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The Lake Thunderbird Micronet is a dense network of environmental sensors and a meteorological tower situated on ~10 acres of rural land in central Oklahoma. The Micronet was established in the spring of 2002 as part of a grassroots effort by a team of faculty and researchers at the University of Oklahoma to provide unique training and research opportunities for undergraduate and graduate students in meteorology and related environmental sciences. The history and design of the Micronet and use of the Micronet in undergraduate and graduate student training and research are described. Examples of interesting phenomena sampled at the Micronet are also presented.
The Lake Thunderbird Micronet is a dense network of environmental sensors and a meteorological tower situated on ~10 acres of rural land in central Oklahoma. The Micronet was established in the spring of 2002 as part of a grassroots effort by a team of faculty and researchers at the University of Oklahoma to provide unique training and research opportunities for undergraduate and graduate students in meteorology and related environmental sciences. The history and design of the Micronet and use of the Micronet in undergraduate and graduate student training and research are described. Examples of interesting phenomena sampled at the Micronet are also presented.
Abstract
A simulated vortex within a large-eddy simulation is subjected to various surface terrain, implemented through the immersed boundary method, to analyze the effects of complex topography on vortex behavior. Thirty simulations, including a control with zero-height terrain, are grouped into four categories—2D sinusoidal hills, 3D hills, valleys, and ridges—with slight modifications within each category. A medium-swirl-ratio vortex is translated over shallow terrain, which is modest in size relative to the vortex core diameter and with no explicitly defined surface roughness. While domain size restricts results to the very near-field effects of terrain, vortex–terrain interaction yields notable results. Terrain influences act to increase the variability of the near-surface vortex, including a notable leftward (rightward) deflection, acceleration (deceleration), and an expansion (a contraction) of the vortex as it ascends (descends) the terrain owing to changes in the corner flow swirl ratio. Additionally, 10-m track analyses show stronger horizontal wind speeds are found 1) on upslope terrain, resulting from transient subvortices that are more intense compared to the control simulation, and 2) in between adjacent hills simultaneous with strong pressure perturbations that descend from aloft. Composite statistics confirm that the region in between adjacent hills has the strongest horizontal wind speeds, while upward motions are more intense during ascent. Overall, valley (ridge) simulations have the largest horizontal (vertically upward) wind speeds. Last, horizontal and vertical wind speeds are shown to be affected by other terrain properties such as slope steepness and two-dimensionality of the terrain.
Abstract
A simulated vortex within a large-eddy simulation is subjected to various surface terrain, implemented through the immersed boundary method, to analyze the effects of complex topography on vortex behavior. Thirty simulations, including a control with zero-height terrain, are grouped into four categories—2D sinusoidal hills, 3D hills, valleys, and ridges—with slight modifications within each category. A medium-swirl-ratio vortex is translated over shallow terrain, which is modest in size relative to the vortex core diameter and with no explicitly defined surface roughness. While domain size restricts results to the very near-field effects of terrain, vortex–terrain interaction yields notable results. Terrain influences act to increase the variability of the near-surface vortex, including a notable leftward (rightward) deflection, acceleration (deceleration), and an expansion (a contraction) of the vortex as it ascends (descends) the terrain owing to changes in the corner flow swirl ratio. Additionally, 10-m track analyses show stronger horizontal wind speeds are found 1) on upslope terrain, resulting from transient subvortices that are more intense compared to the control simulation, and 2) in between adjacent hills simultaneous with strong pressure perturbations that descend from aloft. Composite statistics confirm that the region in between adjacent hills has the strongest horizontal wind speeds, while upward motions are more intense during ascent. Overall, valley (ridge) simulations have the largest horizontal (vertically upward) wind speeds. Last, horizontal and vertical wind speeds are shown to be affected by other terrain properties such as slope steepness and two-dimensionality of the terrain.
