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-scale turbulent eddies. They also find, however, that resolving the bulk properties of a convective cloud may be possible with 1-km grid spacing. Similarly, Weisman et al. (1997) found that 4 km may be sufficient to represent the system-scale properties of midlatitude squall-line-type convection. To achieve a balance between ensemble size and the fidelity of the simulated storms, the model grid spacing used herein is Δ x = 1 km and Δ z = 500 m (the vertical grid spacing is 250 m in the lowest 1250 m and
-scale turbulent eddies. They also find, however, that resolving the bulk properties of a convective cloud may be possible with 1-km grid spacing. Similarly, Weisman et al. (1997) found that 4 km may be sufficient to represent the system-scale properties of midlatitude squall-line-type convection. To achieve a balance between ensemble size and the fidelity of the simulated storms, the model grid spacing used herein is Δ x = 1 km and Δ z = 500 m (the vertical grid spacing is 250 m in the lowest 1250 m and
indicative of the orogenic 1 nature of storms in these locations. Supercell thunderstorm formation and mesoscale storm structure over the Great Plains of the United States have been extensively studied. For example, Carlson et al. (1983) documented the conditions associated with supercell development, and Houze et al. (1990) showed the tendency for storms over the Great Plains to develop into mesoscale convective systems with leading lines of intense convection and trailing-stratiform precipitation
indicative of the orogenic 1 nature of storms in these locations. Supercell thunderstorm formation and mesoscale storm structure over the Great Plains of the United States have been extensively studied. For example, Carlson et al. (1983) documented the conditions associated with supercell development, and Houze et al. (1990) showed the tendency for storms over the Great Plains to develop into mesoscale convective systems with leading lines of intense convection and trailing-stratiform precipitation
1. Introduction A quasi-linear convective system (QLCS) is a type of organized convective system that produces torrential rain, straight-line winds, tornadoes, and hail. In the United States, ∼18% of tornadoes are spawned by QLCSs ( Trapp et al. 2005 ). It is noted that about half of the tornadoes in the morning between 0600 and 1200 LST are associated with QLCSs ( Ashley et al. 2019 ), although a considerable fraction of tornadoes in QLCSs are EF0 or EF1 scale. The QLCS tornadoes tend to
1. Introduction A quasi-linear convective system (QLCS) is a type of organized convective system that produces torrential rain, straight-line winds, tornadoes, and hail. In the United States, ∼18% of tornadoes are spawned by QLCSs ( Trapp et al. 2005 ). It is noted that about half of the tornadoes in the morning between 0600 and 1200 LST are associated with QLCSs ( Ashley et al. 2019 ), although a considerable fraction of tornadoes in QLCSs are EF0 or EF1 scale. The QLCS tornadoes tend to
numerically simulated nocturnal squall lines. Mon. Wea. Rev. , 134 , 3735 – 3752 . Fritsch , J. M. , and G. S. Forbes , 2001 : Mesoscale convective systems. Severe Convective Storms, Meteor. Monogr., No. 50, Amer. Meteor. Soc., 323–357 . Gallus , W. A. , and R. H. Johnson , 1992 : The momentum budget of an intense midlatitude squall line. J. Atmos. Sci. , 49 , 422 – 450 . Gallus , W. A. , and M. Pfeifer , 2008 : Intercomparison of simulations using 5 WRF microphysical schemes
numerically simulated nocturnal squall lines. Mon. Wea. Rev. , 134 , 3735 – 3752 . Fritsch , J. M. , and G. S. Forbes , 2001 : Mesoscale convective systems. Severe Convective Storms, Meteor. Monogr., No. 50, Amer. Meteor. Soc., 323–357 . Gallus , W. A. , and R. H. Johnson , 1992 : The momentum budget of an intense midlatitude squall line. J. Atmos. Sci. , 49 , 422 – 450 . Gallus , W. A. , and M. Pfeifer , 2008 : Intercomparison of simulations using 5 WRF microphysical schemes
temperature, and equivalent potential temperature associated with the cold pool, and the maximum wind speeds in the cold pool). A total of 47 stations are associated with the first storms life cycle stage, 156 with the MCS initiation stage, 948 with the mature MCS stage, and 238 with the MCS dissipation stage. Since idealized simulations of convective systems often use environmental thermodynamic profiles typical of the late afternoon and early evening, the 200 station time series that had outflow
temperature, and equivalent potential temperature associated with the cold pool, and the maximum wind speeds in the cold pool). A total of 47 stations are associated with the first storms life cycle stage, 156 with the MCS initiation stage, 948 with the mature MCS stage, and 238 with the MCS dissipation stage. Since idealized simulations of convective systems often use environmental thermodynamic profiles typical of the late afternoon and early evening, the 200 station time series that had outflow
