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Abstract
An analysis of how parameterized convection interacts with hydrostatic, explicitly resolved precipitation processes to represent multiscale convective overturning in a mesoscale-resolution numerical simulation is presented. Critically important ingredients of the successful simulation are identified and the degree to which simulations are consistent with observations and theoretical considerations is examined. Of particular concern is how convective parameterization routines reconcile the deep moist absolutely unstable structures that form in mesoscale convective systems.
It is found that these structures arise primarily from resolvable-scale vertical motion and that the model responds to these structures not only by maintaining parameterized convection, but also by developing a hydrostatic manifestation of convective overturning on its smallest resolvable horizontal scales. The strongest and most distinctive mesoscale perturbations develop in regions where the mesoscale contribution to convective overturning rivals, and often exceeds, the parameterized contribution.
Because the internal features of mesoscale convective systems are poorly resolved by meso-β-scale grid elements in this simulation, their scale tends to be overestimated. However, the model results and observations suggest that models must account for multiscale convective overturning in order to properly characterize convective mass transports. Therefore, it is argued that the manner of representation of the convective process, wherein deep convection is allowed to occur partly as a parameterized subgrid-scale process and partly as a hydrostatic manifestation of convective overturning, is likely to give the optimal numerical solution for modeling systems with meso-β-scale resolution.
Abstract
An analysis of how parameterized convection interacts with hydrostatic, explicitly resolved precipitation processes to represent multiscale convective overturning in a mesoscale-resolution numerical simulation is presented. Critically important ingredients of the successful simulation are identified and the degree to which simulations are consistent with observations and theoretical considerations is examined. Of particular concern is how convective parameterization routines reconcile the deep moist absolutely unstable structures that form in mesoscale convective systems.
It is found that these structures arise primarily from resolvable-scale vertical motion and that the model responds to these structures not only by maintaining parameterized convection, but also by developing a hydrostatic manifestation of convective overturning on its smallest resolvable horizontal scales. The strongest and most distinctive mesoscale perturbations develop in regions where the mesoscale contribution to convective overturning rivals, and often exceeds, the parameterized contribution.
Because the internal features of mesoscale convective systems are poorly resolved by meso-β-scale grid elements in this simulation, their scale tends to be overestimated. However, the model results and observations suggest that models must account for multiscale convective overturning in order to properly characterize convective mass transports. Therefore, it is argued that the manner of representation of the convective process, wherein deep convection is allowed to occur partly as a parameterized subgrid-scale process and partly as a hydrostatic manifestation of convective overturning, is likely to give the optimal numerical solution for modeling systems with meso-β-scale resolution.
Abstract
A new one-dimensional cloud model, specifically designed for application in mesoscale convective parameterization schemes (CPSs), is introduced. The model is unique in its representation of environmental entrainment and updraft detrainment rates. In particular, the two-way exchange of mass between clouds and their environment is modulated at each vertical level by a buoyancy sorting mechanism at the interface of clear and cloudy air. The new entrainment/detrainment scheme allows vertical profiles of both updraft moisture detrainment and updraft vertical mass flux to vary in a physically realistic way as a function of the cloud-scale environment. These performance characteristics allow the parameterized vertical distribution of convective heating and drying to be much more responsive to environmental conditions than is possible with a traditional one-dimensional entraining plume model.
The sensitivities of the new model to variations in environmental convective available potential energy and vertical moisture distribution in idealized convective environments are demonstrated and its sensitivities to several key control parameters are examined. Finally, the performance of the new model in the Fritsch–Chappell CPS is evaluated. Parameterized heating and drying profiles are elucidated as they relate to the convective environment and to the type of cloud model used in the CPS.
Abstract
A new one-dimensional cloud model, specifically designed for application in mesoscale convective parameterization schemes (CPSs), is introduced. The model is unique in its representation of environmental entrainment and updraft detrainment rates. In particular, the two-way exchange of mass between clouds and their environment is modulated at each vertical level by a buoyancy sorting mechanism at the interface of clear and cloudy air. The new entrainment/detrainment scheme allows vertical profiles of both updraft moisture detrainment and updraft vertical mass flux to vary in a physically realistic way as a function of the cloud-scale environment. These performance characteristics allow the parameterized vertical distribution of convective heating and drying to be much more responsive to environmental conditions than is possible with a traditional one-dimensional entraining plume model.