Abstract
On 20 May 2013, the cities of Newcastle, Oklahoma City, and Moore, Oklahoma, were impacted by a long-track violent tornado that was rated as an EF5 on the enhanced Fujita scale by the National Weather Service. Despite a relatively sustained long track, damage surveys revealed a number of small-scale damage indicators that hinted at storm-scale processes that occurred over short time periods. The University of Oklahoma (OU) Advanced Radar Research Center’s PX-1000 transportable, polarimetric, X-band weather radar was operating in a single-elevation PPI scanning strategy at the OU Westheimer airport throughout the duration of the tornado, collecting high spatial and temporal resolution polarimetric data every 20 s at ranges as close as 10 km and heights below 500 m AGL. This dataset contains the only known polarimetric radar observations of the Moore tornado at such high temporal resolution, providing the opportunity to analyze and study finescale phenomena occurring on rapid time scales. Analysis is presented of a series of debris ejections and rear-flank gust front surges that both preceded and followed a loop of the tornado as it weakened over the Moore Medical Center before rapidly accelerating and restrengthening to the east. The gust front structure, debris characteristics, and differential reflectivity arc breakdown are explored as evidence for a “failed occlusion” hypothesis. Observations are supported by rigorous hand analysis of critical storm attributes, including tornado track relative to the damage survey, sudden track shifts, and a directional debris ejection analysis. A conceptual description and illustration of the suspected failed occlusion process is provided, and its implications are discussed.
Abstract
On 20 May 2013, the cities of Newcastle, Oklahoma City, and Moore, Oklahoma, were impacted by a long-track violent tornado that was rated as an EF5 on the enhanced Fujita scale by the National Weather Service. Despite a relatively sustained long track, damage surveys revealed a number of small-scale damage indicators that hinted at storm-scale processes that occurred over short time periods. The University of Oklahoma (OU) Advanced Radar Research Center’s PX-1000 transportable, polarimetric, X-band weather radar was operating in a single-elevation PPI scanning strategy at the OU Westheimer airport throughout the duration of the tornado, collecting high spatial and temporal resolution polarimetric data every 20 s at ranges as close as 10 km and heights below 500 m AGL. This dataset contains the only known polarimetric radar observations of the Moore tornado at such high temporal resolution, providing the opportunity to analyze and study finescale phenomena occurring on rapid time scales. Analysis is presented of a series of debris ejections and rear-flank gust front surges that both preceded and followed a loop of the tornado as it weakened over the Moore Medical Center before rapidly accelerating and restrengthening to the east. The gust front structure, debris characteristics, and differential reflectivity arc breakdown are explored as evidence for a “failed occlusion” hypothesis. Observations are supported by rigorous hand analysis of critical storm attributes, including tornado track relative to the damage survey, sudden track shifts, and a directional debris ejection analysis. A conceptual description and illustration of the suspected failed occlusion process is provided, and its implications are discussed.
Abstract
On 27 May 2015, the Atmospheric Imaging Radar (AIR) collected high-temporal resolution radar observations of an EF-2 tornado near Canadian, Texas. The AIR is a mobile, X-band, imaging radar that uses digital beamforming to collect simultaneous RHI scans while steering mechanically in azimuth to obtain rapid-update weather data. During this deployment, 20°-by-80° (elevation × azimuth) sector volumes were collected every 5.5 s at ranges as close as 6 km. The AIR captured the late-mature and decaying stages of the tornado. Early in the deployment, the tornado had a radius of maximum winds (RMW) of 500 m and exhibited maximum Doppler velocities near 65 m s−1. This study documents the rapid changes associated with the dissipation stages of the tornado. A 10-s resolution time–height investigation of vortex tilt and differential velocity 
Abstract
On 27 May 2015, the Atmospheric Imaging Radar (AIR) collected high-temporal resolution radar observations of an EF-2 tornado near Canadian, Texas. The AIR is a mobile, X-band, imaging radar that uses digital beamforming to collect simultaneous RHI scans while steering mechanically in azimuth to obtain rapid-update weather data. During this deployment, 20°-by-80° (elevation × azimuth) sector volumes were collected every 5.5 s at ranges as close as 6 km. The AIR captured the late-mature and decaying stages of the tornado. Early in the deployment, the tornado had a radius of maximum winds (RMW) of 500 m and exhibited maximum Doppler velocities near 65 m s−1. This study documents the rapid changes associated with the dissipation stages of the tornado. A 10-s resolution time–height investigation of vortex tilt and differential velocity
