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David R. Novak, David R. Bright, and Michael J. Brennan

1. Introduction Uncertainty is a fundamental characteristic of hydrometeorological (hydrologic, weather, and seasonal climate) prediction, and is a consequence of the inherent chaotic nature of the atmosphere, inadequate observations, and numerical weather prediction (NWP) deficiencies ( NRC 2006 ). Thus, the assessment and communication of uncertainty is an inherent part of any forecast process. The assessment of uncertainty in modern operational forecasting has largely relied on the use of

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Thomas C. Pagano, Andrew W. Wood, Maria-Helena Ramos, Hannah L. Cloke, Florian Pappenberger, Martyn P. Clark, Michael Cranston, Dmitri Kavetski, Thibault Mathevet, Soroosh Sorooshian, and Jan S. Verkade

countries is staggering, with disasters routinely displacing from tens to hundreds of thousands of people; for example, nearly 2000 people were dead or missing after the Philippines typhoon of 2012, with evacuations exceeding 780 000 people. Droughts can be just as damaging, with the U.S. drought of 2012 costing nearly $80 billion (U.S. dollars). Some of these consequences are avoidable through advance warning, emergency response, and other preparations; thus, operational river forecasters can help

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Chermelle Engel and Elizabeth Ebert

techniques that try to predict the best “model of the day” ( Hibon and Evgeniou 2005 ; Fritsch et al. 2000 ). Combining multiple individual forecasts to increase accuracy is an approach used in various fields from business to psychology ( Clemen 1989 ). The operational consensus forecast (OCF) scheme developed at the Bureau combines multimodel guidance. Daily OCF has been shown to produce objective guidance for forecast fields such as maximum and minimum daily air temperatures that is competitive with

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Amir Givati, Barry Lynn, Yubao Liu, and Alon Rimmer

Kinneret play a crucial role in Israeli agricultural and hydrological planning and in flood control. Hydrological forecasts are instrumental for decision-support activities at the Israel Water Authority. Major operational weather forecast centers provide relatively coarse (~16–25-km grid increment) precipitation analyses and forecasts, which are incapable of resolving the necessary details of the complex precipitation structures that are forced by mesoscale orography, land surface heterogeneities, and

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Ralph F. Milliff and Peter A. Stamus

1. Introduction The research value of satellite ocean surface vector wind (SVW) data has been well established over more than a decade of published work, across a broad range of studies. What is also emerging is that these data play essential roles in many operational applications as well, including (i) numerical weather prediction (NWP) and (ii) the manual production of forecasts, analyses, and warnings by trained analysts (i.e., forecasters). SVW impacts in NWP have been highlighted recently

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Jose-Henrique G. M. Alves, Arun Chawla, Hendrik L. Tolman, David Schwab, Gregory Lang, and Greg Mann

. The current paper describes the challenges faced, and solutions adopted by the wave modeling group at the Environmental Modeling Center of NOAA’s National Centers for Environmental Prediction (NCEP), which led to the successful deployment of a Great Lakes wave forecasting system using the third-generation model WAVEWATCH III ( Tolman 2002b ; Tolman et al. 2002 ). Performance of the current operational wave forecasting system for the Great Lakes, and the impacts of scheduled upgrades that will

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Namyoung Kang

as the probability that a storm will fall somewhere within a particular distance close enough to a location (i.e., 140 km) ( Jarrell and Brand 1983 ). This is why the potential area of the storm wind occurrence is more useful information than track uncertainty itself since a track is only the center of danger. Successful representation of the forecast uncertainty has been available in operations, using Monte Carlo sampling from past operational forecasts ( DeMaria et al. 2009 ). Owing to the

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Keqi Zhang, Yuepeng Li, Huiqing Liu, Jamie Rhome, and Cristina Forbes

continuity equation by maintaining nonlinear advective acceleration and diffusion terms. These models and their extensions also include the wetting–drying component and have recently been applied to the simulation of overland flooding ( Bunya et al. 2010 ; Forbes et al. 2009 ; Forbes et al. 2010 ; Huang et al. 2010 ; Shen et al. 2006 ; Sheng et al. 2010 ; Xie et al. 2004 ; Xu et al. 2010 ). However, these models cannot be used directly for operational surge forecasts because most of them are

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Natacha B. Bernier, Jose-Henrique G. M. Alves, Hendrik Tolman, Arun Chawla, Syd Peel, Benoit Pouliot, Jean-Marc Bélanger, Pierre Pellerin, Mario Lépine, and Michel Roch

continues to receive considerable attention from both the research and operational communities (e.g., Tolman et al. 2013 ). Several countries have already developed and implemented global wave forecast systems—for example, the European Center for Medium-Range Weather Forecasts [ Bidlot (2012) and references therein], the Met Office ( Li and Saulter 2014 ), the U.S. Navy ( Rogers et al. 2014 ), and the National Centers for Environmental Prediction ( Chawla et al. 2013 ), and additional centers are

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Francesco Silvestro, Nicola Rebora, and Luca Ferraris

atmospheric observations. The second is that, in order to cope with operational procedures and decision-making responsibility, hydrologists are allowed, in certain cases, to use “certified” predictions from (human) expert forecasters. These forecasters, on the basis of their knowledge, can analyze different meteorological models and estimate their reliability in different synoptic conditions and different local meteorological situations. This results in issuing a QPF that synthesizes a large quantity of

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