The sensitivities of the new model to variations in environmental convective available potential energy and vertical moisture distribution in idealized convective environments are demonstrated and its sensitivities to several key control parameters are examined. Finally, the performance of the new model in the Fritsch–Chappell CPS is evaluated. Parameterized heating and drying profiles are elucidated as they relate to the convective environment and to the type of cloud model used in the CPS.
Abstract
A convectively generated mesoscale vortex that was instrumental in initiating and organizing five successive mesoscale convective systems over a period of three days is documented. Two of these convective systems were especially intense and resulted in widespread heavy rain with localized flooding. Based upon radar and satellite data, the detectable size of the vortex became much larger following the strong convective developments, nearly tripling its initial diameter over its three-day life cycle. During nighttime, when convection typically intensified within the vortex, movement of the system tended to slow. Following dissipation of the convection in the morning, the daytime movement accelerated.
Cross sections of potential vorticity taken through the vortex center clearly show a maximum at midlevels and a well-defined minimum directly above. The vortex and the potential vorticity maximum were essentially colocated and the system was nearly axisymmetric in the vertical. Over the three-day life cycle of the system, the strength of the vortex, as measured by the magnitude of the midlevel potential vorticity maximum, steadily increased.
At low levels, isentropic surfaces sloped upward from the rear of the potential vorticity anomaly into the vortex center so that relatively fast-moving low-level southwesterly flow, which was overtaking the slow-moving vortex from the rear, ascended as it approached the vortex center. Computations of the magnitude and duration of the ascent indicate that the lifting was sufficient to initiate new convection only if parcels realized the maximum possible ascent by flowing into the innermost region of the vortex circulation. In support of this interpretation, satellite observations show that new convection repeatedly developed near the vortex center instead of along well-defined surface outflow boundaries that encircled the convective system. A conceptual model describing the redevelopment mechanism is presented.
Analyses of the large-scale environment of the vortex show that it formed and persisted in a deep and broad zone of southwesterly flow just upstream of a synoptic-scale ridge. At tropopause levels, a large anticyclone covered the region. Potential buoyant energy in the vortex environment typically ranged from about 1000 J kg−1 at 1200 UTC to 1900 J kg−1 at 0000 UTC. Extreme values were as large as 3500 J kg−1. Except for a low-level jet, wind speed and vertical wind shear were relatively small throughout the troposphere, especially in the vortex-bearing layer (700–300 mb) where shear values were only about 0.8 × 10−3 s−1. The deep midlevel layer of weak shear provided a favorable environment for the formation and persistence of the nearly axisymmetric vertical disturbance.
Since the vortex formed and grew over land, this study demonstrates that warm-core mesovortex genesis and amplification do not require heat and moisture fluxes from a tropical marine surface. Evidently, ambient CAPE is sufficient for vortex formation and limited growth. However, since the vortex growth primarily occurred in the middle troposphere, and since anticyclonic outflow was usually present at the surface, marine surface fluxes may be necessary for transformation of such convectively generated vortices into surface-based tropical disturbances.
Abstract
A convectively generated mesoscale vortex that was instrumental in initiating and organizing five successive mesoscale convective systems over a period of three days is documented. Two of these convective systems were especially intense and resulted in widespread heavy rain with localized flooding. Based upon radar and satellite data, the detectable size of the vortex became much larger following the strong convective developments, nearly tripling its initial diameter over its three-day life cycle. During nighttime, when convection typically intensified within the vortex, movement of the system tended to slow. Following dissipation of the convection in the morning, the daytime movement accelerated.
Cross sections of potential vorticity taken through the vortex center clearly show a maximum at midlevels and a well-defined minimum directly above. The vortex and the potential vorticity maximum were essentially colocated and the system was nearly axisymmetric in the vertical. Over the three-day life cycle of the system, the strength of the vortex, as measured by the magnitude of the midlevel potential vorticity maximum, steadily increased.
At low levels, isentropic surfaces sloped upward from the rear of the potential vorticity anomaly into the vortex center so that relatively fast-moving low-level southwesterly flow, which was overtaking the slow-moving vortex from the rear, ascended as it approached the vortex center. Computations of the magnitude and duration of the ascent indicate that the lifting was sufficient to initiate new convection only if parcels realized the maximum possible ascent by flowing into the innermost region of the vortex circulation. In support of this interpretation, satellite observations show that new convection repeatedly developed near the vortex center instead of along well-defined surface outflow boundaries that encircled the convective system. A conceptual model describing the redevelopment mechanism is presented.
