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Abstract
Downslope windstorms are of major concern to those living in and around Boulder, Colorado, often striking with little warning, occasionally bringing clear-air wind gusts of 35–50 m s−1 or higher, and producing widespread damage. Historically, numerical models used for forecasting these events had lower than desired accuracy. This observation provides the motivation to study the potential for improving windstorm forecasting through the use of linear and nonlinear statistical modeling techniques with a perfect prog approach. A 10-yr mountain-windstorm dataset and a set of 18 predictors are used to train and test the models. For the linear model, a stepwise regression is applied. It is difficult to determine which predictor is the most important, although significance testing suggests that 700-hPa flow is selected often. The nonlinear techniques employed, feedforward neural networks (NN) and support vector regression (SVR), do not filter out predictors as the former uses a hidden layer to account for the nonlinearities in the data, whereas the latter fits a kernel function to the data to optimize prediction. The models are evaluated using root-mean-square error (RMSE) and median residuals. The SVR model has the lowest forecast errors, consistently, and is not prone to creating outlier forecasts. Stepwise linear regression (LR) yielded results that were accurate to within an RMSE of 8 m s−1; whereas an NN had errors of 7–9 m s−1 and SVR had errors of 4–6 m s−1. For SVR, 85% of the forecasts predicted maximum wind gusts with an RMSE of less than 6 m s−1 and all forecasts predicted wind gusts with an RMSE of below 12 m s−1. The LR method performed slightly better in most evaluations than NNs; however, SVR was the optimal technique.
Abstract
Downslope windstorms are of major concern to those living in and around Boulder, Colorado, often striking with little warning, occasionally bringing clear-air wind gusts of 35–50 m s−1 or higher, and producing widespread damage. Historically, numerical models used for forecasting these events had lower than desired accuracy. This observation provides the motivation to study the potential for improving windstorm forecasting through the use of linear and nonlinear statistical modeling techniques with a perfect prog approach. A 10-yr mountain-windstorm dataset and a set of 18 predictors are used to train and test the models. For the linear model, a stepwise regression is applied. It is difficult to determine which predictor is the most important, although significance testing suggests that 700-hPa flow is selected often. The nonlinear techniques employed, feedforward neural networks (NN) and support vector regression (SVR), do not filter out predictors as the former uses a hidden layer to account for the nonlinearities in the data, whereas the latter fits a kernel function to the data to optimize prediction. The models are evaluated using root-mean-square error (RMSE) and median residuals. The SVR model has the lowest forecast errors, consistently, and is not prone to creating outlier forecasts. Stepwise linear regression (LR) yielded results that were accurate to within an RMSE of 8 m s−1; whereas an NN had errors of 7–9 m s−1 and SVR had errors of 4–6 m s−1. For SVR, 85% of the forecasts predicted maximum wind gusts with an RMSE of less than 6 m s−1 and all forecasts predicted wind gusts with an RMSE of below 12 m s−1. The LR method performed slightly better in most evaluations than NNs; however, SVR was the optimal technique.
Abstract
The Marshall Fire on 30 December 2021 became the most destructive wildfire costwise in Colorado history as it evolved into a suburban firestorm in southeastern Boulder County, driven by strong winds and a snow-free and drought-influenced fuel state. The fire was driven by a strong downslope windstorm that maintained its intensity for nearly 11 hours. The southward movement of a large-scale jet axis across Boulder County brought a quick transition that day into a zone of upper-level descent, enhancing the midlevel inversion providing a favorable environment for an amplifying downstream mountain wave. In several aspects, this windstorm did not follow typical downslope windstorm behavior. NOAA rapidly updating numerical weather prediction guidance (including the High-Resolution Rapid Refresh) provided operationally useful forecasts of the windstorm, leading to the issuance of a High-Wind Warning (HWW) for eastern Boulder County. No Red Flag Warning was issued due to a too restrictive relative humidity criterion (already published alternatives are recommended); however, owing to the HWW, a countywide burn ban was issued for that day. Consideration of spatial (vertical and horizontal) and temporal (both valid time and initialization time) neighborhoods allows some quantification of forecast uncertainty from deterministic forecasts—important in real-time use for forecasting and public warnings of extreme events. Essentially, dimensions of the deterministic model were used to roughly estimate an ensemble forecast. These dimensions including run-to-run consistency are also important for subsequent evaluation of forecasts for small-scale features such as downslope windstorms and the tropospheric features responsible for them, similar to forecasts of deep, moist convection and related severe weather.
