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Abstract
The Warn-on-Forecast system (WoFS) provides short-term, probabilistic forecasts of severe convective hazards including tornadoes, hail, and damaging winds. WoFS initial conditions are created through frequent assimilation of radar (reflectivity and radial velocity), satellite, and in situ observations. From 2016 to 2018, 5-km radial velocity Cressman superob analyses were created to reduce the observation counts and subsequent assimilation computational costs. The superobbing procedure smooths the radial velocity and subsequently fails to accurately depict important storm-scale features such as mesocyclones. This study retrospectively assimilates denser, 3-km radial velocity analyses in lieu of the 5-km analyses for eight case studies during the spring of 2018. Although there are forecast improvements during and shortly after convection initiation, 3-km analyses negatively impact forecasts initialized when convection is ongoing, as evidenced by model failure and initiation of spurious convection. Therefore, two additional experiments are performed using adaptive assimilation of 3-km radial velocity observations. Initially, an updraft variance mask is applied that limits radial velocity assimilation to areas where the observations are more likely to be beneficial. This experiment reduces spurious convection as well as the number of observations assimilated, in some cases even below that of the 5-km analysis experiments. The masking, however, eliminates an advantage of 3-km radial velocity assimilation for convection initiation timing. This problem is mitigated by additionally assimilating 3-km radial velocity observations in locations where large differences exist between the observed and ensemble-mean reflectivity fields, which retains the benefits of the denser radial velocity analyses while reducing the number of observations assimilated.
Abstract
The Warn-on-Forecast system (WoFS) provides short-term, probabilistic forecasts of severe convective hazards including tornadoes, hail, and damaging winds. WoFS initial conditions are created through frequent assimilation of radar (reflectivity and radial velocity), satellite, and in situ observations. From 2016 to 2018, 5-km radial velocity Cressman superob analyses were created to reduce the observation counts and subsequent assimilation computational costs. The superobbing procedure smooths the radial velocity and subsequently fails to accurately depict important storm-scale features such as mesocyclones. This study retrospectively assimilates denser, 3-km radial velocity analyses in lieu of the 5-km analyses for eight case studies during the spring of 2018. Although there are forecast improvements during and shortly after convection initiation, 3-km analyses negatively impact forecasts initialized when convection is ongoing, as evidenced by model failure and initiation of spurious convection. Therefore, two additional experiments are performed using adaptive assimilation of 3-km radial velocity observations. Initially, an updraft variance mask is applied that limits radial velocity assimilation to areas where the observations are more likely to be beneficial. This experiment reduces spurious convection as well as the number of observations assimilated, in some cases even below that of the 5-km analysis experiments. The masking, however, eliminates an advantage of 3-km radial velocity assimilation for convection initiation timing. This problem is mitigated by additionally assimilating 3-km radial velocity observations in locations where large differences exist between the observed and ensemble-mean reflectivity fields, which retains the benefits of the denser radial velocity analyses while reducing the number of observations assimilated.
Abstract
The Storm Prediction Center (SPC) tornado database, generated from NCEI’s Storm Data publication, is indispensable for assessing U.S. tornado risk and investigating tornado–climate connections. Maximizing the value of this database, however, requires accounting for systemically lower reported tornado counts in rural areas owing to a lack of observers. This study uses Bayesian hierarchical modeling to estimate tornado reporting rates and expected tornado counts over the central United States during 1975–2016. Our method addresses a serious solution nonuniqueness issue that may have affected previous studies. The adopted model explains 73% (>90%) of the variance in reported counts at scales of 50 km (>100 km). Population density explains more of the variance in reported tornado counts than other examined geographical covariates, including distance from nearest city, terrain ruggedness index, and road density. The model estimates that approximately 45% of tornadoes within the analysis domain were reported. The estimated tornado reporting rate decreases sharply away from population centers; for example, while >90% of tornadoes that occur within 5 km of a city with population > 100 000 are reported, this rate decreases to <70% at distances of 20–25 km. The method is directly extendable to other events subject to underreporting (e.g., severe hail and wind) and could be used to improve climate studies and tornado and other hazard models for forecasters, planners, and insurance/reinsurance companies, as well as for the development and verification of storm-scale prediction systems.