Analyses of the large-scale environment of the vortex show that it formed and persisted in a deep and broad zone of southwesterly flow just upstream of a synoptic-scale ridge. At tropopause levels, a large anticyclone covered the region. Potential buoyant energy in the vortex environment typically ranged from about 1000 J kg−1 at 1200 UTC to 1900 J kg−1 at 0000 UTC. Extreme values were as large as 3500 J kg−1. Except for a low-level jet, wind speed and vertical wind shear were relatively small throughout the troposphere, especially in the vortex-bearing layer (700–300 mb) where shear values were only about 0.8 × 10−3 s−1. The deep midlevel layer of weak shear provided a favorable environment for the formation and persistence of the nearly axisymmetric vertical disturbance.
Since the vortex formed and grew over land, this study demonstrates that warm-core mesovortex genesis and amplification do not require heat and moisture fluxes from a tropical marine surface. Evidently, ambient CAPE is sufficient for vortex formation and limited growth. However, since the vortex growth primarily occurred in the middle troposphere, and since anticyclonic outflow was usually present at the surface, marine surface fluxes may be necessary for transformation of such convectively generated vortices into surface-based tropical disturbances.
Abstract
Convection-allowing configurations of the Weather Research and Forecast (WRF) model were evaluated during the 2004 Storm Prediction Center–National Severe Storms Laboratory Spring Program in a simulated severe weather forecasting environment. The utility of the WRF forecasts was assessed in two different ways. First, WRF output was used in the preparation of daily experimental human forecasts for severe weather. These forecasts were compared with corresponding predictions made without access to WRF data to provide a measure of the impact of the experimental data on the human decision-making process. Second, WRF output was compared directly with output from current operational forecast models. Results indicate that human forecasts showed a small, but measurable, improvement when forecasters had access to the high-resolution WRF output and, in the mean, the WRF output received higher ratings than the operational Eta Model on subjective performance measures related to convective initiation, evolution, and mode. The results suggest that convection-allowing models have the potential to provide a value-added benefit to the traditional guidance package used by severe weather forecasters.
Abstract
Convection-allowing configurations of the Weather Research and Forecast (WRF) model were evaluated during the 2004 Storm Prediction Center–National Severe Storms Laboratory Spring Program in a simulated severe weather forecasting environment. The utility of the WRF forecasts was assessed in two different ways. First, WRF output was used in the preparation of daily experimental human forecasts for severe weather. These forecasts were compared with corresponding predictions made without access to WRF data to provide a measure of the impact of the experimental data on the human decision-making process. Second, WRF output was compared directly with output from current operational forecast models. Results indicate that human forecasts showed a small, but measurable, improvement when forecasters had access to the high-resolution WRF output and, in the mean, the WRF output received higher ratings than the operational Eta Model on subjective performance measures related to convective initiation, evolution, and mode. The results suggest that convection-allowing models have the potential to provide a value-added benefit to the traditional guidance package used by severe weather forecasters.
Abstract
The authors have altered the vertical profile of updraft mass flux detrainment in an implementation of the Kain–Fritsch2 (KF2) convective parameterization within the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (Penn State–NCAR) Mesoscale Model (MM5). The effect of this modification was to alter the vertical profile of convective parameterization cloud mass (including cloud water and ice) supplied to the host model for explicit simulation by the grid-resolved dynamical equations and parameterized microphysical processes. These modifications and their sensitivity to horizontal resolution in a matrix of experimental simulations of the June–July 1993 flood in the central United States were tested.
The KF2 modifications impacted the diurnal cycle of precipitation by reducing precipitation from the convective parameterization and increasing precipitation from more slowly evolving mesoscale processes. The modified KF2 reduced an afternoon bias of high precipitation rate in both low- and high-resolution simulations but affected mesoscale precipitation processes only in high-resolution simulations. The combination of high-resolution and modified KF2 resulted in more frequent and more realistically clustered propagating, nocturnal mesoscale precipitation events and agreed best with observations of the nocturnal precipitation rate.
Abstract
The authors have altered the vertical profile of updraft mass flux detrainment in an implementation of the Kain–Fritsch2 (KF2) convective parameterization within the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (Penn State–NCAR) Mesoscale Model (MM5). The effect of this modification was to alter the vertical profile of convective parameterization cloud mass (including cloud water and ice) supplied to the host model for explicit simulation by the grid-resolved dynamical equations and parameterized microphysical processes. These modifications and their sensitivity to horizontal resolution in a matrix of experimental simulations of the June–July 1993 flood in the central United States were tested.