Significance Statement
The Front Range windstorm of 30 December 2021 combined extreme surface winds (>45 m s−1) with fire ignition resulting in an extraordinary and quickly evolving, extremely destructive wildfire–urban interface fire event. This windstorm differed from typical downslope windstorms in several aspects. We describe the observations, model guidance, and decision-making of operational forecasters for this event. In effect, an ensemble forecast was approximated by use of a frequently updated deterministic model by operational forecasters, and this combined use of temporal, spatial (horizontal and vertical), and other forecast dimensions is suggested to better estimate the possibility of such extreme events.
Abstract
The Marshall Fire on 30 December 2021 became the most destructive wildfire costwise in Colorado history as it evolved into a suburban firestorm in southeastern Boulder County, driven by strong winds and a snow-free and drought-influenced fuel state. The fire was driven by a strong downslope windstorm that maintained its intensity for nearly 11 hours. The southward movement of a large-scale jet axis across Boulder County brought a quick transition that day into a zone of upper-level descent, enhancing the midlevel inversion providing a favorable environment for an amplifying downstream mountain wave. In several aspects, this windstorm did not follow typical downslope windstorm behavior. NOAA rapidly updating numerical weather prediction guidance (including the High-Resolution Rapid Refresh) provided operationally useful forecasts of the windstorm, leading to the issuance of a High-Wind Warning (HWW) for eastern Boulder County. No Red Flag Warning was issued due to a too restrictive relative humidity criterion (already published alternatives are recommended); however, owing to the HWW, a countywide burn ban was issued for that day. Consideration of spatial (vertical and horizontal) and temporal (both valid time and initialization time) neighborhoods allows some quantification of forecast uncertainty from deterministic forecasts—important in real-time use for forecasting and public warnings of extreme events. Essentially, dimensions of the deterministic model were used to roughly estimate an ensemble forecast. These dimensions including run-to-run consistency are also important for subsequent evaluation of forecasts for small-scale features such as downslope windstorms and the tropospheric features responsible for them, similar to forecasts of deep, moist convection and related severe weather.
Significance Statement
The Front Range windstorm of 30 December 2021 combined extreme surface winds (>45 m s−1) with fire ignition resulting in an extraordinary and quickly evolving, extremely destructive wildfire–urban interface fire event. This windstorm differed from typical downslope windstorms in several aspects. We describe the observations, model guidance, and decision-making of operational forecasters for this event. In effect, an ensemble forecast was approximated by use of a frequently updated deterministic model by operational forecasters, and this combined use of temporal, spatial (horizontal and vertical), and other forecast dimensions is suggested to better estimate the possibility of such extreme events.
Abstract
Tropical cyclone (TC) translation speed influences rainfall accumulation, storm surge, and exposure to high winds. These effects are greatest when storms stall. Here, we provide a definition and climatology of slow-moving or stalling TCs in the North Atlantic from 1900–2020. A stall is defined as a TC with a track contained in a circular area (“corral”) with a radius of ≤ 200 km for 72 hours. Of the 1,274 North Atlantic TCs, 191 storms met this definition (15%). Ten are multi-stalling storms, or those that experienced more than one stall period. Hurricane Ginger in 1971 stalled the most with four separate stalls. Stalling TC locations are clustered in the western Caribbean, the central Gulf Coast, the Bay of Campeche, and near Florida and the Carolinas. Stalling was most common in October TCs (17.3% of October total) and least common in August (8.2%). The estimated annual frequency of stalls significantly increased over the satellite-era (1966–2020) by 1.5% per year, and the cumulative frequency in the number of stalls compared to all storms also increased. Stalling storms have a significantly higher frequency of major hurricane status than non-stalling storms. Storms are also more likely to stall near the coast (≤ 200 km). Approximately 40% (n=77) of the stalling TCs experienced a period of rapid intensification, and five did so within 200 km of a coastal zone. These results will aid emergency managers in regions that experience frequent stalls by providing information they can use to better prepare for the future.