Abstract
The Storm Prediction Center (SPC) tornado database, generated from NCEI’s Storm Data publication, is indispensable for assessing U.S. tornado risk and investigating tornado–climate connections. Maximizing the value of this database, however, requires accounting for systemically lower reported tornado counts in rural areas owing to a lack of observers. This study uses Bayesian hierarchical modeling to estimate tornado reporting rates and expected tornado counts over the central United States during 1975–2016. Our method addresses a serious solution nonuniqueness issue that may have affected previous studies. The adopted model explains 73% (>90%) of the variance in reported counts at scales of 50 km (>100 km). Population density explains more of the variance in reported tornado counts than other examined geographical covariates, including distance from nearest city, terrain ruggedness index, and road density. The model estimates that approximately 45% of tornadoes within the analysis domain were reported. The estimated tornado reporting rate decreases sharply away from population centers; for example, while >90% of tornadoes that occur within 5 km of a city with population > 100 000 are reported, this rate decreases to <70% at distances of 20–25 km. The method is directly extendable to other events subject to underreporting (e.g., severe hail and wind) and could be used to improve climate studies and tornado and other hazard models for forecasters, planners, and insurance/reinsurance companies, as well as for the development and verification of storm-scale prediction systems.
Abstract
During the 2017 Spring Forecasting Experiment in NOAA’s Hazardous Weather Testbed, 62 meteorologists completed a survey designed to test their understanding of forecast uncertainty. Survey questions were based on probabilistic forecast guidance provided by the NSSL Experimental Warn-on-Forecast System for ensembles (NEWS-e). A mix of 20 multiple-choice and open-ended questions required participants to explain basic probability and percentile concepts, extract information using graphical representations of uncertainty, and determine what type of weather scenario the graphics depicted. Multiple-choice questions were analyzed using frequency counts, and open-ended questions were analyzed using thematic coding methods. Of the 18 questions that could be scored, 60%–96% of the participants’ responses aligned with the researchers’ intended response. Some of the most challenging questions proved to be those requiring qualitative explanations, such as to explain what the 70th-percentile value of accumulated rainfall represents in an ensemble-based probabilistic forecast. Additionally, participants providing answers not aligning with the intended response oftentimes appeared to consider the given information with a deterministic rather than probabilistic mindset. Applications of a deterministic mindset resulted in tendencies to focus on the worst-case scenario and to modify understanding of probabilistic concepts when presented with different variables. The findings from this survey support the need for improved basic and applied training for the development, interpretation, and use of probabilistic ensemble forecast guidance. Future work should collect data for a larger sample size to examine the knowledge gaps across specific user groups and to guide development of probabilistic forecast training tools.
Abstract
During the 2017 Spring Forecasting Experiment in NOAA’s Hazardous Weather Testbed, 62 meteorologists completed a survey designed to test their understanding of forecast uncertainty. Survey questions were based on probabilistic forecast guidance provided by the NSSL Experimental Warn-on-Forecast System for ensembles (NEWS-e). A mix of 20 multiple-choice and open-ended questions required participants to explain basic probability and percentile concepts, extract information using graphical representations of uncertainty, and determine what type of weather scenario the graphics depicted. Multiple-choice questions were analyzed using frequency counts, and open-ended questions were analyzed using thematic coding methods. Of the 18 questions that could be scored, 60%–96% of the participants’ responses aligned with the researchers’ intended response. Some of the most challenging questions proved to be those requiring qualitative explanations, such as to explain what the 70th-percentile value of accumulated rainfall represents in an ensemble-based probabilistic forecast. Additionally, participants providing answers not aligning with the intended response oftentimes appeared to consider the given information with a deterministic rather than probabilistic mindset. Applications of a deterministic mindset resulted in tendencies to focus on the worst-case scenario and to modify understanding of probabilistic concepts when presented with different variables. The findings from this survey support the need for improved basic and applied training for the development, interpretation, and use of probabilistic ensemble forecast guidance. Future work should collect data for a larger sample size to examine the knowledge gaps across specific user groups and to guide development of probabilistic forecast training tools.