The KF2 modifications impacted the diurnal cycle of precipitation by reducing precipitation from the convective parameterization and increasing precipitation from more slowly evolving mesoscale processes. The modified KF2 reduced an afternoon bias of high precipitation rate in both low- and high-resolution simulations but affected mesoscale precipitation processes only in high-resolution simulations. The combination of high-resolution and modified KF2 resulted in more frequent and more realistically clustered propagating, nocturnal mesoscale precipitation events and agreed best with observations of the nocturnal precipitation rate.
Abstract
An ensemble of the three-dimensional variational data assimilation (En3DA) method for convective-scale weather has been developed. It consists of an ensemble of three-dimensional variational data assimilations and forecasts in which member differences are introduced by perturbing initial conditions and/or observations, and it uses flow-dependent error covariances generated by the ensemble forecasts. The method is applied to the assimilation of simulated radar data for a supercell storm. Results indicate that the flow-dependent ensemble covariances are effective in enabling convective-scale analyses, as the most important features of the simulated storm, including the low-level cold pool and midlevel mesocyclone, are well analyzed. Several groups of sensitivity experiments are conducted to test the robustness of the method. The first group demonstrates that incorporating a mass continuity equation as a weak constraint into the En3DA algorithm can improve the quality of the analyses when radial velocity observations contain large errors. In the second group of experiments, the sensitivity of analyses to the microphysical parameterization scheme is explored. Results indicate that the En3DA analyses are quite sensitive to differences in the microphysics scheme, suggesting that ensemble forecasts with multiple microphysics schemes could reduce uncertainty related to model physics errors. Experiments also show that assimilating reflectivity observations can reduce spinup time and that it has a small positive impact on the quality of the wind field analysis. Of the threshold values tested for assimilating reflectivity observations, 15 dBZ provides the best analysis. The final group of experiments demonstrates that it is not necessary to perturb radial velocity observations for every ensemble number in order to improve the quality of the analysis.
Abstract
An ensemble of the three-dimensional variational data assimilation (En3DA) method for convective-scale weather has been developed. It consists of an ensemble of three-dimensional variational data assimilations and forecasts in which member differences are introduced by perturbing initial conditions and/or observations, and it uses flow-dependent error covariances generated by the ensemble forecasts. The method is applied to the assimilation of simulated radar data for a supercell storm. Results indicate that the flow-dependent ensemble covariances are effective in enabling convective-scale analyses, as the most important features of the simulated storm, including the low-level cold pool and midlevel mesocyclone, are well analyzed. Several groups of sensitivity experiments are conducted to test the robustness of the method. The first group demonstrates that incorporating a mass continuity equation as a weak constraint into the En3DA algorithm can improve the quality of the analyses when radial velocity observations contain large errors. In the second group of experiments, the sensitivity of analyses to the microphysical parameterization scheme is explored. Results indicate that the En3DA analyses are quite sensitive to differences in the microphysics scheme, suggesting that ensemble forecasts with multiple microphysics schemes could reduce uncertainty related to model physics errors. Experiments also show that assimilating reflectivity observations can reduce spinup time and that it has a small positive impact on the quality of the wind field analysis. Of the threshold values tested for assimilating reflectivity observations, 15 dBZ provides the best analysis. The final group of experiments demonstrates that it is not necessary to perturb radial velocity observations for every ensemble number in order to improve the quality of the analysis.
Abstract
Parameterized updraft mass flux, available as a unique predictive field from the Kain–Fritsch (KF) convective parameterization, is presented as a potentially valuable predictor of convective intensity. The KF scheme is described in some detail, focusing on a version that is currently being run semioperationally in an experimental version of the Eta Model. It is shown that updraft mass flux computed by this scheme is a function of the specific algorithm that it utilizes and is very sensitive to the thermodynamic characteristics of input soundings. These same characteristics appear to be related to the severity of convection, suggesting that updraft mass flux predicted by the KF scheme has value for predicting severe weather. This argument is supported by anecdotal evidence and a case study.
Abstract
Parameterized updraft mass flux, available as a unique predictive field from the Kain–Fritsch (KF) convective parameterization, is presented as a potentially valuable predictor of convective intensity. The KF scheme is described in some detail, focusing on a version that is currently being run semioperationally in an experimental version of the Eta Model. It is shown that updraft mass flux computed by this scheme is a function of the specific algorithm that it utilizes and is very sensitive to the thermodynamic characteristics of input soundings. These same characteristics appear to be related to the severity of convection, suggesting that updraft mass flux predicted by the KF scheme has value for predicting severe weather. This argument is supported by anecdotal evidence and a case study.