Abstract
Tropical cyclone (TC) translation speed influences rainfall accumulation, storm surge, and exposure to high winds. These effects are greatest when storms stall. Here, we provide a definition and climatology of slow-moving or stalling TCs in the North Atlantic from 1900–2020. A stall is defined as a TC with a track contained in a circular area (“corral”) with a radius of ≤ 200 km for 72 hours. Of the 1,274 North Atlantic TCs, 191 storms met this definition (15%). Ten are multi-stalling storms, or those that experienced more than one stall period. Hurricane Ginger in 1971 stalled the most with four separate stalls. Stalling TC locations are clustered in the western Caribbean, the central Gulf Coast, the Bay of Campeche, and near Florida and the Carolinas. Stalling was most common in October TCs (17.3% of October total) and least common in August (8.2%). The estimated annual frequency of stalls significantly increased over the satellite-era (1966–2020) by 1.5% per year, and the cumulative frequency in the number of stalls compared to all storms also increased. Stalling storms have a significantly higher frequency of major hurricane status than non-stalling storms. Storms are also more likely to stall near the coast (≤ 200 km). Approximately 40% (n=77) of the stalling TCs experienced a period of rapid intensification, and five did so within 200 km of a coastal zone. These results will aid emergency managers in regions that experience frequent stalls by providing information they can use to better prepare for the future.
Abstract
A modified urban canopy parameterization (UCP) is developed and evaluated in a three-dimensional mesoscale model to assess the urban impact on surface and lower-atmospheric properties. This parameterization accounts for the effects of building drag, turbulent production, radiation balance, anthropogenic heating, and building rooftop heating/cooling. U.S. Geological Survey (USGS) land-use data are also utilized to derive urban infrastructure and urban surface properties needed for driving the UCP. An intensive observational period with clear sky, strong ambient wind, and drainage flow, and the absence of a land–lake breeze over the Salt Lake Valley, occurring on 25–26 October 2000, is selected for this study.
A series of sensitivity experiments are performed to gain understanding of the urban impact in the mesoscale model. Results indicate that within the selected urban environment, urban surface characteristics and anthropogenic heating play little role in the formation of the modeled nocturnal urban boundary layer. The rooftop effect appears to be the main contributor to this urban boundary layer. Sensitivity experiments also show that for this weak urban heat island case, the model horizontal grid resolution is important in simulating the elevated inversion layer.
The root-mean-square errors of the predicted wind and temperature with respect to surface station measurements exhibit substantially larger discrepancies at the urban locations than their rural counterparts. However, the close agreement of modeled tracer concentration with observations fairly justifies the modeled urban impact on the wind-direction shift and wind-drag effects.
Abstract
A modified urban canopy parameterization (UCP) is developed and evaluated in a three-dimensional mesoscale model to assess the urban impact on surface and lower-atmospheric properties. This parameterization accounts for the effects of building drag, turbulent production, radiation balance, anthropogenic heating, and building rooftop heating/cooling. U.S. Geological Survey (USGS) land-use data are also utilized to derive urban infrastructure and urban surface properties needed for driving the UCP. An intensive observational period with clear sky, strong ambient wind, and drainage flow, and the absence of a land–lake breeze over the Salt Lake Valley, occurring on 25–26 October 2000, is selected for this study.
A series of sensitivity experiments are performed to gain understanding of the urban impact in the mesoscale model. Results indicate that within the selected urban environment, urban surface characteristics and anthropogenic heating play little role in the formation of the modeled nocturnal urban boundary layer. The rooftop effect appears to be the main contributor to this urban boundary layer. Sensitivity experiments also show that for this weak urban heat island case, the model horizontal grid resolution is important in simulating the elevated inversion layer.