Abstract
Phased-array radar (PAR) technology offers the flexibility of sampling the storm and clear-air regions with different update times. As such, the radial velocity from clear-air regions, typically with a lower signal-to-noise ratio, can be measured more accurately. In this work, observing system simulation experiments are conducted to explore the potential value of assimilating clear-air radial velocity observations to improve numerical prediction of supercell thunderstorms. Synthetic PAR observations of a splitting supercell are assimilated at different life cycle stages using an ensemble Kalman filter. Results show that assimilating environmental clear-air radial velocity can reduce wind errors in the near-storm environment and within the precipitation region. Improvements in the forecast are seen at different stages, especially for the forecast after 30 min. After assimilating clear-air radial velocity observations, the probabilities of updraft helicity and precipitation within the corresponding swaths of the truth simulation increase up to 30%–40%. Additional diagnostics suggest that the more accurate track forecast, stronger vertical motion, and better-maintained supercell can be attributed to the better analysis and prediction of the mean environmental winds and linear and nonlinear dynamic forces. Consequently, assimilating clear-air radial velocity produces accurate storm structure (rotating updrafts), updraft size, and storm track, and improves the surface accumulated precipitation forecast. The performance of forecasts with a higher frequency of assimilating clear-air radial velocity does not show systematic improvement. These results highlight the potential of assimilating clear-air radial velocity observations to improve numerical weather prediction forecasts of supercell thunderstorms.
Abstract
Phased-array radar (PAR) technology offers the flexibility of sampling the storm and clear-air regions with different update times. As such, the radial velocity from clear-air regions, typically with a lower signal-to-noise ratio, can be measured more accurately. In this work, observing system simulation experiments are conducted to explore the potential value of assimilating clear-air radial velocity observations to improve numerical prediction of supercell thunderstorms. Synthetic PAR observations of a splitting supercell are assimilated at different life cycle stages using an ensemble Kalman filter. Results show that assimilating environmental clear-air radial velocity can reduce wind errors in the near-storm environment and within the precipitation region. Improvements in the forecast are seen at different stages, especially for the forecast after 30 min. After assimilating clear-air radial velocity observations, the probabilities of updraft helicity and precipitation within the corresponding swaths of the truth simulation increase up to 30%–40%. Additional diagnostics suggest that the more accurate track forecast, stronger vertical motion, and better-maintained supercell can be attributed to the better analysis and prediction of the mean environmental winds and linear and nonlinear dynamic forces. Consequently, assimilating clear-air radial velocity produces accurate storm structure (rotating updrafts), updraft size, and storm track, and improves the surface accumulated precipitation forecast. The performance of forecasts with a higher frequency of assimilating clear-air radial velocity does not show systematic improvement. These results highlight the potential of assimilating clear-air radial velocity observations to improve numerical weather prediction forecasts of supercell thunderstorms.
Abstract
A detailed damage survey is combined with high-resolution mobile, rapid-scanning X-band polarimetric radar data collected on the Shawnee, Oklahoma, tornado of 19 May 2013. The focus of this study is the radar data collected during a period when the tornado was producing damage rated EF3. Vertical profiles of mobile radar data, centered on the tornado, revealed that the radar reflectivity was approximately uniform with height and increased in magnitude as more debris was lofted. There was a large decrease in both the cross-correlation coefficient (ρ hv) and differential radar reflectivity (Z DR) immediately after the tornado exited the damaged area rated EF3. Low ρ hv and Z DR occurred near the surface where debris loading was the greatest. The 10th percentile of ρ hv decreased markedly after large amounts of debris were lofted after the tornado leveled a number of structures. Subsequently, ρ hv quickly recovered to higher values. This recovery suggests that the largest debris had been centrifuged or fallen out whereas light debris remained or continued to be lofted. Range–height profiles of the dual-Doppler analyses that were azimuthally averaged around the tornado revealed a zone of maximum radial convergence at a smaller radius relative to the leading edge of lofted debris. Low-level inflow into the tornado encountering a positive bias in the tornado-relative radial velocities could explain the existence of the zone. The vertical structure of the convergence zone was shown for the first time.
