Search Results
You are looking at 1 - 10 of 24 items for
- Author or Editor: Andrew R. Dean x
- Refine by Access: All Content x
Abstract
In this study, both linear regression and a nonlinear neural network are used to forecast burnoff of low clouds in the warm season at San Francisco International Airport (SFO). Both forecast systems show skill scores between 0.2 and 0.25 in comparison with use of climatological values. The neural network is slightly more skillful. The forecast systems are derived from 45 yr of NCEP–NCAR reanalysis data and SFO surface observations. A forecast is attempted for both the time of burnoff and the probability of being burned off by 1000 Pacific standard time. The lack of significant superiority of the neural network over linear regression is not due to a failing of the neural network as a method. When both methods are applied to a statistical prediction of the afternoon temperature at SFO, based on early morning conditions, the neural network has a skill score of 0.446 and the linear regression has a skill score of 0.290.
Abstract
In this study, both linear regression and a nonlinear neural network are used to forecast burnoff of low clouds in the warm season at San Francisco International Airport (SFO). Both forecast systems show skill scores between 0.2 and 0.25 in comparison with use of climatological values. The neural network is slightly more skillful. The forecast systems are derived from 45 yr of NCEP–NCAR reanalysis data and SFO surface observations. A forecast is attempted for both the time of burnoff and the probability of being burned off by 1000 Pacific standard time. The lack of significant superiority of the neural network over linear regression is not due to a failing of the neural network as a method. When both methods are applied to a statistical prediction of the afternoon temperature at SFO, based on early morning conditions, the neural network has a skill score of 0.446 and the linear regression has a skill score of 0.290.
Abstract
Recent literature has identified several supercell/tornado forecast parameters in common use that are operationally beneficial in assessing environments supportive of supercell tornadoes. These parameters are utilized in the computation of tornado forecast guidance such as the significant tornado parameter (STP), a dimensionless parameter developed at the Storm Prediction Center (SPC) that applies a subjectively chosen scale. The goal of this research is to determine if useful logistic regression equations can be developed to estimate the conditional probability of supercell tornadoes that are categorized as level 2 or stronger on the enhanced Fujita scale (EF) when a similar set of environmental background parameters is applied as variables. A large database of Rapid Update Cycle (RUC) analysis soundings in proximity to a representative sample of tornadic and nontornadic supercells over the central and eastern United States, a number of which were associated with EF2 or stronger tornadoes, was used to compute supercell tornado forecast parameters similar to those in the original version of STP. Three logistic regression equations were developed from this database, two of which are described and analyzed in detail. Statistical verification for both equations was accomplished using independent data from 2008 in proximity to supercell storms identified by staff at SPC. A recent version of the STP was utilized as a comparison diagnostic to accomplish part of the statistical verification. The results of this research suggest that output from both logistic regression equations can provide valuable guidance in a probabilistic sense, when adjustments are made for the ongoing convective mode. Case studies presented also suggest that this guidance can provide information complementary to STP in severe weather situations with potential for supercell tornadoes.
Abstract
Recent literature has identified several supercell/tornado forecast parameters in common use that are operationally beneficial in assessing environments supportive of supercell tornadoes. These parameters are utilized in the computation of tornado forecast guidance such as the significant tornado parameter (STP), a dimensionless parameter developed at the Storm Prediction Center (SPC) that applies a subjectively chosen scale. The goal of this research is to determine if useful logistic regression equations can be developed to estimate the conditional probability of supercell tornadoes that are categorized as level 2 or stronger on the enhanced Fujita scale (EF) when a similar set of environmental background parameters is applied as variables. A large database of Rapid Update Cycle (RUC) analysis soundings in proximity to a representative sample of tornadic and nontornadic supercells over the central and eastern United States, a number of which were associated with EF2 or stronger tornadoes, was used to compute supercell tornado forecast parameters similar to those in the original version of STP. Three logistic regression equations were developed from this database, two of which are described and analyzed in detail. Statistical verification for both equations was accomplished using independent data from 2008 in proximity to supercell storms identified by staff at SPC. A recent version of the STP was utilized as a comparison diagnostic to accomplish part of the statistical verification. The results of this research suggest that output from both logistic regression equations can provide valuable guidance in a probabilistic sense, when adjustments are made for the ongoing convective mode. Case studies presented also suggest that this guidance can provide information complementary to STP in severe weather situations with potential for supercell tornadoes.
