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1. Introduction The pioneering numerical modeling studies of Weisman and Klemp (1982 , hereafter WK82) have helped guide convective storm research for nearly three decades. Their work studied storm behavior in an expansive array of convective available potential energy (CAPE) and bulk shear values ( Fig. 1 ), showing dramatic increases in storm updraft velocity as CAPE increased from roughly 1000 to 2000 J kg −1 , with persistent updrafts at all values of CAPE above 2000 J kg −1 (given
1. Introduction The pioneering numerical modeling studies of Weisman and Klemp (1982 , hereafter WK82) have helped guide convective storm research for nearly three decades. Their work studied storm behavior in an expansive array of convective available potential energy (CAPE) and bulk shear values ( Fig. 1 ), showing dramatic increases in storm updraft velocity as CAPE increased from roughly 1000 to 2000 J kg −1 , with persistent updrafts at all values of CAPE above 2000 J kg −1 (given
1. Introduction The objective of this study is to investigate the causes of convective storm initiation and the subsequent evolution during the wet season in the southwest Amazon region. The focus is on a day when large-scale convective storm forcing mechanisms were at a minimum. While the focus of this study is on just 1 day, it is likely representative of many days over the Amazon and other similar tropical areas. Since synoptic-scale fronts are relatively rare in such tropical regions local
1. Introduction The objective of this study is to investigate the causes of convective storm initiation and the subsequent evolution during the wet season in the southwest Amazon region. The focus is on a day when large-scale convective storm forcing mechanisms were at a minimum. While the focus of this study is on just 1 day, it is likely representative of many days over the Amazon and other similar tropical areas. Since synoptic-scale fronts are relatively rare in such tropical regions local
1. Introduction Deep moist convection can be either surface based or elevated. Surface-based convection occurs when near-surface air is the most unstable air parcel in the column. Elevated convection occurs above a near-surface stable layer, such that the most-unstable parcel is above the surface. Recent research has shown that elevated convective storms may be relatively common. For example, Wilson and Roberts (2006) found that half of the convective storms during the International H 2 O
1. Introduction Deep moist convection can be either surface based or elevated. Surface-based convection occurs when near-surface air is the most unstable air parcel in the column. Elevated convection occurs above a near-surface stable layer, such that the most-unstable parcel is above the surface. Recent research has shown that elevated convective storms may be relatively common. For example, Wilson and Roberts (2006) found that half of the convective storms during the International H 2 O
1. Introduction The motion of a convective storm is one of its most readily observed characteristics. Other common descriptors such as updraft velocity or vertical vorticity require more complex observations and calculations (e.g., retrieval techniques such as in Shapiro et al. 2003 ). Driven partly by features of the ambient atmospheric profile, storm motion is intimately related to the intensity of the updraft and the rotation (if present) within a storm. Cotton and Anthes (1989 , p. 497
1. Introduction The motion of a convective storm is one of its most readily observed characteristics. Other common descriptors such as updraft velocity or vertical vorticity require more complex observations and calculations (e.g., retrieval techniques such as in Shapiro et al. 2003 ). Driven partly by features of the ambient atmospheric profile, storm motion is intimately related to the intensity of the updraft and the rotation (if present) within a storm. Cotton and Anthes (1989 , p. 497
An Iterative Storm Segmentation and Classification Algorithm for Convection-Allowing Models and Gridded Radar Analyses1. Introduction a. Motivation for storm classification algorithms Storm mode features prominently in many thunderstorm research and forecast applications. The primary processes driving storm behavior can often be confidently inferred from the storm morphology manifested in radar and satellite imagery, or visually. For example, the propagation and strength of a quasi-linear convective system (QLCS) is typically governed largely by a systemwide cold pool (e.g., Weisman and Rotunno 2004
1. Introduction a. Motivation for storm classification algorithms Storm mode features prominently in many thunderstorm research and forecast applications. The primary processes driving storm behavior can often be confidently inferred from the storm morphology manifested in radar and satellite imagery, or visually. For example, the propagation and strength of a quasi-linear convective system (QLCS) is typically governed largely by a systemwide cold pool (e.g., Weisman and Rotunno 2004
