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Simon Schick, Ole Rössler, and Rolf Weingartner

1. Introduction Subseasonal and seasonal forecasts of environmental conditions are increasingly based on numerically coupled models of the various Earth system components. These include general circulation models of the atmosphere and oceans and dynamical land surface or sea ice models ( National Academies 2016 ). Such forecast systems represent diverse physical, chemical, and biological processes and continuously progress toward Earth system models (ESMs). However, not all environmental

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Yue Zheng, Kiran Alapaty, Jerold A. Herwehe, Anthony D. Del Genio, and Dev Niyogi

1. Introduction Numerical weather prediction (NWP) forecast models have been greatly improved, motivated by the role of providing accurate forecasts about severe weather events to mitigate the loss of life and property. Furthermore, credibility of climate change simulations at urban scales can be increased by first improving the accuracy of high-resolution model simulations at weather prediction time scales ( Chen et al. 2011 ). In particular, moist processes play an important role in properly

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Felix Fundel, Andre Walser, Mark A. Liniger, Christoph Frei, and Christof Appenzeller

, those techniques usually compare a set of past model forecasts with observations in order to identify systematic relationships that can be used to correct the current forecast operationally. The successful application of various ensemble calibration techniques has been shown in a comparison study by Wilks (2006a) as well as Wilks and Hamill (2007) . However, those techniques are not designed to calibrate rare events, especially rare precipitation events, as the sample size of the training

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Marie Boisserie, Laurent Descamps, and Philippe Arbogast

1. Introduction Improving the early detection of extreme weather events is of primary importance to national meteorological services and would greatly help community responders and government stakeholders to take appropriate actions to mitigate damage to property and prevent fatal casualties caused by these devastating events. However, such forecasts are not yet routine and more research is needed. Although numerical weather prediction (NWP) systems have been considerably improved in the past

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Leonard A. Smith, Hailiang Du, and Sarah Higgins

1. Introduction Forecasters are often faced with an ensemble of model simulations that are to be incorporated into quantitative forecast system and presented as a probabilistic forecast. Indeed, ensembles of initial conditions have been operational in weather centers in both the United States ( Kirtman et al. 2014 ) and Europe ( Palmer et al. 2004 ; Weisheimer et al. 2009 ) since the early 1990s, and there is a significant literature on their interpretation ( Raftery et al. 2005 ; Hoeting et

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Chermelle Engel and Elizabeth Ebert

1. Introduction Forecasters in Australian regional weather forecasting centers have a wealth of numerical model guidance available to them, including regional models produced in the Australian Bureau of Meteorology (the Bureau) and a subset of the guidance from global models produced in other international centers. When guidance is available from a number of different models, consensus-forecasting techniques, which combine the models, have been found to be more accurate on the average than

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Edwin Welles and Soroosh Sorooshian

1. Introduction Recently, Welles et al. (2007) evaluated National Weather Service (NWS) river stage forecasts. They found the forecast skill may not have improved as much as expected because, as they suggested, forecast system updates were not driven by objective measures of forecast skill. Many people have studied elements of the forecast process—calibration, state updating, and precipitation forecasts—but the forecast process itself with the various elements linked together has not been

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Anne C. Steinemann

1. Introduction Improvements in climate forecasts have created the potential to improve seasonal to interannual water resources management. This potential remains largely untapped, however, because forecasts are not frequently used in actual decision making. Barriers to forecast use, as noted in prior studies, include user difficulties in understanding, applying, evaluating, and trusting the forecasts ( Hartmann et al. 2002 ; Pulwarty and Redmond 1997 ; Schneider and Garbrecht 2003

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Timothy DelSole, Liwei Jia, and Michael K. Tippett

1. Introduction Several operational forecast centers issue predictions of weather and climate. The availability of multiple forecasts from different institutions raises the question of whether the forecasts can be combined to increase skill and reliability. Although several multimodel prediction systems have been proposed in the past, the relatively short datasets available for calibration lead to serious problems with overfitting. Consequently, a variety of approaches have been proposed to

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Nathalie Voisin, John C. Schaake, and Dennis P. Lettenmaier

1. Introduction The ability to forecast large river floods globally could have substantial economic and humanitarian benefits. Sophisticated flood forecast systems in the developed world have been credited with reducing the loss of life over recent decades [notwithstanding increases in flood losses, which are largely attributable to increased development in flood plains; Pielke et al. (2002) ]. However, the benefits of these systems do not extend to the developing world, where in situ

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