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Wahid Palash, Yudan Jiang, Ali S. Akanda, David L. Small, Amin Nozari, and Shafiqul Islam

Bangladesh share the top two positions on the list of flood-prone nations, with Pakistan coming in at fifth and Afghanistan at ninth ( WRI 2015 ). A forecasting lead time of ~5–10 days is desired to increase the flood response and preparedness ( ADPC 2002 ; Webster and Hoyos 2004 ; CEGIS 2006 ) in flood-prone regions across the world, including Bangladesh. The major limitation of providing short (3–5 days) to midrange (7–10 days) flood forecasting is mainly associated with large uncertainty in

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Kristofer Y. Shrestha, Peter J. Webster, and Violeta E. Toma

1. Introduction The unanticipated variability of monsoon rainfall in the current and future climate can have devastating consequences for the developing world, where societies are centered on agrarian pursuits ( Webster and Jian 2011 ). Ensembles of extended-range weather forecasts are now being used as forcing data for hydrological model simulations to determine the risk posed by potential weather extremes ( Cloke and Pappenberger 2009 ). For example, hydrological forecasts and flood warnings

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N. Wanders, A. Bachas, X. G. He, H. Huang, A. Koppa, Z. T. Mekonnen, B. R. Pagán, L. Q. Peng, N. Vergopolan, K. J. Wang, M. Xiao, S. Zhan, D. P. Lettenmaier, and E. F. Wood

/15 winter ( Mote et al. 2016 ), followed by the warmest summer of the instrumental record, which led to record wildfires ( U.S. Forest Service 2016 ). In the summer and autumn of 2015, one of the strongest El Niños on record began to evolve and was forecasted by the National Oceanic and Atmospheric Administration (NOAA) to intensify in the 2015/16 winter ( Klein 2015 ). Seasonal forecast models predicted an alleviation of the drought conditions that were generally consistent with the so-called canonical

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Nusrat Yussouf, Katie A. Wilson, Steven M. Martinaitis, Humberto Vergara, Pamela L. Heinselman, and Jonathan J. Gourley

1. Introduction The National Oceanic and Atmospheric Administration (NOAA) National Severe Storms Laboratory’s (NSSL) Warn-on-Forecast program (WoF; Stensrud et al. 2009 , 2013 ) is developing a frequently cycled, probabilistic, convective-scale, numerical weather prediction (NWP) model-based ensemble system, which is referred to as the Warn-on-Forecast System (WoFS). The vision of the WoF program is to fill the gap in forecasters’ current watch-to-warning paradigm for severe thunderstorm

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Ridwan Siddique and Alfonso Mejia

, reservoir operations, hydropower generation, and navigation), streamflow forecasts are essential ( Alfieri et al. 2014 ; Day 1985 ). Streamflow forecasts are often generated by a hydrological forecasting system forced by outputs from a numerical weather prediction (NWP) model, whereby the uncertainties in the meteorological outputs are propagated into the streamflow forecasts. To characterize and assess the uncertainty of hydrological forecasts, hydrological ensemble prediction systems (HEPS) are

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M. Verbunt, A. Walser, J. Gurtz, A. Montani, and C. Schär

1. Introduction The Alpine region is exposed to a high frequency of heavy precipitation events ( Frei et al. 2000 ). Due to the complex topography this region is particularly vulnerable to these events and its consequent effects like floods, landslides, and erosion, which endanger environment, inhabitants, and infrastructure. To mitigate the consequences of such events, it is therefore of utmost importance to produce reliable forecasts with sufficient lead time. Observed and anticipated changes

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Cara Tobin, Andrea Rinaldo, and Bettina Schaefli

, and freezing of hydrometeors, thereby acting as an influential factor in the prediction of the altitude where the transition from snow to rainfall occurs [i.e., snowfall limit (SL)]. Accordingly, the wet-bulb temperature is used in different precipitation phase models for forecast purposes ( Bourgouin 2000 ; Graham and Evans 2011 ). In contrast, computation of SLs within hydrological models is typically based on a spatial interpolation of dry ground temperatures with estimated lapse rates

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Lizhi Tao, Xinguang He, and Rui Wang

( Peng et al. 2014 ). This makes monthly and seasonal precipitation predictions particularly difficult. Consequently, it has been challenging to obtain accurate and reliable precipitation predictions ( Wang et al. 2014 ). In the past decades, various methods have been developed for forecasting precipitation. These approaches roughly fall into two categories: empirical and dynamical ( He et al. 2015 ). The dynamical models based on the laws of physics such as general circulation models (GCMs) have

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Di Tian, Christopher J. Martinez, and Wendy D. Graham

, solar radiation, and dewpoint temperature or relative humidity, which are often not available in many regions. Coupled ocean–land–atmosphere general circulation models (CGCMs) combine models for the ocean, atmosphere, land surface, and sea ice and run from several months to 1 year ahead to produce seasonal forecasts ( Troccoli 2010 ). CGCMs have been operationally implemented at major weather and climate forecast centers around the world ( Palmer et al. 2004 ; Saha et al. 2006 ; Yuan et al. 2011

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Natacha B. Bernier, Stéphane Bélair, Bernard Bilodeau, and Linying Tong

1. Introduction The 2010 Winter Olympic and Paralympic Games (VO2010) were held in Vancouver, Canada from 12 to 28 February 2010 and from 12 to 21 March 2010, respectively. In view of these Games, Environment Canada was asked to provide timely and accurate forecasts over the Games’ venue region ( Fig. 1 ). To fulfill these requirements, Environment Canada made use of existing operational and new experimental forecast products. An overview of the suite of VO2010 specially designed and

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