Abstract
A detailed damage survey is combined with high-resolution mobile, rapid-scanning X-band polarimetric radar data collected on the Shawnee, Oklahoma, tornado of 19 May 2013. The focus of this study is the radar data collected during a period when the tornado was producing damage rated EF3. Vertical profiles of mobile radar data, centered on the tornado, revealed that the radar reflectivity was approximately uniform with height and increased in magnitude as more debris was lofted. There was a large decrease in both the cross-correlation coefficient (ρ hv) and differential radar reflectivity (Z DR) immediately after the tornado exited the damaged area rated EF3. Low ρ hv and Z DR occurred near the surface where debris loading was the greatest. The 10th percentile of ρ hv decreased markedly after large amounts of debris were lofted after the tornado leveled a number of structures. Subsequently, ρ hv quickly recovered to higher values. This recovery suggests that the largest debris had been centrifuged or fallen out whereas light debris remained or continued to be lofted. Range–height profiles of the dual-Doppler analyses that were azimuthally averaged around the tornado revealed a zone of maximum radial convergence at a smaller radius relative to the leading edge of lofted debris. Low-level inflow into the tornado encountering a positive bias in the tornado-relative radial velocities could explain the existence of the zone. The vertical structure of the convergence zone was shown for the first time.
Abstract
This study investigates the use of tornadic debris signature (TDS) parameters to estimate tornado damage severity using Norman, Oklahoma (KOUN), polarimetric radar data (polarimetric version of the Weather Surveillance Radar-1988 Doppler radar). Several TDS parameters are examined, including parameters based on the 10th or 90th percentiles of polarimetric variables (lowest tilt TDS parameters) and TDS parameters based on the TDS volumetric coverage (spatial TDS parameters). Two highly detailed National Weather Service (NWS) damage surveys are compared to TDS parameters. The TDS parameters tend to be correlated with the enhanced Fujita scale (EF) rating. The 90th percentile reflectivity, TDS height, and TDS volume increase during tornado intensification and decrease during tornado dissipation. For 14 tornado cases, the maximum or minimum TDS parameter values are compared to the tornado’s EF rating. For tornadoes with a higher EF rating, higher maximum values of the 90th percentile Z HH, TDS height, and volume, as well as lower minimum values of 10th percentile ρ HV and Z DR, are observed. Maxima in spatial TDS parameters are observed after periods of severe, widespread tornado damage for violent tornadoes. This paper discusses how forecasters could use TDS parameters to obtain near-real-time information about tornado damage severity and spatial extent.
Abstract
This study investigates the use of tornadic debris signature (TDS) parameters to estimate tornado damage severity using Norman, Oklahoma (KOUN), polarimetric radar data (polarimetric version of the Weather Surveillance Radar-1988 Doppler radar). Several TDS parameters are examined, including parameters based on the 10th or 90th percentiles of polarimetric variables (lowest tilt TDS parameters) and TDS parameters based on the TDS volumetric coverage (spatial TDS parameters). Two highly detailed National Weather Service (NWS) damage surveys are compared to TDS parameters. The TDS parameters tend to be correlated with the enhanced Fujita scale (EF) rating. The 90th percentile reflectivity, TDS height, and TDS volume increase during tornado intensification and decrease during tornado dissipation. For 14 tornado cases, the maximum or minimum TDS parameter values are compared to the tornado’s EF rating. For tornadoes with a higher EF rating, higher maximum values of the 90th percentile Z HH, TDS height, and volume, as well as lower minimum values of 10th percentile ρ HV and Z DR, are observed. Maxima in spatial TDS parameters are observed after periods of severe, widespread tornado damage for violent tornadoes. This paper discusses how forecasters could use TDS parameters to obtain near-real-time information about tornado damage severity and spatial extent.