Abstract
This study introduces a novel method for comparing vertical thermodynamic profiles, focusing on the atmospheric boundary layer, across a wide range of meteorological conditions. This method is developed using observed temperature and dewpoint temperature data from 31 153 soundings taken at 0000 UTC and 32 308 soundings taken at 1200 UTC between May 2019 and March 2020. Temperature and dewpoint temperature vertical profiles are first interpolated onto a height above ground level (AGL) coordinate, after which the temperature of the dry adiabat defined by the surface-based parcel’s temperature is subtracted from each quantity at all altitudes. This allows for common sounding features, such as turbulent mixed layers and inversions, to be similarly depicted regardless of temperature and dewpoint temperature differences resulting from altitude, latitude, or seasonality. The soundings that result from applying this method to the observed sounding collection described above are then clustered to identify distinct boundary layer structures in the data. Specifically, separately at 0000 and 1200 UTC, a k-means clustering analysis is conducted in the phase space of the leading two empirical orthogonal functions of the sounding data. As compared to clustering based on the original vertical profiles, which results in clusters that are dominated by seasonal and latitudinal differences, clusters derived from transformed data are less latitudinally and seasonally stratified and better represent boundary layer features such as turbulent mixed layers and pseudoadiabatic profiles. The sounding-comparison method thus provides an objective means of categorizing vertical thermodynamic profiles with wide-ranging applications, as demonstrated by using the method to verify short-range Global Forecast System model forecasts.
Abstract
This study introduces a novel method for comparing vertical thermodynamic profiles, focusing on the atmospheric boundary layer, across a wide range of meteorological conditions. This method is developed using observed temperature and dewpoint temperature data from 31 153 soundings taken at 0000 UTC and 32 308 soundings taken at 1200 UTC between May 2019 and March 2020. Temperature and dewpoint temperature vertical profiles are first interpolated onto a height above ground level (AGL) coordinate, after which the temperature of the dry adiabat defined by the surface-based parcel’s temperature is subtracted from each quantity at all altitudes. This allows for common sounding features, such as turbulent mixed layers and inversions, to be similarly depicted regardless of temperature and dewpoint temperature differences resulting from altitude, latitude, or seasonality. The soundings that result from applying this method to the observed sounding collection described above are then clustered to identify distinct boundary layer structures in the data. Specifically, separately at 0000 and 1200 UTC, a k-means clustering analysis is conducted in the phase space of the leading two empirical orthogonal functions of the sounding data. As compared to clustering based on the original vertical profiles, which results in clusters that are dominated by seasonal and latitudinal differences, clusters derived from transformed data are less latitudinally and seasonally stratified and better represent boundary layer features such as turbulent mixed layers and pseudoadiabatic profiles. The sounding-comparison method thus provides an objective means of categorizing vertical thermodynamic profiles with wide-ranging applications, as demonstrated by using the method to verify short-range Global Forecast System model forecasts.
Abstract
Radar-identified convective modes, peak low-level rotational velocities, and near-storm environmental data were assigned to a sample of tornadoes reported in the contiguous United States during 2009–13. The tornado segment data were filtered by the maximum enhanced Fujita (EF)-scale tornado event per hour using a 40-km horizontal grid. Convective mode was assigned to each tornado event by examining full volumetric Weather Surveillance Radar-1988 Doppler data at the beginning time of each event, and 0.5° peak rotational velocity (V rot) data were identified manually during the life span of each tornado event. Environmental information accompanied each grid-hour event, consisting primarily of supercell-related convective parameters from the hourly objective mesoscale analyses calculated and archived at the Storm Prediction Center. Results from examining environmental and radar attributes, featuring the significant tornado parameter (STP) and 0.5° peak V rot data, suggest an increasing conditional probability for greater EF-scale damage as both STP and 0.5° peak V rot increase, especially with supercells. Possible applications of these findings include using the conditional probability of tornado intensity as a real-time situational awareness tool.
Abstract
Radar-identified convective modes, peak low-level rotational velocities, and near-storm environmental data were assigned to a sample of tornadoes reported in the contiguous United States during 2009–13. The tornado segment data were filtered by the maximum enhanced Fujita (EF)-scale tornado event per hour using a 40-km horizontal grid. Convective mode was assigned to each tornado event by examining full volumetric Weather Surveillance Radar-1988 Doppler data at the beginning time of each event, and 0.5° peak rotational velocity (V rot) data were identified manually during the life span of each tornado event. Environmental information accompanied each grid-hour event, consisting primarily of supercell-related convective parameters from the hourly objective mesoscale analyses calculated and archived at the Storm Prediction Center. Results from examining environmental and radar attributes, featuring the significant tornado parameter (STP) and 0.5° peak V rot data, suggest an increasing conditional probability for greater EF-scale damage as both STP and 0.5° peak V rot increase, especially with supercells. Possible applications of these findings include using the conditional probability of tornado intensity as a real-time situational awareness tool.
