Dagostaro 1995 ) showed no improvement. In addition, while the NWP models have improved, they have not yet developed to the point that the forecasts cannot be improved with postprocessing. Dallavalle et al. (2004) compared model output statistics (MOS) guidance from the Global Forecast System (GFS; Kalnay et al. 1990 ) and the Nested Grid Model (NGM; Hoke et al. 1989 ) with the direct model output from

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Bob Glahn, Kathryn Gilbert, Rebecca Cosgrove, David P. Ruth, and Kari Sheets

1. Introduction Model output statistics (MOS) guidance forecasts have been produced at points and provided to National Weather Service (NWS) forecasters and private entities for over three decades ( Glahn and Lowry 1972 ; Carter et al. 1989 ). As the numerical weather prediction (NWP) models became more accurate, MOS followed that trend ( WMO 1999 , 249–251). Up until a few years ago, the MOS produced at observation locations met the basic need for guidance, although there have been many

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