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Hong Guan and Yuejian Zhu

1. Introduction An extreme weather event is unusual, unexpected, or rare weather. It could be defined from a climatological base, a forecast base, or a user specification. In general, it results in the loss of lives, property, equipment, etc. For example, the special report of the Intergovernmental Panel on Climate Change ( IPCC 2011 ) shows the annual losses from weather- and climate-related disasters since 1980 has ranged from a few billion U.S. dollars to more than $200 billion. Therefore

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Phillip E. Shafer and Henry E. Fuelberg

1. Introduction Cloud-to-ground (CG) lightning is one of the leading causes of weather-related fatalities in the United States ( Holle et al. 1999 ). In fact, Curran et al. (2000) showed that only river and flash floods ranked higher than lightning in terms of deaths. Aside from the loss of life, CG lightning damages trees, buildings, and utility lines, often leading to power outages and disruptions to communications. Improved forecasts of CG lightning would have many potential societal

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Michael K. Tippett, Anthony G. Barnston, and Shuhua Li

1. Introduction The purpose of this paper is to document the performance of a multimodel real-time ENSO forecast product over the period 2002–11. Since February of 2002, a number of groups have provided their ENSO forecasts to the International Research Institute for Climate and Society (IRI). Those forecasts are the basis for probabilistic ENSO category forecasts as well as an “ENSO prediction plume” like the one shown in Fig. 1 from February 2011. Here we limit our analysis to the ENSO

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Mohammad Zaved Kaiser Khan, Rajeshwar Mehrotra, Ashish Sharma, and A. Sankarasubramanian

1. Introduction Skilful climate predictions provide useful scientific information to planners and operational agencies to plan and develop contingency measures and strategies to deal with the adverse conditions. In this regard, seasonal to interannual (long lead) climate forecasts are issued on a regular basis by various national and international agencies using both coupled ocean–atmosphere general circulation models (CGCMs) ( Saha et al. 2006 ; Weisheimer et al. 2009 ; Palmer et al. 2004

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Mingyue Chen, Wanqiu Wang, and Arun Kumar

1. Introduction Weather prediction enterprise at various operational centers is a well-coordinated effort, with forecast systems initiated at the same time (0000 UTC, 0600 UTC, etc.). This standardization in the timing of the forecast system has facilitated an easy exchange of forecast data among operational centers. The standardization in operational practices for weather prediction has also led to the development of coordinated databases, such as The Observing System Research and

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C. Cattoën, D. E. Robertson, J. C. Bennett, Q. J. Wang, and T. K. Carey-Smith

1. Introduction Two trends have emerged in the development of new streamflow forecasting systems: (i) a shift from deterministic to ensemble streamflow predictions ( Alfieri et al. 2013 ; Cloke and Pappenberger 2009 ; Demargne et al. 2014 ; Thielen et al. 2009 ), and (ii) a move toward national/continental scale systems that attempt to describe hydrological fluxes for all reaches over a given domain ( Adams and Pagano 2016 ; Bell et al. 2017 ; Emerton et al. 2016 ; Maxey et al. 2012

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Michael K. Tippett, Laurie Trenary, Timothy DelSole, Kathleen Pegion, and Michelle L. L’Heureux

1. Introduction “Forecast climatologies” are used in weather and climate prediction to correct systematic model errors and to express forecasts as anomalies. A forecast climatology is the expected (average) forecast value for a specified start time, lead time, and target period. The calculation of a forecast climatology is similar in many ways to that of an observational climatology, except that a forecast climatology can depend on lead time as well as target period and is computed from

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Aaron J. Hill, Christopher C. Weiss, and Brian C. Ancell

initiation ( Doswell and Bosart 2001 ). Although the importance of drylines in severe storm development is fairly well understood, forecasting their position, intensity, and the severe thunderstorms forced by the boundary remains difficult. Errors, for example, in the precise location of the parent synoptic cyclone, the distribution of boundary layer moisture (e.g., Holt et al. 2006 ), the intensity of capping inversions, and the strength of vertical mixing processes are critical components that

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Veronica J. Berrocal, Adrian E. Raftery, and Tilmann Gneiting

1. Introduction Ensemble prediction systems have been developed to generate probabilistic forecasts of weather quantities that address the two major sources of forecast uncertainty in numerical weather prediction: uncertainty in initial conditions, and uncertainty in model formulation. Originally suggested by Epstein (1969) and Leith (1974) , ensemble forecasts have been operationally implemented on the synoptic scale ( Toth and Kalnay 1993 ; Houtekamer et al. 1996 ; Molteni et al. 1996

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Stephen Cusack and Alberto Arribas

1. Introduction Uncertainties in forecasts arise from errors in both the initialization and subsequent modeled evolution and are amplified by the chaotic nonlinear dynamics of the climate system (e.g., Lorenz 1963 , 1993 ). These sources of uncertainty are sampled by current ensemble prediction systems to produce a probability distribution function (pdf) of a predictand (e.g., Toth and Kalnay 1993 ; Palmer et al. 1993 ). Therefore, the forecast is probabilistic in nature, and a full

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