Abstract
Cyclic mesocyclogenesis is the process by which a supercell produces multiple mesocyclones with similar life cycles. The frequency of cyclic mesocyclogenesis has been linked to tornado potential, with higher frequencies decreasing the potential for tornadogenesis. Thus, the ability to predict the presence and frequency of cycling in supercells may be beneficial to forecasters for assessing tornado potential. However, idealized simulations of cyclic mesocyclogenesis have found it to be highly sensitive to environmental and computational parameters. Thus, whether convective-allowing models can resolve and predict cycling has yet to be determined. This study tests the capability of a storm-scale, ensemble prediction system to resolve the cycling process and predict its frequency. Forecasts for three cyclic supercells occurring in May 2017 are generated by NSSL’s Warn-on-Forecast System (WoFS) using 3- and 1-km grid spacing. Rare cases of cyclic-like processes were identified at 3 km, but cycling occurred more frequently at 1 km. WoFS predicted variation in cycling frequencies for the storms that were similar to observed variations in frequency. Object-based identification of mesocyclones was used to extract environmental parameters from a storm-relative inflow sector from each mesocyclone. Lower magnitudes of 0–1-km storm-relative helicity and significant tornado parameter are present for the two more frequently cycling supercells, and higher values are present for the case with the fewest cycles. These results provide initial evidence that high-resolution ensemble forecasts can potentially provide useful guidance on the likelihood and cycling frequency of cyclic supercells.
Abstract
Cyclic mesocyclogenesis is the process by which a supercell produces multiple mesocyclones with similar life cycles. The frequency of cyclic mesocyclogenesis has been linked to tornado potential, with higher frequencies decreasing the potential for tornadogenesis. Thus, the ability to predict the presence and frequency of cycling in supercells may be beneficial to forecasters for assessing tornado potential. However, idealized simulations of cyclic mesocyclogenesis have found it to be highly sensitive to environmental and computational parameters. Thus, whether convective-allowing models can resolve and predict cycling has yet to be determined. This study tests the capability of a storm-scale, ensemble prediction system to resolve the cycling process and predict its frequency. Forecasts for three cyclic supercells occurring in May 2017 are generated by NSSL’s Warn-on-Forecast System (WoFS) using 3- and 1-km grid spacing. Rare cases of cyclic-like processes were identified at 3 km, but cycling occurred more frequently at 1 km. WoFS predicted variation in cycling frequencies for the storms that were similar to observed variations in frequency. Object-based identification of mesocyclones was used to extract environmental parameters from a storm-relative inflow sector from each mesocyclone. Lower magnitudes of 0–1-km storm-relative helicity and significant tornado parameter are present for the two more frequently cycling supercells, and higher values are present for the case with the fewest cycles. These results provide initial evidence that high-resolution ensemble forecasts can potentially provide useful guidance on the likelihood and cycling frequency of cyclic supercells.
Abstract
Many tornadoes are unreported because of lack of observers or are underrated in intensity, width, or track length because of lack of damage indicators. These reporting biases substantially degrade estimates of tornado frequency and thereby undermine important endeavors such as studies of climate impacts on tornadoes and cost–benefit analyses of tornado damage mitigation. Building on previous studies, we use a Bayesian hierarchical modeling framework to estimate and correct for tornado reporting biases over the central United States during 1975–2018. The reporting biases are treated as a univariate function of population density. We assess how these biases vary with tornado intensity, width, and track length and over the analysis period. We find that the frequencies of tornadoes of all kinds, but especially stronger or wider tornadoes, have been substantially underestimated. Most strikingly, the Bayesian model estimates that there have been approximately 3 times as many tornadoes capable of (E)F2+ damage as have been recorded as (E)F2+ [(E)F indicates a rating on the (enhanced) Fujita scale]. The model estimates that total tornado frequency changed little over the analysis period. Statistically significant trends in frequency are found for tornadoes within certain ranges of intensity, pathlength, and width, but it is unclear what proportion of these trends arise from changes in damage survey practices. Simple analyses of the tornado database corroborate many of the inferences from the Bayesian model.