The root-mean-square errors of the predicted wind and temperature with respect to surface station measurements exhibit substantially larger discrepancies at the urban locations than their rural counterparts. However, the close agreement of modeled tracer concentration with observations fairly justifies the modeled urban impact on the wind-direction shift and wind-drag effects.
Abstract
Tropical cyclone (TC) forecast verification techniques have traditionally focused on track and intensity, as these are some of the most important characteristics of TCs and are often the principal verification concerns of operational forecast centers. However, there is a growing need to verify other aspects of TCs as process-based validation techniques may be increasingly necessary for further track and intensity forecast improvements as well as improving communication of the broad impacts of TCs including inland flooding from precipitation. Here we present a set of TC-focused verification methods available via the Model Evaluation Tools (MET) ranging from traditional approaches to the application of storm-centric coordinates and the use of feature-based verification of spatially defined TC objects. Storm-relative verification using observed and forecast tracks can be useful for identifying model biases in precipitation accumulation in relation to the storm center. Using a storm-centric cylindrical coordinate system based on the radius of maximum wind adds additional storm-relative capabilities to regrid precipitation fields onto cylindrical or polar coordinates. This powerful process-based model diagnostic and verification technique provides a framework for improved understanding of feedbacks between forecast tracks, intensity, and precipitation distributions. Finally, object-based verification including land masking capabilities provides even more nuanced verification options. Precipitation objects of interest, either the central core of TCs or extended areas of rainfall after landfall, can be identified, matched to observations, and quickly aggregated to build meaningful spatial and summary verification statistics.
Abstract
Tropical cyclone (TC) forecast verification techniques have traditionally focused on track and intensity, as these are some of the most important characteristics of TCs and are often the principal verification concerns of operational forecast centers. However, there is a growing need to verify other aspects of TCs as process-based validation techniques may be increasingly necessary for further track and intensity forecast improvements as well as improving communication of the broad impacts of TCs including inland flooding from precipitation. Here we present a set of TC-focused verification methods available via the Model Evaluation Tools (MET) ranging from traditional approaches to the application of storm-centric coordinates and the use of feature-based verification of spatially defined TC objects. Storm-relative verification using observed and forecast tracks can be useful for identifying model biases in precipitation accumulation in relation to the storm center. Using a storm-centric cylindrical coordinate system based on the radius of maximum wind adds additional storm-relative capabilities to regrid precipitation fields onto cylindrical or polar coordinates. This powerful process-based model diagnostic and verification technique provides a framework for improved understanding of feedbacks between forecast tracks, intensity, and precipitation distributions. Finally, object-based verification including land masking capabilities provides even more nuanced verification options. Precipitation objects of interest, either the central core of TCs or extended areas of rainfall after landfall, can be identified, matched to observations, and quickly aggregated to build meaningful spatial and summary verification statistics.
Abstract
Over the past 100 years, the collaborative effort of the international science community, including government weather services and the media, along with the associated proliferation of environmental observations, improved scientific understanding, and growth of technology, has radically transformed weather forecasting into an effective global and regional environmental prediction capability. This chapter traces the evolution of forecasting, starting in 1919 [when the American Meteorological Society (AMS) was founded], over four eras separated by breakpoints at 1939, 1956, and 1985. The current state of forecasting could not have been achieved without essential collaboration within and among countries in pursuing the common weather and Earth-system prediction challenge. AMS itself has had a strong role in enabling this international collaboration.
Abstract
Over the past 100 years, the collaborative effort of the international science community, including government weather services and the media, along with the associated proliferation of environmental observations, improved scientific understanding, and growth of technology, has radically transformed weather forecasting into an effective global and regional environmental prediction capability. This chapter traces the evolution of forecasting, starting in 1919 [when the American Meteorological Society (AMS) was founded], over four eras separated by breakpoints at 1939, 1956, and 1985. The current state of forecasting could not have been achieved without essential collaboration within and among countries in pursuing the common weather and Earth-system prediction challenge. AMS itself has had a strong role in enabling this international collaboration.
