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L. Stefanova and T. N. Krishnamurti

1. Introduction Various methods of ensemble averaging have been carried out in previous studies by numerous authors. Examples include Thompson (1977) , Fraedrich and Smith (1989) , Wobus and Kalnay (1995) , Sarda et al. (1996) , and Pavan and Doblas-Reyes (2000) . The superensemble is a technique developed by Krishnamurti et al. (1999) that produces a single forecast derived from a multimodel set of forecasts. It differs from a conventional bias-removed multimodel ensemble in that the

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G. A. Vecchi, T. Delworth, R. Gudgel, S. Kapnick, A. Rosati, A. T. Wittenberg, F. Zeng, W. Anderson, V. Balaji, K. Dixon, L. Jia, H.-S. Kim, L. Krishnamurthy, R. Msadek, W. F. Stern, S. D. Underwood, G. Villarini, X. Yang, and S. Zhang

1. Introduction Predicting and projecting future tropical cyclone (TC) activity is a topic of scientific interest and high societal significance. Forecasts of TCs provide information to support planning, with the potential utility of the forecasts limited in part by their expected and realized skill and by the relevance of the quantity being predicted to the particular decision structure. A variety of methodologies have been developed to predict the path and intensity of individual TCs days in

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Thomas H. Jagger and James B. Elsner

-validation exercise to obtain an estimate of how well the set of covariates will predict future data. However, as noted in Elsner and Schmertmann (1994) , this exercise does not result in a “full” cross validation as the procedure for selecting the reduced set of covariates is not itself cross validated. Cross validation is a procedure for assessing how well an algorithm for choosing a particular model (including the predictor selection phase) will do in forecasting the unknown future ( Michaelsen 1987

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Andrew Schepen, Q. J. Wang, and David Robertson

1. Introduction Probabilistic seasonal rainfall forecasts are important for users such as irrigators and water managers to assist in developing risk-management strategies and to inform decisions. Both statistical and dynamical climate prediction systems are widely used in practice to produce probabilistic seasonal rainfall forecasts up to a year in advance ( Goddard et al. 2001 ). Statistical prediction systems are based on empirical relationships between observed variables and therefore rely

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Lei Wang, Xiaojun Yuan, Mingfang Ting, and Cuihua Li

mainly focused on the seasonal forecast of sea ice concentration (SIC) and sea ice extent (SIE), either using coupled dynamical models (e.g., Sigmond et al. 2013 ; Wang et al. 2013 ; Zhang et al. 2013 ; Chevallier et al. 2013 ; Merryfield et al. 2013 ; Peterson et al. 2015 ; Msadek et al. 2014 ) or statistical models based on past observations of the atmospheric and oceanic states [e.g., Lindsay et al. 2008 ; Kapsch et al. 2013 ; see Guemas et al. (2016) for more references]. Much less

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Tim P. Barnett

of the last23 years have been made with a new two-tiered climate forecast technique. The internal model variability (IMV)within the atmospheric model used in the forecasts was large, approximately 1/3 to 1/2 the variability obtainedfrom the same model forced by observed SST over the last 20 yr. This variability can lead to a wide range ofrealistic-looking forecasts, even when the equatorial SST forcing was nil. For example, single model forecastsforced by climatological SST produced excellent

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Viatcheslav V. Kharin and Francis W. Zwiers

forecast estimation are considered here. First, a parametric approach to deriving probabilistic information from an ensemble of deterministic forecasts is compared to the straightforward nonparametric approach based on the relative number of the ensemble members in a category of interest. The parametric probability estimator exploits explicitly the properties of the assumed underlying distribution of seasonal variations. Second, a statistical skill improvement technique for biased probability forecasts

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Yoo-Geun Ham, Siegfried Schubert, and Yehui Chang

–sea coupled models. For example, Vitart et al. (2007) showed that the European Centre for Medium-Range Weather Forecasts (ECMWF) coupled model is skillful in predicting the evolution of the MJO up to about 14 days, and Seo (2009) and Seo et al (2009) showed that the National Centers for Environmental Prediction (NCEP)’s operational coupled Climate Forecast System (CFS) model exhibits useful skill out to 2 and 3 pentads when the initial MJO convection is located over the Maritime Continent and the

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Stefan Hastenrath

five years in the seasonal forecasting for certain target regions of the Tropics. Variousapproaches are of interest: (a) empirical methods based on the combination of general circulation diagnosticsand statistical techniques; (b) numerical modeling, itself requiring also a diagnostic understanding from empiricalanalyses; and (c) purely statistical techniques. Regional targets include Indian monsoon, Nile and Ethiopia,eastern Africa, southern Africa, Sahel, Nordeste, North Atlantic storms, northwest

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Mark J. Rodwell and Francisco J. Doblas-Reyes

) based on the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) record ( Uppala et al. 2005 ). The much higher daily observed values for summer 2003 are shown by the dotted curve. Horizontal dashed and dotted lines show the corresponding weekly mean and monthly mean values. Important questions are how well was this 2003 event “predicted,” and what impact did the predictions have on decision making? The solid black curve shows a single high-resolution forecast

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