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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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Zied Ben Bouallègue
,
Mariana C. A. Clare
,
Linus Magnusson
,
Estibaliz Gascón
,
Michael Maier-Gerber
,
Martin Janoušek
,
Mark Rodwell
,
Florian Pinault
,
Jesper S. Dramsch
,
Simon T. K. Lang
,
Baudouin Raoult
,
Florence Rabier
,
Matthieu Chevallier
,
Irina Sandu
,
Peter Dueben
,
Matthew Chantry
, and
Florian Pappenberger

Forecasting System (IFS) has been run operationally since 1979 with regular updates of the different components of the forecasting system. The evolution of the IFS accuracy over the last two decades is shown in Fig. 1 (red lines). The steady increase in forecast accuracy, thanks to incremental improvements in numerical modeling, supercomputing, data assimilation and ensemble techniques, observations, and their use in the NWP system, has become known as the “quiet revolution” of weather forecasting

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Thomas C. Pagano
,
Barbara Casati
,
Stephanie Landman
,
Nicholas Loveday
,
Robert Taggart
,
Elizabeth E. Ebert
,
Mohammadreza Khanarmuei
,
Tara L. Jensen
,
Marion Mittermaier
,
Helen Roberts
,
Steve Willington
,
Nigel Roberts
,
Mike Sowko
,
Gordon Strassberg
,
Charles Kluepfel
,
Timothy A. Bullock
,
David D. Turner
,
Florian Pappenberger
,
Neal Osborne
, and
Chris Noble

their daily lives and are also essential for various industries, emergency management communities, and governments ( World Meteorological Organization 2015 ). Fig. 1. Operational meteorologists from the South African Weather Service reviewing recent weather conditions. Nevertheless, operational agencies face growing pressure to demonstrate value and accountability, particularly in the aftermath of extreme weather, shifting funding priorities, and competition from other forecast

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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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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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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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