northwest of the S-Pol radar, allowing for coordinated profiler/S-Pol radar observations. The locations of both the S-Pol radar and NOAA field site are shown in Fig. 1 . Since both instrument sites were situated on the coastal plain, unobstructed views over the profiler by the S-Pol radar could be obtained. One particular focus of NAME was documenting the detailed physical structure and kinematics of mesoscale convective systems (MCSs; Higgins et al. 2006 ). Lang et al. (2007) , in performing a
northwest of the S-Pol radar, allowing for coordinated profiler/S-Pol radar observations. The locations of both the S-Pol radar and NOAA field site are shown in Fig. 1 . Since both instrument sites were situated on the coastal plain, unobstructed views over the profiler by the S-Pol radar could be obtained. One particular focus of NAME was documenting the detailed physical structure and kinematics of mesoscale convective systems (MCSs; Higgins et al. 2006 ). Lang et al. (2007) , in performing a
explicitly model when events occurred. An event simulation meant that a storm passed over the study/simulation window, but without a specific time or date assigned to the event. As rainfall event occurrence is a major characteristic of a given rainfall regime, there is a need to simulate it in the rainfall generator. b. Event-based rain fields An event-based rain field is defined here as the cumulative rainfall left by the passing of convective rainfall systems over the study site. The stochastic
explicitly model when events occurred. An event simulation meant that a storm passed over the study/simulation window, but without a specific time or date assigned to the event. As rainfall event occurrence is a major characteristic of a given rainfall regime, there is a need to simulate it in the rainfall generator. b. Event-based rain fields An event-based rain field is defined here as the cumulative rainfall left by the passing of convective rainfall systems over the study site. The stochastic
mountain-initiated episodes (0300–0500 UTC). During the late night hours and through morning (0600–1400 UTC), the dominant precipitation type was from merged episodes as mountain- and plains-initiated systems combined, grew upscale and precipitated over the Great Plains. The largest 10% rain-producing storms produced 91% of the PECAN precipitation, which underlines the importance of investigations into the factors that lead to maintenance and upscale growth of convection. Analysis of these largest
mountain-initiated episodes (0300–0500 UTC). During the late night hours and through morning (0600–1400 UTC), the dominant precipitation type was from merged episodes as mountain- and plains-initiated systems combined, grew upscale and precipitated over the Great Plains. The largest 10% rain-producing storms produced 91% of the PECAN precipitation, which underlines the importance of investigations into the factors that lead to maintenance and upscale growth of convection. Analysis of these largest
( Feng et al. 2019 ). FLEXTRKR classifies the tracked system into convective, stratiform, and anvil components using the storm labeling in three dimensions (SL3D; Starzec et al. 2017 ) algorithm. FLEXTRKR also provides many statistical properties including the cloud and PF areas, mean precipitation rate, major axis length, eccentricity, and duration. These properties, as well as the convective and stratiform properties of each tracked MCS, are used as input variables for training the random
( Feng et al. 2019 ). FLEXTRKR classifies the tracked system into convective, stratiform, and anvil components using the storm labeling in three dimensions (SL3D; Starzec et al. 2017 ) algorithm. FLEXTRKR also provides many statistical properties including the cloud and PF areas, mean precipitation rate, major axis length, eccentricity, and duration. These properties, as well as the convective and stratiform properties of each tracked MCS, are used as input variables for training the random
The Impact of Airborne Doppler Aerosol Wind (DAWN) Lidar Wind Profiles on Numerical Simulations of Tropical Convective Systems during the NASA Convective Processes Experiment (CPEX)) project ( Geerts et al. 2017 ). However, the Gulf of Mexico, which also has very active MCSs and has a significant influence on severe weather systems over the U.S. coastal region, has not yet been well studied due to a lack of observations, although the region has occasionally been sampled during the hurricane season. To understand convective storm initiation, evolution, and dissipation over the Gulf of Mexico, the NASA Convective Processes Experiment (CPEX) aircraft field campaign took place in the
) project ( Geerts et al. 2017 ). However, the Gulf of Mexico, which also has very active MCSs and has a significant influence on severe weather systems over the U.S. coastal region, has not yet been well studied due to a lack of observations, although the region has occasionally been sampled during the hurricane season. To understand convective storm initiation, evolution, and dissipation over the Gulf of Mexico, the NASA Convective Processes Experiment (CPEX) aircraft field campaign took place in the