Abstract
A stochastic parameter perturbation (SPP) scheme consisting of spatially and temporally varying perturbations of uncertain parameters in the Grell–Freitas convective scheme and the Mellor–Yamada–Nakanishi–Niino planetary boundary scheme was developed within the Rapid Refresh ensemble system based on the Weather Research and Forecasting Model. Alone the stochastic parameter perturbations generate insufficient spread to be an alternative to the operational configuration that utilizes combinations of multiple parameterization schemes. However, when combined with other stochastic parameterization schemes, such as the stochastic kinetic energy backscatter (SKEB) scheme or the stochastic perturbation of physics tendencies (SPPT) scheme, the stochastic ensemble system has comparable forecast performance. An additional analysis quantifies the added value of combining SPP and SPPT over an ensemble that uses SPPT only, which is generally beneficial, especially for surface variables. The ensemble combining all three stochastic methods consistently produces the best spread–skill ratio and generally outperforms the multiphysics ensemble. The results of this study indicate that using a single-physics suite ensemble together with stochastic methods is an attractive alternative to multiphysics ensembles and should be considered in the design of future high-resolution regional and global ensembles.
Abstract
A stochastic parameter perturbation (SPP) scheme consisting of spatially and temporally varying perturbations of uncertain parameters in the Grell–Freitas convective scheme and the Mellor–Yamada–Nakanishi–Niino planetary boundary scheme was developed within the Rapid Refresh ensemble system based on the Weather Research and Forecasting Model. Alone the stochastic parameter perturbations generate insufficient spread to be an alternative to the operational configuration that utilizes combinations of multiple parameterization schemes. However, when combined with other stochastic parameterization schemes, such as the stochastic kinetic energy backscatter (SKEB) scheme or the stochastic perturbation of physics tendencies (SPPT) scheme, the stochastic ensemble system has comparable forecast performance. An additional analysis quantifies the added value of combining SPP and SPPT over an ensemble that uses SPPT only, which is generally beneficial, especially for surface variables. The ensemble combining all three stochastic methods consistently produces the best spread–skill ratio and generally outperforms the multiphysics ensemble. The results of this study indicate that using a single-physics suite ensemble together with stochastic methods is an attractive alternative to multiphysics ensembles and should be considered in the design of future high-resolution regional and global ensembles.
Abstract
Spurious mountain-wave features have been reported as false alarms of light-or-stronger numerical weather prediction (NWP)-based cruise level turbulence forecasts especially over the western mountainous region of North America. To reduce this problem, a hybrid sigma–pressure vertical coordinate system was implemented in NOAA’s operational Rapid Refresh model, version 4 (RAPv4), which has been running in parallel with the conventional terrain-following coordinate system of RAP version 3 (RAPv3). Direct comparison of vertical velocity |w| fields from the RAPv4 and RAPv3 models shows that the new RAPv4 model significantly reduces small-scale spurious vertical velocities induced by the conventional terrain-following coordinate system in the RAPv3. For aircraft-scale turbulence forecasts, |w| and |w|/Richardson number (|w|/Ri) derived from both the RAPv4 and RAPv3 models are converted into energy dissipation rate (EDR) estimates. Then, those EDR-scaled indices are evaluated using more than 1.2 million in situ EDR turbulence reports from commercial aircraft for 4 months (September–December 2017). Scores of the area under receiver operating characteristic curves for the |w|- and |w|/Ri-based EDR forecasts from the RAPv4 are 0.69 and 0.83, which is statistically significantly improved over the RAPv3 of 0.63 and 0.77, respectively. The new RAPv4 became operational on 12 July 2018 and provides better guidance for operational turbulence forecasting over North America.