Significance Statement
Prior studies have shown that the probabilities of a tornado being reported and of its intensity, track length, and width being accurately estimated are strongly correlated with the local population density. We have developed a sophisticated statistical model that accounts for these population-dependent tornado reporting biases to improve estimates of tornado frequency in the central United States. The bias-corrected tornado frequency estimates differ markedly from the official tornado climatology and have important implications for tornado risk assessment, damage mitigation, and studies of climate change impacts on tornado activity.
Abstract
Many tornadoes are unreported because of lack of observers or are underrated in intensity, width, or track length because of lack of damage indicators. These reporting biases substantially degrade estimates of tornado frequency and thereby undermine important endeavors such as studies of climate impacts on tornadoes and cost–benefit analyses of tornado damage mitigation. Building on previous studies, we use a Bayesian hierarchical modeling framework to estimate and correct for tornado reporting biases over the central United States during 1975–2018. The reporting biases are treated as a univariate function of population density. We assess how these biases vary with tornado intensity, width, and track length and over the analysis period. We find that the frequencies of tornadoes of all kinds, but especially stronger or wider tornadoes, have been substantially underestimated. Most strikingly, the Bayesian model estimates that there have been approximately 3 times as many tornadoes capable of (E)F2+ damage as have been recorded as (E)F2+ [(E)F indicates a rating on the (enhanced) Fujita scale]. The model estimates that total tornado frequency changed little over the analysis period. Statistically significant trends in frequency are found for tornadoes within certain ranges of intensity, pathlength, and width, but it is unclear what proportion of these trends arise from changes in damage survey practices. Simple analyses of the tornado database corroborate many of the inferences from the Bayesian model.
Significance Statement
Prior studies have shown that the probabilities of a tornado being reported and of its intensity, track length, and width being accurately estimated are strongly correlated with the local population density. We have developed a sophisticated statistical model that accounts for these population-dependent tornado reporting biases to improve estimates of tornado frequency in the central United States. The bias-corrected tornado frequency estimates differ markedly from the official tornado climatology and have important implications for tornado risk assessment, damage mitigation, and studies of climate change impacts on tornado activity.
Abstract
Observational data collection is extremely hazardous in supercell storm environments, which makes for a scarcity of data used for evaluating the storm-scale guidance from convection allowing models (CAMs) like the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast System (WoFS). The Targeted Observations with UAS and Radar of Supercells (TORUS) 2019 field mission provided a rare opportunity to not only collect these observations, but to do so with advanced technology: vertically pointing Doppler lidar. One standing question for WoFS is how the system forecasts the feedback between supercells and their near-storm environment. The lidar can observe vertical profiles of wind over time, creating unique datasets to compare to WoFS kinematic predictions in rapidly evolving severe weather environments. Mobile radiosonde data are also presented to provide a thermodynamic comparison. The five lidar deployments (three of which observed tornadic supercells) analyzed show WoFS accurately predicted general kinematic trends in the inflow environment; however, the predicted feedback between the supercell and its environment, which resulted in enhanced inflow and larger storm-relative helicity (SRH), were muted relative to observations. The radiosonde observations reveal an overprediction of CAPE in WoFS forecasts, both in the near and far field, with an inverse relationship between the CAPE errors and distance from the storm.