Abstract
A continuous-update-cycle storm-scale ensemble data assimilation (DA) and prediction system using the ARW model and DART software is used to generate retrospective 0–6-h ensemble forecasts of the 31 May 2013 tornado and flash flood event over central Oklahoma, with a focus on the prediction of heavy rainfall. Results indicate that the model-predicted probabilities of strong low-level mesocyclones correspond well with the locations of observed mesocyclones and with the observed damage track. The ensemble-mean quantitative precipitation forecast (QPF) from the radar DA experiments match NCEP’s stage IV analyses reasonably well in terms of location and amount of rainfall, particularly during the 0–3-h forecast period. In contrast, significant displacement errors and lower rainfall totals are evident in a control experiment that withholds radar data during the DA. The ensemble-derived probabilistic QPF (PQPF) from the radar DA experiment is more skillful than the PQPF from the no_radar experiment, based on visual inspection and probabilistic verification metrics. A novel object-based storm-tracking algorithm provides additional insight, suggesting that explicit assimilation and 1–2-h prediction of the dominant supercell is remarkably skillful in the radar experiment. The skill in both experiments is substantially higher during the 0–3-h forecast period than in the 3–6-h period. Furthermore, the difference in skill between the two forecasts decreases sharply during the latter period, indicating that the impact of radar DA is greatest during early forecast hours. Overall, the results demonstrate the potential for a frequently updated, high-resolution ensemble system to extend probabilistic low-level mesocyclone and flash flood forecast lead times and improve accuracy of convective precipitation nowcasting.
Abstract
A continuous-update-cycle storm-scale ensemble data assimilation (DA) and prediction system using the ARW model and DART software is used to generate retrospective 0–6-h ensemble forecasts of the 31 May 2013 tornado and flash flood event over central Oklahoma, with a focus on the prediction of heavy rainfall. Results indicate that the model-predicted probabilities of strong low-level mesocyclones correspond well with the locations of observed mesocyclones and with the observed damage track. The ensemble-mean quantitative precipitation forecast (QPF) from the radar DA experiments match NCEP’s stage IV analyses reasonably well in terms of location and amount of rainfall, particularly during the 0–3-h forecast period. In contrast, significant displacement errors and lower rainfall totals are evident in a control experiment that withholds radar data during the DA. The ensemble-derived probabilistic QPF (PQPF) from the radar DA experiment is more skillful than the PQPF from the no_radar experiment, based on visual inspection and probabilistic verification metrics. A novel object-based storm-tracking algorithm provides additional insight, suggesting that explicit assimilation and 1–2-h prediction of the dominant supercell is remarkably skillful in the radar experiment. The skill in both experiments is substantially higher during the 0–3-h forecast period than in the 3–6-h period. Furthermore, the difference in skill between the two forecasts decreases sharply during the latter period, indicating that the impact of radar DA is greatest during early forecast hours. Overall, the results demonstrate the potential for a frequently updated, high-resolution ensemble system to extend probabilistic low-level mesocyclone and flash flood forecast lead times and improve accuracy of convective precipitation nowcasting.
Abstract
Since the early 2000s, growing computing resources for numerical weather prediction (NWP) and scientific advances enabled development and testing of experimental, real-time deterministic convection-allowing models (CAMs). By the late 2000s, continued advancements spurred development of CAM ensemble forecast systems, through which a broad range of successful forecasting applications have been demonstrated. This work has prepared the National Weather Service (NWS) for practical usage of the High Resolution Ensemble Forecast (HREF) system, which was implemented operationally in November 2017. Historically, methods for postprocessing and visualizing products from regional and global ensemble prediction systems (e.g., ensemble means and spaghetti plots) have been applied to fields that provide information on mesoscale to synoptic-scale processes. However, much of the value from CAMs is derived from the explicit simulation of deep convection and associated storm-attribute fields like updraft helicity and simulated reflectivity. Thus, fully exploiting CAM ensembles for forecasting applications has required the development of fundamentally new data extraction, postprocessing, and visualization strategies. In the process, challenges imposed by the immense data volume inherent to these systems required new approaches when considering diverse factors like forecaster interpretation and computational expense. In this article, we review the current state of postprocessing and visualization for CAM ensembles, with a particular focus on forecast applications for severe convective hazards that have been evaluated within NOAA’s Hazardous Weather Testbed. The HREF web viewer implemented at the NWS Storm Prediction Center (SPC) is presented as a prototype for deploying these techniques in real time on a flexible and widely accessible platform.
