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Marion P. Mittermaier

1. Background Forecast centers typically wish to compute verification statistics to show that the numerical weather prediction (NWP) models that are run to produce forecasts are providing guidance that is skillful, and useful. This immediately begs the question: Skillful and useful relative to what? One can list several reasons as to why this interest in model performance exists. In the first instance, model performance relative to what actually happened is of interest. Second, model

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Peter Schaumann, Mathieu de Langlard, Reinhold Hess, Paul James, and Volker Schmidt

1. Introduction In many situations, it is possible to have access to several probabilistic forecasts of the same event ( Clemen 1989 ; Graham 1996 ; Ariely et al. 2000 ; Winkler and Poses 1993 ). As these forecasts might be provided by independent models, nonnegligible differences can be observed. It is then necessary to find a combination of all forecasts for decision makers. Keeping the probabilistic forecast that performs best for some specific scores, thus dropping the others, is not an

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David E. Robertson and Q. J. Wang

1. Introduction Forecasts of future seasonal streamflows provide valuable information to many water users and managers, including irrigators, hydroelectricity generators, rural and urban water supply authorities, and environmental managers ( Plummer et al. 2009 ). Statistical techniques commonly used as a practical approach to produce forecasts of seasonal streamflows include regression models ( Garen 1992 ; Kwon et al. 2009 ; Lima and Lall 2009 ; Pagano et al. 2009 ; Ruiz et al. 2007

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Sanjib Sharma, Ridwan Siddique, Nicholas Balderas, Jose D. Fuentes, Seann Reed, Peter Ahnert, Robert Shedd, Brian Astifan, Reggina Cabrera, Arlene Laing, Mark Klein, and Alfonso Mejia

1. Introduction Precipitation is a key forcing of interest in many forecasting applications ( Cherubini et al. 2002 ; Ebert and McBride 2000 ; Ebert et al. 2003 ; Fritsch et al. 1998 ; Hall et al. 1999 ; Voisin et al. 2008 ; Zhu and Luo 2015 ). Precipitation forecasts are used to issue severe weather warnings ( Messner et al. 2014 ); forecast floods and other hydrological variables ( Kim and Barros 2001 ); support the operation of water supply reservoirs ( Demargne et al. 2014 ; Pagano

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Nicholas A. Bond and Clifford F. Mass

1. Introduction Although a large body of work has examined the skill of weather forecasts, less effort has been devoted to examining the development of forecast skill, with the notable exceptions of Gedzelman (1978) and Roebber and Bosart (1996) . This paper extends their work, using the results from 10 yr of forecasts made by students enrolled in a senior-level weather analysis and forecasting class (ATMS 452) taught at the University of Washington. The primary objective of ATMS 452 is to

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Paul J. Roebber

1. Introduction The utility of deterministic weather forecasts is undermined by uncertainties arising from sensitive dependence on initial conditions ( Lorenz 1963 and many others) and model error (e.g., Harrison et al. 1999 ; Stensrud et al. 2000 ; Orrell 2005 ). Probabilistic forecasts are the logical alternative, since they can be used to quantify the degree of this uncertainty. In meteorology, probabilistic forecasts are most often generated using ensembles of numerical weather

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Jean-Jacques Morcrette, George Mozdzynski, and Martin Leutbecher

this radiation burden, radiation transfer is only computed every few model hours. For example, with full radiation computations performed every 2 h at all grid points, radiation transfer accounts for 27% of the run time of the “GME” forecast model ( Majewski et al. 2002 ). The recent introduction of the McRad package for radiation computations ( Morcrette et al. 2008 ) in the Integrated Forecasting System (IFS) has increased the cost of the radiation computations and required revisiting the use of

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Christine Johnson and Neill Bowler

1. Introduction Ensemble forecasts are used to give probabilistic forecasts. One aspect to the usefulness of these forecasts, known as the resolution , is that we would like there to be a large variability of the observed frequencies associated with different forecast probabilities around the climatological value. Another aspect to the usefulness of these forecasts, known as the reliability , is that the probabilities should give an estimate of the expected frequencies of the event occurring

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Andrew Cottrill, Harry H. Hendon, Eun-Pa Lim, Sally Langford, Kay Shelton, Andrew Charles, David McClymont, David Jones, and Yuriy Kuleshov

island Countries (SCOPIC) model, which is derived from statistical relationships between sea surface temperature (SST) variations, primarily those associated with El Niño–La Niña, and the local climate in the Pacific. He and Barnston (1996) have previously reported on similar statistically based seasonal forecasts for the Pacific region. Although these statistical forecasts have merit, especially in the Pacific region where El Niño–La Niña impacts are strong, the PASAP seasonal prediction project

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Charles R. Sampson, James A. Hansen, Paul A. Wittmann, John A. Knaff, and Andrea Schumacher

disaster was the creation of weather facilities in the western North Pacific and eventually the creation of the Joint Typhoon Warning Center (JTWC). Since ship safety and performance is highly dependent on sea state, wave models that produce forecasts of significant wave height and swell are of great interest to the navy. Forecasting the state of the sea is also of great importance to commercial shipping, offshore oil/gas operations, and recreational boating, to name a few additional interests

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