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Jie Chen, François P. Brissette, and Zhi Li

1. Introduction Ensemble weather forecasts offer great potential benefits for water resource management, as they provide useful information for analyzing the uncertainty of predicted variables ( Boucher et al. 2011 ). The advantages of ensemble weather forecasts over deterministic forecasts were observed in several studies, even at locations where the spatial resolution of ensemble forecasts was much lower ( Bertotti et al. 2011 ; Boucher et al. 2011 ). However, raw ensemble forecasts are

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Timothy DelSole and Michael K. Tippett

1. Introduction A basic question in weather and climate forecasting is whether one prediction system is more skillful than another. For instance, users want to know how different prediction systems have performed in the past, modelers want to know if changes in prediction system improved the skill, and operational centers want to know where to allocate resources. Regardless of application, a universal concern is whether the observed difference in skill reflects an actual difference or is

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Charles R. Sampson, James S. Goerss, John A. Knaff, Brian R. Strahl, Edward M. Fukada, and Efren A. Serra

1. Introduction Forecasting tropical cyclone (TC; see Table 1 for this and other acronyms used in this paper) surface wind structure has been one of the challenges of the forecast process at the Joint Typhoon Warning Center (JTWC). Wind structure analyses and forecasts are provided in terms of the “wind radii”. 1 Wind radii are defined as the maximum extent of 34- (R34), 50-, and 64-kt winds in the four compass quadrants (northeast, southeast, southwest, and northwest) surrounding the TC. In

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Nazario Tartaglione

1. Introduction Skill scores are often used to assess dichotomous forecasts. Dichotomous forecasts are obtained using a 2 × 2 contingency table ( Table 1 ), where the four elements of the table are the hit a , false alarm b , miss c , and correct negative events d . Skill scores measure the accuracy of a forecast over reference forecasts such as random chance, persistence, or climatology. Skill scores are frequently used to verify precipitation forecasts ( Accadia et al. 2003 , 2005

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Tushar Sinha, A. Sankarasubramanian, and Amirhossein Mazrooei

1. Introduction Over the last decade, considerable progress has been made in the ability to forecast seasonal streamflow through better understanding of climatic teleconnections (e.g., ENSO) as well as through the development of subgrid-scale land surface models (LSMs) that capture land–atmosphere interactions ( Koster and Suarez 1995 ; Betts et al. 1997 ; Hamlet et al. 2002 ; Maurer et al. 2002 ; Mahanama et al. 2012 ). Nevertheless, the skill of climate forecasts varies significantly for

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Andrew R. Lawrence and James A. Hansen

1. Introduction A popular method used to account for initial condition uncertainty in Numerical Weather Prediction (NWP) is the ensemble approach to forecasting ( Palmer et al. 1992 ; Toth and Kalnay 1993 ). The atmosphere’s initial condition probability distribution function (PDF) is discretely sampled, and each sample is propagated forward by the NWP model. The resulting collection of forecasts is treated as a discrete sample of the forecast PDF. One factor that can limit an ensemble

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Timothy DelSole and Michael K. Tippett

1. Introduction This paper is concerned with comparing the skill of two forecasts. One of the most elegant methods of comparing skill is the sign test . The procedure is simple: given a criterion for selecting the most skillful forecast of a single event, count the number of times that forecast A has more skill than forecast B. If the count is “large,” then forecast A is more skillful than forecast B, whereas if the count is “small” then A is less skillful than B. To define large and small

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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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Phillip E. Shafer and Henry E. Fuelberg

1. Introduction Over the past 30 yr, cloud-to-ground (CG) lightning has exceeded both tornadoes and hurricanes in causing weather-related fatalities across the United States ( Curran et al. 1997 ). Aside from the loss of life, lightning damages trees, buildings, and utility lines, and is one of the leading causes of power outages and disruptions to communications. Improved forecasts of the timing and location of thunderstorms and associated lightning are of great interest to all persons

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Ming Liu, Douglas L. Westphal, Annette L. Walker, Teddy R. Holt, Kim A. Richardson, and Steven D. Miller

or local scales, dust storms are associated with strong winds and severe turbulence ( Xu et al. 2000 ; Liu et al. 2000 ). These factors influenced the outcome of the hostage rescue mission in Iran in 1980. Real-time prediction of dust storms, especially quantitative forecasting of dust concentration and visibility, has become highly desirable as a meteorological service to the public and military activities. The development of advanced dust aerosol process-oriented numerical weather prediction

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