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J. McLean Sloughter, Tilmann Gneiting, and Adrian E. Raftery

distributions. Section 2 describes the data used in this study. In section 3 , we review the BMA technique and describe our extension of it to wind vectors. Then in section 4 we give results for 48-h-ahead forecasts of wind vectors over the North American Pacific Northwest in 2003 based on the eight-member University of Washington mesoscale ensemble ( Grimit and Mass 2002 ; Eckel and Mass 2005 ). Throughout the paper we use illustrative examples drawn from these data, and we find that BMA is better

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Jonathan Poterjoy and Fuqing Zhang

cases at most levels. This result suggests that the elevated values of CRH found near the disturbance in E4DVar and 4DEnVar analyses, compared to E3DVar in Figs. 2e and 3e , provide a better depiction of Karl’s pregenesis relative humidity field. We also verify the predicted track and intensity of the developing cyclone in deterministic forecasts. Here, the performance of the data assimilation techniques depends on how well each method captures features in analyses that produce accurate

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Thordis L. Thorarinsdottir and Matthew S. Johnson

as follows. In section 2 , we review the NGR technique and describe our extension of it to wind gust forecasting. In section 3 , we present the results of a case study in which we issue daily 48-h-ahead forecasts of maximum wind and gust speed over the North American Pacific Northwest in 2008 based on the eight-member University of Washington mesoscale ensemble ( Eckel and Mass 2005 ). Finally, a discussion is provided in section 4 . 2. Nonhomogeneous Gaussian regression The NGR or ensemble

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Rochelle P. Worsnop, Michael Scheuerer, and Thomas M. Hamill

skillful predictions at subseasonal to monthly time scales ( Hudson et al. 2011 ; White et al. 2017 ). At these scales, forecasts not only include influences from initial model conditions but also conditions that evolve on slower time scales such as soil moisture and sea surface temperatures ( White et al. 2017 ). Advances in data assimilation techniques, model initialization, physics parameterizations, and spatial and temporal resolution over the last decade now allow researchers to explore forecasts

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Stefan Siegert, Omar Bellprat, Martin Ménégoz, David B. Stephenson, and Francisco J. Doblas-Reyes

1. Introduction Hindcast experiments are routinely generated to detect systematic biases of forecast systems, and to assess forecast quality. Hindcast data from a competing forecast system are often available, from either a low-resolution version of the same forecast system, the system of a competing forecast institution, or a simple statistical benchmark forecast. It is then of interest to address the question whether the forecast system at hand offers an improvement over the competitor. A

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Durga Lal Shrestha, David E. Robertson, James C. Bennett, and Q. J. Wang

produce useable streamflow forecasts. The precipitation forecasts that are publicly available from Australian NWP models are deterministic and often contain systematic errors. This study evaluates a postprocessing technique for deterministic precipitation forecasts from a NWP model. The postprocessing method first applies a version of the Bayesian joint probability model to mean areal precipitation over individual catchment subareas and individual lead times to produce forecast ensembles that are bias

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Stephen G. Penny, David W. Behringer, James A. Carton, and Eugenia Kalnay

1. Introduction The National Centers for Environmental Prediction (NCEP) has used the same 3D variational data assimilation (3DVar) approach to provide initial conditions and verification of the ocean state since its development in the late 1980s ( Derber and Rosati 1989 ). The computationally inexpensive 3DVar was implemented operationally at NCEP within the Global Ocean Data Assimilation System (GODAS) in 2003 ( Behringer and Xue 2004 ; Behringer 2007 ) and as part of the Climate Forecast

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Alex M. Kowaleski and Jenni L. Evans

; Kim et al. 2011 ; Kozar et al. 2012 ; Paliwal and Patwardhan 2013 ). Many of these studies (e.g., Arnott et al. 2004 ; Nakamura et al. 2009 ; Kim et al. 2011 ) use nonhierarchical k -means or c -means clustering. Nonhierarchical clustering is also used on ensemble forecasts of synoptic conditions during and after ET ( Harr et al. 2008 ; Anwender et al. 2008 ; Keller et al. 2011 , 2014 ). These clustering techniques are well suited to clustering forecasts verifying at a single time

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Thomas M. Hamill and Michael Scheuerer

) to increase training sample size and improve the postprocessed NBM temperature guidance. It could also be used to improve longer-lead postprocessed guidance such as week +2 to week +4 temperature forecast products generated by the Climate Prediction Center. The forecast temperature training data in the current NBM use a decaying-average bias correction technique ( Cui et al. 2012 ) that requires archival of only the recent most forecast and analysis. While this procedure is attractive from the

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

1. Introduction Nonhomogeneous regression is a popular regression-based technique to statistically correct an ensemble of numerical weather prediction models (NWP; Leith 1974 ). Such corrections are often necessary since current NWP models cannot consider all error sources ( Lorenz 1963 ; Hamill and Colucci 1998 ; Mullen and Buizza 2002 ; Bauer et al. 2015 ) so that the raw forecasts are often biased and uncalibrated. In statistical postprocessing, various approaches have been developed to

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