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Toward a Systematic Evaluation of Warm Conveyor Belts in Numerical Weather Prediction and Climate Models. Part I: Predictor Selection and Logistic Regression Model

Julian F. Quinting and Christian M. Grams

regression and the limitations of this approach. In section 4 we evaluate the performance of the models during Northern Hemisphere winter and demonstrate their applicability to an operational ECMWF ensemble forecast of a WCB event during January 2011. The study ends with concluding remarks and an outlook in section 5 . 2. Data a. Predictor dataset The predictor selection as well as the development and evaluation of the logistic regression models is based on ECMWF’s interim reanalysis data (ERA

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Yi-Hung Kuo, Kathleen A. Schiro, and J. David Neelin

-term forecasting purposes, since models adopted for weather forecasting or reanalysis share common components with climate models. Many conventional diagnostics for climate models emphasize comparisons against long-term climatology or variability at different time scales, and the model performance examined by these metrics is affected by multiple factors. While sensitivity experiments with respect to such metrics are useful in identifying important processes ( Benedict et al. 2013 , 2014 ; Boyle et al. 2015

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Fiaz Ahmed and J. David Neelin

convective systems contributing more to the rapid precipitation increases. The proposed physical argument for the precipitation onset is buoyancy centric. Holloway and Neelin (2009 , hereafter HN09 ) showed, using a steady-state entraining plume model, that for environmental moisture values at and beyond precipitation onset, the entraining plumes are positively buoyant near the freezing level. The implication is that if a convective entity—often represented by a bulk-entraining plume—can survive mixing

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