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K. Fraedrich and L. M. Leslie

AUOUST 1987 K. FRAEDRICH AND L. M. LESLIE 1645Evaluation of Techniques for the Operational, Single Station, Short-Term Forecasting of Rainfall at a Midlatitude Station (Melbourne) K. FRAEDRICH* AND L. M. LESLIEBureau of Meteorology Research Centre, Melbourne, Victoria, 3001, Australia(Manuscript received 22 September 1986, in final form 21 January 1987)ABSTRACT Probabilit~ of

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Craig H. Bishop and Kevin T. Shanley

observations fall outside the range of values predicted by the raw ensemble with a frequency much greater than that expected if the ensemble forecasts were drawn from the distribution of truth given the forecast. Dressing ( Roulston and Smith 2002 ; Wang and Bishop 2005 ; Fortin et al. 2006 ) or Bayesian model averaging (BMA) techniques ( Raftery et al. 2005 ) are specifically designed to address the common problem of ensemble under dispersion and have been employed by, among others, Wilson et al. (2007

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Xingqin Fang and Ying-Hwa Kuo

produce excessive rainfall at the southern tip of the CMR (see Fang et al. 2011 ). Although the exact reason for this rainfall overprediction bias is still not clear, such a topographically locked rainfall bias will be amplified by the probability-matching technique. In this paper, we develop a modified probability-matching technique for ensemble forecasting of the topography-enhanced typhoon heavy rainfall over Taiwan. The basic idea is to collect the track and rainfall forecasts from a large

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Samantha L. Lynch and Russ S. Schumacher

) and Mullen and Buizza (2001) also found that the accuracy in precipitation forecasts decreases as the rainfall threshold increases. Hakim and Torn (2008) introduced the technique of ensemble synoptic analysis for an extratropical cyclone, finding the relationships between different synoptic features by computing statistical operators such as covariances and correlations within an ensemble of forecasts. Similarly, Torn (2010) used this method to examine the dynamical mechanisms that led to

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Manuel Gebetsberger, Jakob W. Messner, Georg J. Mayr, and Achim Zeileis

, and G. Brunet , 2015 : The quiet revolution of numerical weather prediction . Nature , 525 , 47 – 55 , doi: 10.1038/nature14956 . 10.1038/nature14956 Ben Bouallègue , Z. , and S. E. Theis , 2014 : Spatial techniques applied to precipitation ensemble forecasts: From verification results to probabilistic products . Meteor. Appl. , 21 , 922 – 929 , doi: 10.1002/met.1435 . 10.1002/met.1435 Bentzien , S. , and P. Friederichs , 2012 : Generating and calibrating probabilistic

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Debra M. Ford, Russell L. Elsberry, Patrick A. Harr, and Paul H. Dobos

MAY 1993 FORD ET AL. 1279Forecasting Tropical Cyclone Recurvature. Part II: An Objective Technique Using an Empirical Orthogonal Function Representation of Vorticity FieldsDEBRA M. FORD,* RUSSELL L. ELSBERRY, PATRICK A. HARR, AND PAUL H. DOBOSDepartment of Meteorology, Naval Postgraduate School, Monterey, California(Manuscript received 9 March 1992, in final form 13 October 1992

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Yong Wang, Martin Bellus, Jean-Francois Geleyn, Xulin Ma, Weihong Tian, and Florian Weidle

1. Introduction Ensemble prediction techniques have been applied in most numerical weather prediction (NWP) centers as a dynamical way of accounting for the forecast uncertainty. The optimal design of an ensemble prediction system (EPS) strongly depends on the quantification of uncertainties due to errors in initial conditions (ICs), model formulation, and physical parameterizations. Additional challenges posed for a skillful regional EPS include, for example, the problem of quantifying the

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Stefano Alessandrini, Simone Sperati, and Luca Delle Monache

1. Introduction The analog ensemble (AnEn) technique has been recently used to generate probabilistic predictions of 10-m wind speed and 2-m temperature ( Delle Monache et al. 2013 , hereafter DM13 ) starting from a deterministic meteorological forecast. The theoretical basis for the analog approach was provided by Hamill and Whitaker (2006) who used it to calibrate probabilistic predictions of 24-h accumulated precipitation from a numerical weather prediction (NWP) ensemble. The AnEn uses a

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Alexandre O. Fierro, Jidong Gao, Conrad L. Ziegler, Edward R. Mansell, Donald R. MacGorman, and Scott R. Dembek

improving the forecast skill for storm and MCS events at cloud-resolving scales (≤4 km) using numerical weather prediction (NWP) models. This study focuses on evaluating two distinct data assimilation techniques that are aimed at improving the initial representation (e.g., placement, intensity, and morphology) of the convection during the analysis time and subsequent short-term (i.e., ≤6 h) forecasts for the case of the 29–30 June 2012 MCS and derecho event. The first technique is a recently developed

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Sarah Strazzo, Dan C. Collins, Andrew Schepen, Q. J. Wang, Emily Becker, and Liwei Jia

1. Introduction Seasonal climate forecasts provide valuable information for a number of climate-sensitive societal sectors, including agriculture, energy, and public health (e.g., Challinor et al. 2005 ; Hawkins et al. 2013 ; Shukla et al. 2014 ; Tompkins and Di Giuseppe 2015 ; Torralba et al. 2017 ). Over time, seasonal climate prediction has evolved from an endeavor relying primarily on statistical modeling (e.g., van den Dool 2007 ) to one that increasingly utilizes dynamical climate

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