Abstract
A gridded, hourly, three-dimensional environmental mesoanalysis database at the Storm Prediction Center (SPC), based on objectively analyzed surface observations blended with the Rapid Update Cycle (RUC) model-analysis fields and described in Parts I and II of this series, is applied to a 2003–11 subset of the SPC tropical cyclone (TC) tornado records. Distributions of environmental convective parameters, derived from SPC hourly mesoanalysis fields that have been related to supercells and tornadoes in the midlatitudes, are evaluated for their pertinence to TC tornado occurrence. The main factor differentiating TC from non-TC tornado environments is much greater deep-tropospheric moisture, associated with reduced lapse rates, lower CAPE, and smaller and more compressed distributions of parameters derived from CAPE and vertical shear. For weak and strong TC tornado categories (EF0–EF1 and EF2–EF3 on the enhanced Fujita scale, respectively), little distinction is evident across most parameters. Radar reflectivity and velocity data also are examined for the same subset of TC tornadoes, in order to determine parent convective modes (e.g., discrete, linear, clustered, supercellular vs nonsupercellular), and the association of those modes with several mesoanalysis parameters. Supercellular TC tornadoes are accompanied by somewhat greater vertical shear than those occurring from other modes. Tornadoes accompanying nonsupercellular radar echoes tend to occur closer to the TC center, where CAPE and shear tend to weaken relative to the outer TC envelope, though there is considerable overlap of their respective radial distributions and environmental parameter spaces.
Abstract
A gridded, hourly, three-dimensional environmental mesoanalysis database at the Storm Prediction Center (SPC), based on objectively analyzed surface observations blended with the Rapid Update Cycle (RUC) model-analysis fields and described in Parts I and II of this series, is applied to a 2003–11 subset of the SPC tropical cyclone (TC) tornado records. Distributions of environmental convective parameters, derived from SPC hourly mesoanalysis fields that have been related to supercells and tornadoes in the midlatitudes, are evaluated for their pertinence to TC tornado occurrence. The main factor differentiating TC from non-TC tornado environments is much greater deep-tropospheric moisture, associated with reduced lapse rates, lower CAPE, and smaller and more compressed distributions of parameters derived from CAPE and vertical shear. For weak and strong TC tornado categories (EF0–EF1 and EF2–EF3 on the enhanced Fujita scale, respectively), little distinction is evident across most parameters. Radar reflectivity and velocity data also are examined for the same subset of TC tornadoes, in order to determine parent convective modes (e.g., discrete, linear, clustered, supercellular vs nonsupercellular), and the association of those modes with several mesoanalysis parameters. Supercellular TC tornadoes are accompanied by somewhat greater vertical shear than those occurring from other modes. Tornadoes accompanying nonsupercellular radar echoes tend to occur closer to the TC center, where CAPE and shear tend to weaken relative to the outer TC envelope, though there is considerable overlap of their respective radial distributions and environmental parameter spaces.
Abstract
A sample of 22 901 tornado and significant severe thunderstorm events, filtered on an hourly 40-km grid, was collected for the period 2003–11 across the contiguous United States (CONUS). Convective mode was assigned to each case via manual examination of full volumetric radar data (Part I of this study), and environmental information accompanied each grid-hour event from the hourly objective analyses calculated and archived at the Storm Prediction Center (SPC). Sounding-derived parameters related to supercells and tornadoes formed the basis of this investigation owing to the dominance of right-moving supercells in tornado production and the availability of supercell-related convective parameters in the SPC environmental archive. The tornado and significant severe thunderstorm events were stratified by convective mode and season. Measures of buoyancy discriminated most strongly between supercell and quasi-linear convective system (QLCS) tornado events during the winter, while bulk wind differences and storm-relative helicity were similar for both supercell and QLCS tornado environments within in each season. The larger values of the effective-layer supercell composite parameter (SCP) and the effective-layer significant tornado parameter (STP) favored right-moving supercells that produced significant tornadoes, as opposed to weak tornadoes or supercells that produced only significant hail or damaging winds. Additionally, mesocyclone strength tended to increase with increasing SCP for supercells, and STP tended to increase as tornado damage class ratings increased. The findings underscore the importance of convective mode (discrete or cluster supercells), mesocyclone strength, and near-storm environment (as represented by large values of STP) in consistent, real-time identification of intense tornadoes.