Abstract
Past numerical simulation studies found that debris loading from sand-sized particles may substantially affect tornado dynamics, causing reductions in near-surface wind speeds up to 50%. To further examine debris loading effects, simulations are performed using a large-eddy simulation model with a two-way drag force coupling between air and sand. Simulations encompass a large range of surface debris fluxes that cause negligible to substantial impact on tornado dynamics for a high-swirl tornado vortex simulation.
Simulations are considered for a specific case with a single vortex flow type (swirl ratio, intensity, and translation velocity) and a fixed set of debris and aerodynamic parameters. Thus, it is stressed that these findings apply to the specific flow and debris parameters herein and would likely vary for different flows or debris parameters. For this specific case, initial surface debris fluxes are varied over a factor of 16 384, and debris cloud mass varies by only 42% of this range because a negative feedback reduces near-surface horizontal velocities. Debris loading effects on the axisymmetric mean flow are evident when maximum debris loading exceeds 0.1 kg kg−1, but instantaneous maximum wind speed and TKE exhibit small changes at smaller debris loadings (greater than 0.01 kg kg−1). Initially, wind speeds are reduced in a shallow, near-surface layer, but the magnitude and depth of these changes increases with higher debris loading. At high debris loading, near-surface horizontal wind speeds are reduced by 30%–60% in the lowest 10 m AGL. In moderate and high debris loading scenarios, the number and intensity of subvortices also decrease close to the surface.
Abstract
Past numerical simulation studies found that debris loading from sand-sized particles may substantially affect tornado dynamics, causing reductions in near-surface wind speeds up to 50%. To further examine debris loading effects, simulations are performed using a large-eddy simulation model with a two-way drag force coupling between air and sand. Simulations encompass a large range of surface debris fluxes that cause negligible to substantial impact on tornado dynamics for a high-swirl tornado vortex simulation.
Simulations are considered for a specific case with a single vortex flow type (swirl ratio, intensity, and translation velocity) and a fixed set of debris and aerodynamic parameters. Thus, it is stressed that these findings apply to the specific flow and debris parameters herein and would likely vary for different flows or debris parameters. For this specific case, initial surface debris fluxes are varied over a factor of 16 384, and debris cloud mass varies by only 42% of this range because a negative feedback reduces near-surface horizontal velocities. Debris loading effects on the axisymmetric mean flow are evident when maximum debris loading exceeds 0.1 kg kg−1, but instantaneous maximum wind speed and TKE exhibit small changes at smaller debris loadings (greater than 0.01 kg kg−1). Initially, wind speeds are reduced in a shallow, near-surface layer, but the magnitude and depth of these changes increases with higher debris loading. At high debris loading, near-surface horizontal wind speeds are reduced by 30%–60% in the lowest 10 m AGL. In moderate and high debris loading scenarios, the number and intensity of subvortices also decrease close to the surface.
Abstract
In this study, data collected by the Atmospheric Imaging Radar (AIR) are analyzed in conjunction with WSR-88D data (KFDR) for a tornado near Tipton, Oklahoma, on 16 May 2015. The analysis presented herein utilizes PPIs from both radars, polarimetric data from KFDR, time–height plots from the AIR, and a ground-based velocity track display (GBVTD) analysis. This study is novel in that it uses high-resolution mobile radar data (update time of 6–7 s) in tandem with polarimetric data from KFDR in order to identify possible areas of debris, including a debris ring contained within the outer vortex circulation. Leveraging the high spatiotemporal resolution of the AIR with the polarimetric capability of KFDR leads to analysis of reflectivity distributions, debris lofting, kinematic changes, and oscillations in tornado intensity during a portion of the mature stage of the tornado, with a particular focus on the relationship between changes in the reflectivity field and dynamical changes around the tornado. Debris is lofted in a high-reflectivity concentric ring of increasing radius and height around the tornado over several minutes, within the outer weak-echo hole (WEH). Simultaneously, debris lofting and asymmetric reflectivity distribution around the WEH coincide with changes in vortex tilt on multiple occasions. In one instance, hydrometeor fallout appears to precede a possible descending reflectivity core. Using the GBVTD results, near-surface convergence intensifies at the same time and location as when the debris ring is lofted. Additionally, strengthening of the tornado via multiple modes of vertical evolution (i.e., bottom-up intensification over time vs simultaneous intensification throughout the lowest few hundred meters) is observed.