1. Introduction Even though there have been many advances in the understanding and technology required to predict and mitigate the effects of convective storms, there are still many lives lost each year due to these events. Of the four most common thunderstorm hazards (wind, hail, flooding, and lightning), convective winds (tornadic and nontornadic) remain the most dangerous threat to life and property. Convective winds are responsible for an average of 84 fatalities per year in the United
1. Introduction Even though there have been many advances in the understanding and technology required to predict and mitigate the effects of convective storms, there are still many lives lost each year due to these events. Of the four most common thunderstorm hazards (wind, hail, flooding, and lightning), convective winds (tornadic and nontornadic) remain the most dangerous threat to life and property. Convective winds are responsible for an average of 84 fatalities per year in the United
severe-hail environment and convective mode of storms generally do not exist. The only related studies are by Roine (2001) , who studied stability indices in thunderstorm environments in southern Finland during May–August for two years (1998 and 2000) and by Punkka and Bister (2015) , who studied the synoptic and thermodynamic environment of MCSs in Finland covering eight warm seasons (April–September for 2000–07). In contrast to Finland, many more studies have been performed on severe- or
severe-hail environment and convective mode of storms generally do not exist. The only related studies are by Roine (2001) , who studied stability indices in thunderstorm environments in southern Finland during May–August for two years (1998 and 2000) and by Punkka and Bister (2015) , who studied the synoptic and thermodynamic environment of MCSs in Finland covering eight warm seasons (April–September for 2000–07). In contrast to Finland, many more studies have been performed on severe- or
1. Introduction The prediction of the initiation and development of convective storms represents a great challenge in the atmospheric sciences. The Lagrangian advection of radar echoes (called radar nowcasting) does not account for initiation, development, and dissipation. Forecasts using numerical weather prediction models are problematic because conventional synoptic-scale observations contain little information on the mesoscale. The initial conditions at the convective scale are therefore
1. Introduction The prediction of the initiation and development of convective storms represents a great challenge in the atmospheric sciences. The Lagrangian advection of radar echoes (called radar nowcasting) does not account for initiation, development, and dissipation. Forecasts using numerical weather prediction models are problematic because conventional synoptic-scale observations contain little information on the mesoscale. The initial conditions at the convective scale are therefore
1. Introduction a. Background Multidecadal changes in convective storms and the associated physical mechanisms are poorly understood relative to long-term trends in other extreme meteorological phenomena, such as hurricanes and heat waves ( Kunkel et al. 2013 ). Additionally, although thunderstorms occur more frequently in the midwestern and southern United States than in the northeastern United States (NEUS), convection has a substantial impact on life and property in the NEUS due to the
1. Introduction a. Background Multidecadal changes in convective storms and the associated physical mechanisms are poorly understood relative to long-term trends in other extreme meteorological phenomena, such as hurricanes and heat waves ( Kunkel et al. 2013 ). Additionally, although thunderstorms occur more frequently in the midwestern and southern United States than in the northeastern United States (NEUS), convection has a substantial impact on life and property in the NEUS due to the
1. Introduction The goal of this paper is to provide a comprehensive statistical analysis of convective storms in and around Belgium. The area of interest is characterized by a temperate climate, a nearby coastline, and intermediate level of orography (maximum 694 m). Convective storms are common meteorological phenomena that involve complex processes at different temporal and spatial scales. Severe events cause flash floods, strong winds, hail falls, and tornadoes, which can significantly
1. Introduction The goal of this paper is to provide a comprehensive statistical analysis of convective storms in and around Belgium. The area of interest is characterized by a temperate climate, a nearby coastline, and intermediate level of orography (maximum 694 m). Convective storms are common meteorological phenomena that involve complex processes at different temporal and spatial scales. Severe events cause flash floods, strong winds, hail falls, and tornadoes, which can significantly