Abstract
A sample of 22 901 tornado and significant severe thunderstorm events, filtered on an hourly 40-km grid, was collected for the period 2003–11 across the contiguous United States (CONUS). Convective mode was assigned to each case via manual examination of full volumetric radar data (Part I of this study), and environmental information accompanied each grid-hour event from the hourly objective analyses calculated and archived at the Storm Prediction Center (SPC). Sounding-derived parameters related to supercells and tornadoes formed the basis of this investigation owing to the dominance of right-moving supercells in tornado production and the availability of supercell-related convective parameters in the SPC environmental archive. The tornado and significant severe thunderstorm events were stratified by convective mode and season. Measures of buoyancy discriminated most strongly between supercell and quasi-linear convective system (QLCS) tornado events during the winter, while bulk wind differences and storm-relative helicity were similar for both supercell and QLCS tornado environments within in each season. The larger values of the effective-layer supercell composite parameter (SCP) and the effective-layer significant tornado parameter (STP) favored right-moving supercells that produced significant tornadoes, as opposed to weak tornadoes or supercells that produced only significant hail or damaging winds. Additionally, mesocyclone strength tended to increase with increasing SCP for supercells, and STP tended to increase as tornado damage class ratings increased. The findings underscore the importance of convective mode (discrete or cluster supercells), mesocyclone strength, and near-storm environment (as represented by large values of STP) in consistent, real-time identification of intense tornadoes.
Abstract
This study evaluates forecast vertical thermodynamic profiles and derived thermodynamic parameters from two regional/convection-allowing model pairs, the North American Mesoscale Forecast System and the North American Mesoscale Nest model pair and the Rapid Refresh and High Resolution Rapid Refresh model pair, in warm-season, thunderstorm-supporting environments. Differences in bias and mean absolute error between the regional and convection-allowing models in each of the two pairs, while often statistically significant, are practically small for the variables, parameters, and vertical levels considered, such that the smaller-scale variability resolved by convection-allowing models does not degrade their forecast skill. Model biases shared by the regional and convection-allowing models in each pair are documented, particularly the substantial cool and moist biases in the planetary boundary layer arising from the Mellor–Yamada–Janjić planetary boundary layer parameterization used by the North American Mesoscale model and the Nest version as well as the middle-tropospheric moist bias shared by the Rapid Refresh and High Resolution Rapid Refresh models. Bias and mean absolute errors typically have larger magnitudes in the evening, when buoyancy is a significant contributor to turbulent vertical mixing, than in the morning. Vertical thermodynamic profile biases extend over a deep vertical layer in the western United States given strong sensible heating of the underlying surface. The results suggest that convection-allowing models can fulfill the use cases typically and historically met by regional models in operations at forecast entities such as the Storm Prediction Center, a fruitful finding given the proposed elimination of regional models with the Next-Generation Global Prediction System initiative.
Abstract
This study evaluates forecast vertical thermodynamic profiles and derived thermodynamic parameters from two regional/convection-allowing model pairs, the North American Mesoscale Forecast System and the North American Mesoscale Nest model pair and the Rapid Refresh and High Resolution Rapid Refresh model pair, in warm-season, thunderstorm-supporting environments. Differences in bias and mean absolute error between the regional and convection-allowing models in each of the two pairs, while often statistically significant, are practically small for the variables, parameters, and vertical levels considered, such that the smaller-scale variability resolved by convection-allowing models does not degrade their forecast skill. Model biases shared by the regional and convection-allowing models in each pair are documented, particularly the substantial cool and moist biases in the planetary boundary layer arising from the Mellor–Yamada–Janjić planetary boundary layer parameterization used by the North American Mesoscale model and the Nest version as well as the middle-tropospheric moist bias shared by the Rapid Refresh and High Resolution Rapid Refresh models. Bias and mean absolute errors typically have larger magnitudes in the evening, when buoyancy is a significant contributor to turbulent vertical mixing, than in the morning. Vertical thermodynamic profile biases extend over a deep vertical layer in the western United States given strong sensible heating of the underlying surface. The results suggest that convection-allowing models can fulfill the use cases typically and historically met by regional models in operations at forecast entities such as the Storm Prediction Center, a fruitful finding given the proposed elimination of regional models with the Next-Generation Global Prediction System initiative.