Abstract
Since the early 2000s, growing computing resources for numerical weather prediction (NWP) and scientific advances enabled development and testing of experimental, real-time deterministic convection-allowing models (CAMs). By the late 2000s, continued advancements spurred development of CAM ensemble forecast systems, through which a broad range of successful forecasting applications have been demonstrated. This work has prepared the National Weather Service (NWS) for practical usage of the High Resolution Ensemble Forecast (HREF) system, which was implemented operationally in November 2017. Historically, methods for postprocessing and visualizing products from regional and global ensemble prediction systems (e.g., ensemble means and spaghetti plots) have been applied to fields that provide information on mesoscale to synoptic-scale processes. However, much of the value from CAMs is derived from the explicit simulation of deep convection and associated storm-attribute fields like updraft helicity and simulated reflectivity. Thus, fully exploiting CAM ensembles for forecasting applications has required the development of fundamentally new data extraction, postprocessing, and visualization strategies. In the process, challenges imposed by the immense data volume inherent to these systems required new approaches when considering diverse factors like forecaster interpretation and computational expense. In this article, we review the current state of postprocessing and visualization for CAM ensembles, with a particular focus on forecast applications for severe convective hazards that have been evaluated within NOAA’s Hazardous Weather Testbed. The HREF web viewer implemented at the NWS Storm Prediction Center (SPC) is presented as a prototype for deploying these techniques in real time on a flexible and widely accessible platform.
Abstract
A three-dimensional (in space and time) object identification algorithm is applied to high-resolution forecasts of hourly maximum updraft helicity (UH)—a diagnostic that identifies simulated rotating storms—with the goal of diagnosing the relationship between forecast UH objects and observed tornado pathlengths. UH objects are contiguous swaths of UH exceeding a specified threshold. Including time allows tracks to span multiple hours and entire life cycles of simulated rotating storms. The object algorithm is applied to 3 yr of 36-h forecasts initialized daily from a 4-km grid-spacing version of the Weather Research and Forecasting Model (WRF) run in real time at the National Severe Storms Laboratory (NSSL), and forecasts from the Storm Scale Ensemble Forecast (SSEF) system run by the Center for Analysis and Prediction of Storms for the 2010 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. Methods for visualizing UH object attributes are presented, and the relationship between pathlengths of UH objects and tornadoes for corresponding 18- or 24-h periods is examined. For deterministic NSSL-WRF UH forecasts, the relationship of UH pathlengths to tornadoes was much stronger during spring (March–May) than in summer (June–August). Filtering UH track segments produced by high-based and/or elevated storms improved the UH–tornado pathlength correlations. The best ensemble results were obtained after filtering high-based and/or elevated UH track segments for the 20 cases in April–May 2010, during which correlation coefficients were as high as 0.91. The results indicate that forecast UH pathlengths during spring could be a very skillful predictor for the severity of tornado outbreaks as measured by total pathlength.
Abstract
A three-dimensional (in space and time) object identification algorithm is applied to high-resolution forecasts of hourly maximum updraft helicity (UH)—a diagnostic that identifies simulated rotating storms—with the goal of diagnosing the relationship between forecast UH objects and observed tornado pathlengths. UH objects are contiguous swaths of UH exceeding a specified threshold. Including time allows tracks to span multiple hours and entire life cycles of simulated rotating storms. The object algorithm is applied to 3 yr of 36-h forecasts initialized daily from a 4-km grid-spacing version of the Weather Research and Forecasting Model (WRF) run in real time at the National Severe Storms Laboratory (NSSL), and forecasts from the Storm Scale Ensemble Forecast (SSEF) system run by the Center for Analysis and Prediction of Storms for the 2010 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. Methods for visualizing UH object attributes are presented, and the relationship between pathlengths of UH objects and tornadoes for corresponding 18- or 24-h periods is examined. For deterministic NSSL-WRF UH forecasts, the relationship of UH pathlengths to tornadoes was much stronger during spring (March–May) than in summer (June–August). Filtering UH track segments produced by high-based and/or elevated storms improved the UH–tornado pathlength correlations. The best ensemble results were obtained after filtering high-based and/or elevated UH track segments for the 20 cases in April–May 2010, during which correlation coefficients were as high as 0.91. The results indicate that forecast UH pathlengths during spring could be a very skillful predictor for the severity of tornado outbreaks as measured by total pathlength.