Significance Statement
It is difficult to evaluate the accuracy of weather prediction model forecasts of severe thunderstorms because observations are rarely available near the storms. However, the TORUS 2019 field experiment collected multiple specialized observations in the near-storm environment of supercells, which are compared to the same near-storm environments predicted by the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast System (WoFS) to gauge its performance. Unique to this study is the use of mobile Doppler lidar observations in the evaluation; lidar can retrieve the horizontal winds in the few kilometers above ground on time scales of a few minutes. Using lidar and radiosonde observations in the near-storm environment of three tornadic supercells, we find that WoFS generally predicts the expected trends in the evolution of the near-storm wind profile, but the response is muted compared to observations. We also find an inverse relationship of errors in instability to distance from the storm. These results can aid model developers in refining model physics to better predict severe storms.
Abstract
Observational data collection is extremely hazardous in supercell storm environments, which makes for a scarcity of data used for evaluating the storm-scale guidance from convection allowing models (CAMs) like the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast System (WoFS). The Targeted Observations with UAS and Radar of Supercells (TORUS) 2019 field mission provided a rare opportunity to not only collect these observations, but to do so with advanced technology: vertically pointing Doppler lidar. One standing question for WoFS is how the system forecasts the feedback between supercells and their near-storm environment. The lidar can observe vertical profiles of wind over time, creating unique datasets to compare to WoFS kinematic predictions in rapidly evolving severe weather environments. Mobile radiosonde data are also presented to provide a thermodynamic comparison. The five lidar deployments (three of which observed tornadic supercells) analyzed show WoFS accurately predicted general kinematic trends in the inflow environment; however, the predicted feedback between the supercell and its environment, which resulted in enhanced inflow and larger storm-relative helicity (SRH), were muted relative to observations. The radiosonde observations reveal an overprediction of CAPE in WoFS forecasts, both in the near and far field, with an inverse relationship between the CAPE errors and distance from the storm.
Significance Statement
It is difficult to evaluate the accuracy of weather prediction model forecasts of severe thunderstorms because observations are rarely available near the storms. However, the TORUS 2019 field experiment collected multiple specialized observations in the near-storm environment of supercells, which are compared to the same near-storm environments predicted by the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast System (WoFS) to gauge its performance. Unique to this study is the use of mobile Doppler lidar observations in the evaluation; lidar can retrieve the horizontal winds in the few kilometers above ground on time scales of a few minutes. Using lidar and radiosonde observations in the near-storm environment of three tornadic supercells, we find that WoFS generally predicts the expected trends in the evolution of the near-storm wind profile, but the response is muted compared to observations. We also find an inverse relationship of errors in instability to distance from the storm. These results can aid model developers in refining model physics to better predict severe storms.
Abstract
In situ data collected within a weakly tornadic, high-precipitation supercell occurring on 23 May 2007 near Perryton, Texas, are presented. Data were collected using a recently developed fleet of 22 durable, rapidly deployable probes dubbed “StickNet” as well as four mobile mesonet probes. Kinematic and thermodynamic observations of boundaries within the supercell are described in tandem with an analysis of data from the Shared Mobile Atmospheric Research and Teaching Radar.
Observations within the rear-flank downdraft of the storm exhibit large deficits of both virtual potential temperature and equivalent potential temperature, with a secondary rear-flank downdraft gust front trailing the mesocyclone. A primarily thermodynamic boundary resided across the forward-flank reflectivity gradient of the supercell. This boundary is characterized by small deficits in virtual potential temperature coupled with positive perturbations of equivalent potential temperature. The opposing thermodynamic perturbations appear to be representative of modified storm inflow, with a flux of water vapor responsible for the positive perturbations of the equivalent potential temperature. Air parcels exhibiting negative perturbations of virtual potential temperature and positive perturbations of equivalent potential temperature have the ability to be a source of both baroclinically generated streamwise horizontal vorticity and greater potential buoyancy if ingested by the low-level mesocyclone.