Abstract
A severe thunderstorm wind gust climatology spanning 2003–09 for the contiguous United States is developed using measured Automated Surface Observing System (ASOS) and Automated Weather Observing System (AWOS) wind gusts. Archived severe report information from the National Climatic Data Center publication Storm Data and single-site volumetric radar data are used to identify severe wind gust observations [≥50 kt (25.7 m s−1)] associated with thunderstorms and to classify the convective mode of the storms. The measured severe wind gust distribution, comprising only 2% of all severe gusts, is examined with respect to radar-based convective modes. The convective mode scheme presented herein focuses on three primary radar-based storm categories: supercell, quasi-linear convective systems (QLCSs), and disorganized. Measured severe gust frequency revealed distinct spatial patterns, where the high plains received the greatest number of gusts and occurred most often in the late spring and summer months. Severe wind gusts produced by supercells were most frequent over the plains, while those from QLCS gusts were most frequent in the plains and Midwest. Meanwhile, disorganized storms produced most of their severe gusts in the plains and Intermountain West. A reverse spatial distribution signal exists in the location between the maximum measured severe wind gust corridor located over the high plains and the maximum in all severe thunderstorm wind reports from Storm Data, located near and west of the southern Appalachians.
Abstract
A severe thunderstorm wind gust climatology spanning 2003–09 for the contiguous United States is developed using measured Automated Surface Observing System (ASOS) and Automated Weather Observing System (AWOS) wind gusts. Archived severe report information from the National Climatic Data Center publication Storm Data and single-site volumetric radar data are used to identify severe wind gust observations [≥50 kt (25.7 m s−1)] associated with thunderstorms and to classify the convective mode of the storms. The measured severe wind gust distribution, comprising only 2% of all severe gusts, is examined with respect to radar-based convective modes. The convective mode scheme presented herein focuses on three primary radar-based storm categories: supercell, quasi-linear convective systems (QLCSs), and disorganized. Measured severe gust frequency revealed distinct spatial patterns, where the high plains received the greatest number of gusts and occurred most often in the late spring and summer months. Severe wind gusts produced by supercells were most frequent over the plains, while those from QLCS gusts were most frequent in the plains and Midwest. Meanwhile, disorganized storms produced most of their severe gusts in the plains and Intermountain West. A reverse spatial distribution signal exists in the location between the maximum measured severe wind gust corridor located over the high plains and the maximum in all severe thunderstorm wind reports from Storm Data, located near and west of the southern Appalachians.
Abstract
With the advent of convection-allowing NWP models (CAMs) comes the potential for new forms of forecast guidance. While CAMs lack the required resolution to simulate many severe phenomena associated with convection (e.g., large hail, downburst winds, and tornadoes), they can still provide unique guidance for the occurrence of these phenomena if “extreme” patterns of behavior in simulated storms are strongly correlated with observed severe phenomena. This concept is explored using output from a series of CAM forecasts generated on a daily basis during the spring of 2008. This output is mined for the presence of extreme values of updraft helicity (UH), a diagnostic field used to identify supercellular storms. Extreme values of the UH field are flagged as simulated “surrogate” severe weather reports and the spatial correspondence between these surrogate reports and actual observed severe reports is determined. In addition, probabilistic forecasts [surrogate severe probabilistic forecasts (SSPFs)] are created from each field’s simulated surrogate severe reports using a Gaussian smoother. The simulated surrogate reports are capable of reproducing the seasonal climatology observed within the field of actual reports. The SSPFs created from the surrogates are verified using ROC curves and reliability diagrams and the sensitivity of the verification metrics to the smoothing parameter in the Gaussian distribution is tested. The SSPFs produce reliable forecast probabilities with minimal calibration. These results demonstrate that a relatively straightforward postprocessing procedure, which focuses on the characteristics of explicitly predicted convective entities, can provide reliable severe weather forecast guidance. It is anticipated that this technique will be even more valuable when implemented within a convection-allowing ensemble forecasting system.
