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require that forecast and observed events match at the grid scale for a forecast to be considered perfect, have not always corroborated subjective evaluations favoring convection-allowing models over convection-parameterizing models (e.g., Mass et al. 2002 ; Weisman et al. 2008 ). Thus, in an attempt to reconcile disparities between objective metrics and subjective evaluations, several spatial verification methods have been developed that can broadly be categorized into “neighborhood,” scale
require that forecast and observed events match at the grid scale for a forecast to be considered perfect, have not always corroborated subjective evaluations favoring convection-allowing models over convection-parameterizing models (e.g., Mass et al. 2002 ; Weisman et al. 2008 ). Thus, in an attempt to reconcile disparities between objective metrics and subjective evaluations, several spatial verification methods have been developed that can broadly be categorized into “neighborhood,” scale
system ( Bassill 2014 ; Magnusson et al. 2014 ; Torn et al. 2015 ). Sandy tested existing infrastructure for hazard communication ( NOAA 2013 ; Blake et al. 2013 ) and posed challenges related to risk perception ( Meyer et al. 2014 ) due to its atypical track and forecast structure ( Munsell and Zhang 2014 ) near landfall. Few TCs produce such a broad range of impacts, but Sandy was not ordinary. Rather, Sandy is a dramatic example of the direct impacts, structural evolution, and forecast
system ( Bassill 2014 ; Magnusson et al. 2014 ; Torn et al. 2015 ). Sandy tested existing infrastructure for hazard communication ( NOAA 2013 ; Blake et al. 2013 ) and posed challenges related to risk perception ( Meyer et al. 2014 ) due to its atypical track and forecast structure ( Munsell and Zhang 2014 ) near landfall. Few TCs produce such a broad range of impacts, but Sandy was not ordinary. Rather, Sandy is a dramatic example of the direct impacts, structural evolution, and forecast
transitioning cyclone (encircled during extratropical stage) and the “L” the position of the downstream cyclone. Together with Part I , this review describes developments in our understanding of ET since the first ET review by Jones et al. (2003 , hereafter J2003 ). The review by J2003 was motivated by the challenges that ET typically poses to forecasters in terms of predicting the structural evolution of the transitioning cyclone itself, and the high-impact weather that might be associated with it
transitioning cyclone (encircled during extratropical stage) and the “L” the position of the downstream cyclone. Together with Part I , this review describes developments in our understanding of ET since the first ET review by Jones et al. (2003 , hereafter J2003 ). The review by J2003 was motivated by the challenges that ET typically poses to forecasters in terms of predicting the structural evolution of the transitioning cyclone itself, and the high-impact weather that might be associated with it
heat in a resting ambient atmosphere yields a net descent in the inner core of the initially warmed region with ascent above and below ( Fanelli and Bannon 2005 ). This contrast in the heating–ascent relationship is relevant in an NWP context. Longstanding challenges in NWP are to deliver reliable quantitative forecasting of precipitation, and to develop robust and effective techniques to assimilate/adjust model fields to account for misrepresented or unrepresented cloud diabatic heating. The
heat in a resting ambient atmosphere yields a net descent in the inner core of the initially warmed region with ascent above and below ( Fanelli and Bannon 2005 ). This contrast in the heating–ascent relationship is relevant in an NWP context. Longstanding challenges in NWP are to deliver reliable quantitative forecasting of precipitation, and to develop robust and effective techniques to assimilate/adjust model fields to account for misrepresented or unrepresented cloud diabatic heating. The
1. Introduction The accuracy of numerical weather prediction (NWP) depends critically on the qualities of the initial conditions and the forecast model. The initial conditions of an NWP model usually come from data assimilation, a procedure that aims to estimate the state and uncertainty of the atmosphere as accurately as possible by combining all available information (including both model forecasts and observations, and their respective uncertainties). In the data assimilation community, the
1. Introduction The accuracy of numerical weather prediction (NWP) depends critically on the qualities of the initial conditions and the forecast model. The initial conditions of an NWP model usually come from data assimilation, a procedure that aims to estimate the state and uncertainty of the atmosphere as accurately as possible by combining all available information (including both model forecasts and observations, and their respective uncertainties). In the data assimilation community, the
