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Fumin Ren, Chenchen Ding, Da-Lin Zhang, Deliang Chen, Hong-li Ren, and Wenyu Qiu

obtaining model forecasts ( Danforth and Kalnay 2008a ). The first empirical strategy is NWP model dependent and has been widely used with various data assimilation techniques in both the operational and research communities (e.g., Kalnay 2002 ). Leith (1978) developed a statistical method in which a state-dependent empirical correction is applied to the dynamical model through minimizing the model tendency errors at every time step. Subsequently, numerous studies have shown improvements of NWP with

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Lisa K. Bengtsson, Linus Magnusson, and Erland Källén

2004–28 February 2005 for forecasts valid at 1200 UTC each day. To generate initial perturbations for the EPS, ECMWF use the singular vector (SV) technique ( Barkmeijer et al. 1999 ; Leutbecher and Palmer 2008 ). Singular vectors are designed to identify directions in phase space that give the largest amplifications of perturbations during a finite-time interval (optimization time), in order to find possible extreme events in the atmosphere. To calculate the singular vectors a tangent linear

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Laurence J. Wilson, Stephane Beauregard, Adrian E. Raftery, and Richard Verret

correlation between forecast and observation. A prime example is model output statistics (MOS; Glahn and Lowry 1972 ), which for over 30 yr has been used in many centers to improve the output of deterministic operational forecasts. MOS typically uses linear statistical predictive techniques such as regression to relate a historical set of model forecast variables (“predictors”) to surface observations. If the predictor set includes the model estimate of the predictand variable, then MOS explicitly

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Ben P. Kirtman and Dughong Min

compared all possible multimodel 11-member ensemble combinations of CCSM + CFS against all possible 11-member ensembles from CFS alone. Over 93% of the time the multimodel ensemble has a higher Niño-3.4 correlation coefficient. d. Traditional significance testing The forecasting approach advocated here is, obviously, a multi-CGCM-based approach. Nevertheless, a comparison with standard statistical forecasting techniques provides useful benchmarks for evaluating the CGCM forecasts (e.g., van Oldenborgh

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N. Vigaud, A. W. Robertson, and M. K. Tippett

can be enhanced by multimodel ensemble (MME) techniques, as has been demonstrated for seasonal ( Robertson et al. 2004 ) and medium-range ( Hamill 2012 ) forecasting. Extended logistic regression (ELR), as used here, includes the quantile threshold along with the ensemble mean as predictor and produces mutually consistent quantile probabilities that sum to one ( Wilks 2009 ; Wilks and Hamill 2007 ). In this respect, this study presents a first attempt to produce weekly and week 3–4 MME

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Jeremy D. Berman, Ryan D. Torn, Glen S. Romine, and Morris L. Weisman

appropriate time with perturbations taken from the WRF three-dimensional variational data assimilation ( Barker et al. 2012 ) system via the fixed covariance perturbation technique of Torn et al. (2006) . This study focuses on forecasts initialized at 1200 UTC 10 June 2013 (i.e., about 33 h prior to convective initiation); however, a similar analysis was performed on forecasts initialized 12 h (0000 UTC 11 June) and 24 h (1200 UTC 11 June) afterward. c. Forecast sensitivity The role of forecast errors at

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Tommaso Diomede, Chiara Marsigli, Andrea Montani, Fabrizio Nerozzi, and Tiziana Paccagnella

regression ( Gneiting et al. 2005 ), logistic regression ( Hamill and Whitaker 2006 ), extended logistic regression ( Wilks 2009 ; Roulin and Vannitsem 2012 ), analog techniques ( Hamill and Whitaker 2006 ), neural networks ( Yuan et al. 2007 ), and several others. Simpler techniques such as linear regression ( Atger 2003 ) and quantile-to-quantile mapping ( Hamill and Whitaker 2006 ) were also applied. The choice of an appropriate method depends on the characteristics of the ensemble forecasts and on

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Jonathan Poterjoy, Ryan A. Sobash, and Jeffrey L. Anderson

. 2003 ; Lei and Bickel 2011 ; Frei and Künsch 2013 ; Van Leeuwen et al. 2015 (chapter 2); Robert and Künsch 2017 ; Penny and Miyoshi 2016 ; Chustagulprom et al. 2016 ]; see Rebeschini and van Handel (2015) and Morzfeld et al. (2017) for an informative review of localization in PFs. The current study applies the local PF in the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ) to demonstrate that the techniques outlined in P16 are sufficient for reducing the well

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Aaron J. Hill, Christopher C. Weiss, and Brian C. Ancell

forecasts of precipitation. They showed strong dynamic links of precipitation development along a dryline to the employed perturbations with strong nonlinear precipitation responses. Here the authors explore a technique that requires minimal computational expense and a capability to reveal dynamic features in the initial conditions that may impact CI timing forecasts. Ensemble-based sensitivity analysis (ESA; Ancell and Hakim 2007 ; Hakim and Torn 2008 ; Torn and Hakim 2008 ) develops linear

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Guillem Candille

dispersion—measures the agreement between the spread defined by the ensemble and the observation standard deviation, and the forecast error of the ensemble mean. A dispersion larger than 1 characterizes an underdispersive system. The limited number of cases on which the statistics are accumulated could restrict the significance of the diagnoses. Confidence intervals (5%–95%) on the CRPS and the RCRV are then defined by bootstrap techniques ( Efron and Tibshirani 1993 ) in order to provide statistically

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