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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
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
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
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
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
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
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
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
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
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
. 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
. 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
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
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
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
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
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
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
1. Introduction As computational power has increased, limited-area operational numerical weather prediction (NWP) models with horizontal grid spacing of 1–10 km for short-range weather forecasting are introduced in many operational forecast centers. At such scales, many models have typically been configured without using a convection parameterization scheme (CPS). However, such a configuration generally leads to subgrid convective updrafts being forced on the grid scale and precipitation with
1. Introduction As computational power has increased, limited-area operational numerical weather prediction (NWP) models with horizontal grid spacing of 1–10 km for short-range weather forecasting are introduced in many operational forecast centers. At such scales, many models have typically been configured without using a convection parameterization scheme (CPS). However, such a configuration generally leads to subgrid convective updrafts being forced on the grid scale and precipitation with