Abstract
In this study, data collected by the Atmospheric Imaging Radar (AIR) are analyzed in conjunction with WSR-88D data (KFDR) for a tornado near Tipton, Oklahoma, on 16 May 2015. The analysis presented herein utilizes PPIs from both radars, polarimetric data from KFDR, time–height plots from the AIR, and a ground-based velocity track display (GBVTD) analysis. This study is novel in that it uses high-resolution mobile radar data (update time of 6–7 s) in tandem with polarimetric data from KFDR in order to identify possible areas of debris, including a debris ring contained within the outer vortex circulation. Leveraging the high spatiotemporal resolution of the AIR with the polarimetric capability of KFDR leads to analysis of reflectivity distributions, debris lofting, kinematic changes, and oscillations in tornado intensity during a portion of the mature stage of the tornado, with a particular focus on the relationship between changes in the reflectivity field and dynamical changes around the tornado. Debris is lofted in a high-reflectivity concentric ring of increasing radius and height around the tornado over several minutes, within the outer weak-echo hole (WEH). Simultaneously, debris lofting and asymmetric reflectivity distribution around the WEH coincide with changes in vortex tilt on multiple occasions. In one instance, hydrometeor fallout appears to precede a possible descending reflectivity core. Using the GBVTD results, near-surface convergence intensifies at the same time and location as when the debris ring is lofted. Additionally, strengthening of the tornado via multiple modes of vertical evolution (i.e., bottom-up intensification over time vs simultaneous intensification throughout the lowest few hundred meters) is observed.
Exploring Tornadic Debris Signature Hypotheses Using Radar Simulations and Large-Eddy SimulationsAbstract
When a tornado lofts debris to the height of the radar beam, a signature known as the tornadic debris signature (TDS) can sometimes be observed on radar. The TDS is a useful signature for operational forecasters because it can confirm the presence of a tornado and provide information about the amount of damage occurring. Since real-time estimates of tornadic intensity do not have a high degree of accuracy, past studies have hypothesized that the TDS could also be an indicator of the strength of a tornado. However, few studies have related the tornadic wind field to TDS characteristics because of the difficulty of obtaining accurate, three-dimensional wind data in tornadoes from radar data. With this in mind, the goals of this study are twofold: 1) to investigate the relationships between polarimetric characteristics of TDSs and the three-dimensional tornadic winds, and 2) to define relationships between polarimetric radar variables and debris characteristics. Simulations are performed using a dual-polarization radar simulator called SimRadar; large-eddy simulations (LESs) of tornadoes; and a single-volume,
Abstract
When a tornado lofts debris to the height of the radar beam, a signature known as the tornadic debris signature (TDS) can sometimes be observed on radar. The TDS is a useful signature for operational forecasters because it can confirm the presence of a tornado and provide information about the amount of damage occurring. Since real-time estimates of tornadic intensity do not have a high degree of accuracy, past studies have hypothesized that the TDS could also be an indicator of the strength of a tornado. However, few studies have related the tornadic wind field to TDS characteristics because of the difficulty of obtaining accurate, three-dimensional wind data in tornadoes from radar data. With this in mind, the goals of this study are twofold: 1) to investigate the relationships between polarimetric characteristics of TDSs and the three-dimensional tornadic winds, and 2) to define relationships between polarimetric radar variables and debris characteristics. Simulations are performed using a dual-polarization radar simulator called SimRadar; large-eddy simulations (LESs) of tornadoes; and a single-volume,