Abstract
In situ data collected within a weakly tornadic, high-precipitation supercell occurring on 23 May 2007 near Perryton, Texas, are presented. Data were collected using a recently developed fleet of 22 durable, rapidly deployable probes dubbed “StickNet” as well as four mobile mesonet probes. Kinematic and thermodynamic observations of boundaries within the supercell are described in tandem with an analysis of data from the Shared Mobile Atmospheric Research and Teaching Radar.
Observations within the rear-flank downdraft of the storm exhibit large deficits of both virtual potential temperature and equivalent potential temperature, with a secondary rear-flank downdraft gust front trailing the mesocyclone. A primarily thermodynamic boundary resided across the forward-flank reflectivity gradient of the supercell. This boundary is characterized by small deficits in virtual potential temperature coupled with positive perturbations of equivalent potential temperature. The opposing thermodynamic perturbations appear to be representative of modified storm inflow, with a flux of water vapor responsible for the positive perturbations of the equivalent potential temperature. Air parcels exhibiting negative perturbations of virtual potential temperature and positive perturbations of equivalent potential temperature have the ability to be a source of both baroclinically generated streamwise horizontal vorticity and greater potential buoyancy if ingested by the low-level mesocyclone.
Abstract
The forcing and origins of an internal rear-flank downdraft (RFD) momentum surge observed by the second Verification of the Origin of Rotation in Tornadoes Experiment (VORTEX2) within a supercell occurring near Dumas, Texas, on 18 May 2010 is assessed through ensemble Kalman filter (EnKF) storm-scale analyses. EnKF analyses are produced every 2 min from mobile Doppler velocity data collected by the Doppler on Wheels and Shared Mobile Atmospheric Research and Teaching radars, as well as radial velocity and reflectivity data from the KAMA (Amarillo, Texas) WSR-88D. EnKF analyses are found to reproduce the structure and evolution of an internal RFD momentum surge observed in independent mobile Doppler radar observations.
Pressure retrievals of EnKF analyses reveal that the low-level RFD outflow structure is primarily determined through nonlinear dynamic perturbation pressure gradient forcing. Horizontal acceleration into a trough of low perturbation pressure between the low-level mesocyclone and mesoanticyclone and trailing the primary RFD gust front is followed by an abrupt deceleration of air parcels crossing the trough axis. This deceleration and associated strong convergence downstream of the pressure trough and horizontal velocity maximum are indicative of an internal RFD momentum surge. Backward trajectory analyses reveal that air parcels within the RFD surge originate from two source regions: near the surface to the north of the low-level mesocyclone, and in the ambient flow outside of the storm environment at a height of approximately 2 km.
Abstract
The forcing and origins of an internal rear-flank downdraft (RFD) momentum surge observed by the second Verification of the Origin of Rotation in Tornadoes Experiment (VORTEX2) within a supercell occurring near Dumas, Texas, on 18 May 2010 is assessed through ensemble Kalman filter (EnKF) storm-scale analyses. EnKF analyses are produced every 2 min from mobile Doppler velocity data collected by the Doppler on Wheels and Shared Mobile Atmospheric Research and Teaching radars, as well as radial velocity and reflectivity data from the KAMA (Amarillo, Texas) WSR-88D. EnKF analyses are found to reproduce the structure and evolution of an internal RFD momentum surge observed in independent mobile Doppler radar observations.
Pressure retrievals of EnKF analyses reveal that the low-level RFD outflow structure is primarily determined through nonlinear dynamic perturbation pressure gradient forcing. Horizontal acceleration into a trough of low perturbation pressure between the low-level mesocyclone and mesoanticyclone and trailing the primary RFD gust front is followed by an abrupt deceleration of air parcels crossing the trough axis. This deceleration and associated strong convergence downstream of the pressure trough and horizontal velocity maximum are indicative of an internal RFD momentum surge. Backward trajectory analyses reveal that air parcels within the RFD surge originate from two source regions: near the surface to the north of the low-level mesocyclone, and in the ambient flow outside of the storm environment at a height of approximately 2 km.