measurements. As a result IHOP_2002 had four complementary components: (i) Quantitative precipitation forecasting (QPF), which aimed to determine the relative improvement in warm-season QPF skill from these enhanced moisture measurements. Warm-season convective rainfall, coupled with very low QPF skill (e.g., Uccellini et al. 1999 ; Fritsch and Carbone 2004 ), dramatically affects society in terms of flash floods, agriculture, transportation, and severe storm prediction. (ii) Convection initiation (CI
measurements. As a result IHOP_2002 had four complementary components: (i) Quantitative precipitation forecasting (QPF), which aimed to determine the relative improvement in warm-season QPF skill from these enhanced moisture measurements. Warm-season convective rainfall, coupled with very low QPF skill (e.g., Uccellini et al. 1999 ; Fritsch and Carbone 2004 ), dramatically affects society in terms of flash floods, agriculture, transportation, and severe storm prediction. (ii) Convection initiation (CI
by earlier authors, but often are understated, misinterpreted, or neglected by later researchers and forecasters who rely on CSI as an explanation for banded precipitation. As the concept of CSI has grown in popularity and usage over time ( Fig. 1 ), these qualifications are often omitted as second-generation references are cited at an increasing rate, instead of the older, but perhaps more correct, references. 2) With the advent of satellite imagery and Doppler radar with higher resolution than
by earlier authors, but often are understated, misinterpreted, or neglected by later researchers and forecasters who rely on CSI as an explanation for banded precipitation. As the concept of CSI has grown in popularity and usage over time ( Fig. 1 ), these qualifications are often omitted as second-generation references are cited at an increasing rate, instead of the older, but perhaps more correct, references. 2) With the advent of satellite imagery and Doppler radar with higher resolution than
to surface weather that falls into the tail(s) of the respective local distribution (e.g., precipitation exceeding the 95th percentile). To the extent that weather events inherit predictability from larger-scale dynamical features such as RWPs ( Anthes et al. 1985 ), a better understanding of the RWPs may help to improve the weather forecast, and this is particularly relevant in case of extreme weather. An example is the episode in August 2002, when a quasi-stationary low pressure system over
to surface weather that falls into the tail(s) of the respective local distribution (e.g., precipitation exceeding the 95th percentile). To the extent that weather events inherit predictability from larger-scale dynamical features such as RWPs ( Anthes et al. 1985 ), a better understanding of the RWPs may help to improve the weather forecast, and this is particularly relevant in case of extreme weather. An example is the episode in August 2002, when a quasi-stationary low pressure system over
1. Introduction Usual data assimilation systems for numerical weather prediction (NWP), using Kalman filter or variational techniques, are based on a statistical combination of observations and a background, which is usually a short-term forecast. This statistical estimation requires the specification of spatial covariances of errors in these two kinds of information. As presented in Hollingsworth (1987) and Daley (1991 , p. 125), the role of background error covariances is to spatially
1. Introduction Usual data assimilation systems for numerical weather prediction (NWP), using Kalman filter or variational techniques, are based on a statistical combination of observations and a background, which is usually a short-term forecast. This statistical estimation requires the specification of spatial covariances of errors in these two kinds of information. As presented in Hollingsworth (1987) and Daley (1991 , p. 125), the role of background error covariances is to spatially
reveal many fronts possessing a variety of nonclassical structures that require explanation. Understanding the processes that control the structure and evolution of fronts is essential for the accuracy of weather forecasts for several reasons. First, Sanders (1967 , 1983 , 1999a ) has argued that the relationship between the wind shift and temperature gradient determines the future strength of the cold front. Cold fronts in which the wind shifts are coincident with the temperature gradient imply
reveal many fronts possessing a variety of nonclassical structures that require explanation. Understanding the processes that control the structure and evolution of fronts is essential for the accuracy of weather forecasts for several reasons. First, Sanders (1967 , 1983 , 1999a ) has argued that the relationship between the wind shift and temperature gradient determines the future strength of the cold front. Cold fronts in which the wind shifts are coincident with the temperature gradient imply