Abstract
With the advent of convection-allowing NWP models (CAMs) comes the potential for new forms of forecast guidance. While CAMs lack the required resolution to simulate many severe phenomena associated with convection (e.g., large hail, downburst winds, and tornadoes), they can still provide unique guidance for the occurrence of these phenomena if “extreme” patterns of behavior in simulated storms are strongly correlated with observed severe phenomena. This concept is explored using output from a series of CAM forecasts generated on a daily basis during the spring of 2008. This output is mined for the presence of extreme values of updraft helicity (UH), a diagnostic field used to identify supercellular storms. Extreme values of the UH field are flagged as simulated “surrogate” severe weather reports and the spatial correspondence between these surrogate reports and actual observed severe reports is determined. In addition, probabilistic forecasts [surrogate severe probabilistic forecasts (SSPFs)] are created from each field’s simulated surrogate severe reports using a Gaussian smoother. The simulated surrogate reports are capable of reproducing the seasonal climatology observed within the field of actual reports. The SSPFs created from the surrogates are verified using ROC curves and reliability diagrams and the sensitivity of the verification metrics to the smoothing parameter in the Gaussian distribution is tested. The SSPFs produce reliable forecast probabilities with minimal calibration. These results demonstrate that a relatively straightforward postprocessing procedure, which focuses on the characteristics of explicitly predicted convective entities, can provide reliable severe weather forecast guidance. It is anticipated that this technique will be even more valuable when implemented within a convection-allowing ensemble forecasting system.
Abstract
The response of severe local storms to environmental evolution across the early evening transition (EET) remains a forecasting challenge, particularly within the context of the Southeast U.S. storm climatology, which includes the increased presence of low-CAPE environments and tornadic nonsupercell modes. To disentangle these complex environmental interactions, Southeast severe convective reports spanning 2003–18 are temporally binned relative to local sunset. Sounding-derived data corresponding to each report are used to characterize how the near-storm environment evolves across the EET, and whether these changes influence the mode, frequency, and tornadic likelihood of their associated storms. High-shear, high-CAPE (HSHC) environments are contrasted with high-shear, low-CAPE (HSLC) environments to highlight physical processes governing storm maintenance and tornadogenesis in the absence of large instability. Last, statistical analysis is performed to determine which aspects of the near-storm environment most effectively discriminate between tornadic (or significantly tornadic) and nontornadic storms toward constructing new sounding-derived forecast guidance parameters for multiple modal and environmental combinations. Results indicate that HSLC environments evolve differently than HSHC environments, particularly for nonsupercell (e.g., quasi-linear convective system) modes. These low-CAPE environments sustain higher values of low-level shear and storm-relative helicity (SRH) and destabilize postsunset—potentially compensating for minimal buoyancy. Furthermore, the existence of HSLC storm environments presunset increases the likelihood of nonsupercellular tornadoes postsunset. Existing forecast guidance metrics such as the significant tornado parameter (STP) remain the most skillful predictors of HSHC tornadoes. However, HSLC tornado prediction can be improved by considering variables like precipitable water, downdraft CAPE, and effective inflow base.
Abstract
The response of severe local storms to environmental evolution across the early evening transition (EET) remains a forecasting challenge, particularly within the context of the Southeast U.S. storm climatology, which includes the increased presence of low-CAPE environments and tornadic nonsupercell modes. To disentangle these complex environmental interactions, Southeast severe convective reports spanning 2003–18 are temporally binned relative to local sunset. Sounding-derived data corresponding to each report are used to characterize how the near-storm environment evolves across the EET, and whether these changes influence the mode, frequency, and tornadic likelihood of their associated storms. High-shear, high-CAPE (HSHC) environments are contrasted with high-shear, low-CAPE (HSLC) environments to highlight physical processes governing storm maintenance and tornadogenesis in the absence of large instability. Last, statistical analysis is performed to determine which aspects of the near-storm environment most effectively discriminate between tornadic (or significantly tornadic) and nontornadic storms toward constructing new sounding-derived forecast guidance parameters for multiple modal and environmental combinations. Results indicate that HSLC environments evolve differently than HSHC environments, particularly for nonsupercell (e.g., quasi-linear convective system) modes. These low-CAPE environments sustain higher values of low-level shear and storm-relative helicity (SRH) and destabilize postsunset—potentially compensating for minimal buoyancy. Furthermore, the existence of HSLC storm environments presunset increases the likelihood of nonsupercellular tornadoes postsunset. Existing forecast guidance metrics such as the significant tornado parameter (STP) remain the most skillful predictors of HSHC tornadoes. However, HSLC tornado prediction can be improved by considering variables like precipitable water, downdraft CAPE, and effective inflow base.
