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John W. Kidson and Craig S. Thompson

models nested within a GCM. Statistical techniques have their origin in the model output statistics (MOS) and “perfect prog” approaches to forecasting surface weather elements (e.g., Klein 1982 ; Glahn 1985 ). They include the use of regression analysis (e.g., Kim et al. 1984 ; Klein and Bloom 1989 ; Karl et al. 1990 ; Hewitson 1994 ), canonical correlations ( von Storch et al. 1993 ), and neural networks ( Hewitson 1997 ). While correlation techniques may be appropriate to continuous variables

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Dallas Foster, Darin Comeau, and Nathan M. Urban

demonstrate that the advantages of enhanced estimation and forecast calibration gained from the Bayesian approach is significant. We compare a suite of estimation techniques, including the ML and various MAP estimators corresponding to combinations of the various aforementioned priors. Furthermore, we test the forecast ability using probabilistic metrics to understand the ability of each method to capture the proper forecast distribution. We consider the task of forecasting yearly SST anomalies. We use

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Stefano Materia, Andrea Borrelli, Alessio Bellucci, Andrea Alessandri, Pierluigi Di Pietro, Panagiotis Athanasiadis, Antonio Navarra, and Silvio Gualdi

1. Introduction Seasonal scale has recently become a crucial time frame for climate forecast, owing to its socioeconomic relevance. In the last decade, important advances have been achieved, thanks to the development of fully coupled general circulation models (CGCMs) initialized for seasonal prediction ( Kug et al. 2008 ; Kim et al. 2012 ). Dynamical predictions are based on the assumption that large-scale and long-lasting anomalies will convey predictive skill to seasonal forecast. In

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Hui Ding, Matthew Newman, Michael A. Alexander, and Andrew T. Wittenberg

). Nevertheless, applying this technique to tropical Indo-Pacific Ocean forecasts yields skill, such as shown in Fig. 1 , that is not only surprisingly competitive with the traditional approach of executing an initialized forecast ensemble from the corresponding CGCMs, but actually appears to exceed it in the key ENSO region within the equatorial eastern Pacific. Fig . 1. (a) Model-analog and (b) NMME hindcast skills of observed SST variations at 6-month lead. Only RMS skill score is shown. The grand mean of

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Robert E. Livezey, Anthony G. Barnston, George V. Gruza, and Esther Ya Ran'kova

(Manuscript received 16 January 1993, in final form l0 August 1993) ABSTRACT Analog prediction systems developed in the United States and the former Soviet Union are com0ared forU.S. seasonal temperature prediction. Of primary interest is the viability of the Russian "optimization" conceptfor a priori selection of U.S. seasonal analog forecast predictors. Optimization is a si:eeific technique for choosingpredictor variables for analog matching on a forecast-by-forecast basis. Validation of

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Hans von Storch, Gerd Bürger, Reiner Schnur, and Jin-Song von Storch

. POPs are successful inidentifying two independent modes with similar timescales in the same dataset. The POP method can also produce forecasts that may potentially be used as a reference for other forecastmodels. The conventional POP analysis technique has been generalized in various ways. In the cyclostationary POPanalysis, the estimated system matrix is allowed to vary deterministically with an externally forced cycle. In thecomplex POP analysis, not only the state of the system but also

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W. A. Müller, C. Appenzeller, F. J. Doblas-Reyes, and M. A. Liniger

1. Introduction In recent years probabilistic ensemble forecast systems have been established in a wide area of applications. The probabilistic nature of these forecasts requires verification techniques based on probabilistic skill measures. However, there is no general agreement on the best skill score. The choice depends on the particular application considered or the forecast system being used. Examples are Brier scores (BSs) or the relative operating characteristics (for details see Swets

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Liwei Jia, Timothy DelSole, and Michael K. Tippett

1. Introduction Many regression techniques for improving the skill of dynamical forecasts are applied pointwise or to smoothed fields. For instance, Hamill et al. (2004) applied a logistic regression technique to improve medium-range probabilistic forecasts at each observation location. Krishnamurti et al. (1999 , 2000 ) showed improved forecast skill by applying multiple regression to combine forecasts from multiple dynamical models into a single forecast for each grid. More advanced

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Wayne A. Woodward and H. L. Gray

be forecast to continue. To address this question, the generic problem of determining whether an observed trend in a time series realization is a random (i.e., short-term) trend or a deterministic(i.e., permanent) trend is considered. The importance of making this determination is that forecasts based onthese two scenarios are dramatically different. Forecasts based on a series with random trends will not predictthe observed trend to continue, while forecasts based on a model with deterministic

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Seung-On Hwang, Jae-Kyung E. Schemm, Anthony G. Barnston, and Won-Tae Kwon

1. Introduction In this study, canonical correlation analysis (CCA) is used to explore the amount and the origin of seasonal forecast skill for surface temperature and precipitation in far eastern midlatitude Asia, including mainly Korea and Japan. CCA is a multivariate linear statistical technique of relating variation in predictor fields to variation in predictand fields using an eigenanalysis of the cross-dataset correlation matrix. Predictive skill in long-range forecasting has been

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