Abstract
Spurious mountain-wave features have been reported as false alarms of light-or-stronger numerical weather prediction (NWP)-based cruise level turbulence forecasts especially over the western mountainous region of North America. To reduce this problem, a hybrid sigma–pressure vertical coordinate system was implemented in NOAA’s operational Rapid Refresh model, version 4 (RAPv4), which has been running in parallel with the conventional terrain-following coordinate system of RAP version 3 (RAPv3). Direct comparison of vertical velocity |w| fields from the RAPv4 and RAPv3 models shows that the new RAPv4 model significantly reduces small-scale spurious vertical velocities induced by the conventional terrain-following coordinate system in the RAPv3. For aircraft-scale turbulence forecasts, |w| and |w|/Richardson number (|w|/Ri) derived from both the RAPv4 and RAPv3 models are converted into energy dissipation rate (EDR) estimates. Then, those EDR-scaled indices are evaluated using more than 1.2 million in situ EDR turbulence reports from commercial aircraft for 4 months (September–December 2017). Scores of the area under receiver operating characteristic curves for the |w|- and |w|/Ri-based EDR forecasts from the RAPv4 are 0.69 and 0.83, which is statistically significantly improved over the RAPv3 of 0.63 and 0.77, respectively. The new RAPv4 became operational on 12 July 2018 and provides better guidance for operational turbulence forecasting over North America.
Abstract
This study describes the initial application of radiance bias correction and channel selection in the hourly updated Rapid Refresh model. For this initial application, data from the Atmospheric Infrared Sounder (AIRS) are used; this dataset gives atmospheric temperature and water vapor information at higher vertical resolution and accuracy than previously launched low-spectral resolution satellite systems. In this preliminary study, data from AIRS are shown to add skill to short-range weather forecasts over a relatively data-rich area. Two 1-month retrospective runs were conducted to evaluate the impact of assimilating clear-sky AIRS radiance data on 1–12-h forecasts using a research version of the National Oceanic and Atmospheric Administration (NOAA) Rapid Refresh (RAP) regional mesoscale model already assimilating conventional and other radiance [AMSU-A, Microwave Humidity Sounder (MHS), HIRS-4] data. Prior to performing the assimilation, a channel selection and bias-correction spinup procedure was conducted that was appropriate for the RAP configuration. RAP forecasts initialized from analyses with and without AIRS data were verified against radiosonde, surface atmosphere, precipitation, and satellite radiance observations. Results show that the impact from AIRS radiance data on short-range forecast skill in the RAP system is small but positive and statistically significant at the 95% confidence level. The RAP-specific channel selection and bias correction procedures described in this study were the basis for similar applications to other radiance datasets now assimilated in version 3 of RAP implemented at NOAA’s National Centers for Environmental Prediction (NCEP) in August 2016.
Abstract
This study describes the initial application of radiance bias correction and channel selection in the hourly updated Rapid Refresh model. For this initial application, data from the Atmospheric Infrared Sounder (AIRS) are used; this dataset gives atmospheric temperature and water vapor information at higher vertical resolution and accuracy than previously launched low-spectral resolution satellite systems. In this preliminary study, data from AIRS are shown to add skill to short-range weather forecasts over a relatively data-rich area. Two 1-month retrospective runs were conducted to evaluate the impact of assimilating clear-sky AIRS radiance data on 1–12-h forecasts using a research version of the National Oceanic and Atmospheric Administration (NOAA) Rapid Refresh (RAP) regional mesoscale model already assimilating conventional and other radiance [AMSU-A, Microwave Humidity Sounder (MHS), HIRS-4] data. Prior to performing the assimilation, a channel selection and bias-correction spinup procedure was conducted that was appropriate for the RAP configuration. RAP forecasts initialized from analyses with and without AIRS data were verified against radiosonde, surface atmosphere, precipitation, and satellite radiance observations. Results show that the impact from AIRS radiance data on short-range forecast skill in the RAP system is small but positive and statistically significant at the 95% confidence level. The RAP-specific channel selection and bias correction procedures described in this study were the basis for similar applications to other radiance datasets now assimilated in version 3 of RAP implemented at NOAA’s National Centers for Environmental Prediction (NCEP) in August 2016.
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.
