20 Sep 2002 Storm-scale Vortex Detection and Diagnosis Real-Time Mining of Integrated Weather Information Meeting 20 September 2002 Real-Time Mining of Integrated Weather Information Meeting 20 September 2002

20 Sep 2002 The WSR-88D system has two independent algorithms for detecting rotation in severe thunderstorms: –Mesocyclone Detection Algorithm (MDA; Fall 2003) –Tornado Detection Algorithm (TDA; current). Current NWS requirements include goals toward improving the probability of detection, false alarm rate, and lead time for tornado warnings. The WSR-88D system has two independent algorithms for detecting rotation in severe thunderstorms: –Mesocyclone Detection Algorithm (MDA; Fall 2003) –Tornado Detection Algorithm (TDA; current). Current NWS requirements include goals toward improving the probability of detection, false alarm rate, and lead time for tornado warnings. INTRODUCTION

20 Sep 2002 INTRODUCTION This talk focuses initially on our reasons for changing automated vortex detection techniques. –Description of current techniques –Examples of why a change is needed NSSL’s proposed solution is a Vortex Detection and Diagnosis Algorithm (VDDA). This talk focuses initially on our reasons for changing automated vortex detection techniques. –Description of current techniques –Examples of why a change is needed NSSL’s proposed solution is a Vortex Detection and Diagnosis Algorithm (VDDA).

20 Sep 2002 OLD RADAR PARADIGM Early studies (1970s, 1980s) used the only available Doppler radar data at the time (mostly Central Oklahoma). Mesocyclones and Tornado Vortex Signatures (TVS) are rotating columns of air in thunderstorms with specific spatial and strength criteria. –How do we define a “mesocyclone” or “TVS”? What is operationally-significant? Early studies (1970s, 1980s) used the only available Doppler radar data at the time (mostly Central Oklahoma). Mesocyclones and Tornado Vortex Signatures (TVS) are rotating columns of air in thunderstorms with specific spatial and strength criteria. –How do we define a “mesocyclone” or “TVS”? What is operationally-significant?

20 Sep 2002 WSR-88D NETWORK COMPLETED We have gathered a plethora of storm data from around the country. Not all storms are like those observed in Central Oklahoma in the 70s and 80s. –Many storm and storm-scale vortex types can be associated with tornadoes Field projects (e.g., VORTEX) have observed interesting new things too. We have gathered a plethora of storm data from around the country. Not all storms are like those observed in Central Oklahoma in the 70s and 80s. –Many storm and storm-scale vortex types can be associated with tornadoes Field projects (e.g., VORTEX) have observed interesting new things too.

20 Sep 2002 NEW ALGORITHMS DEVELOPED IN THE MID 1990s On the radar, a “Big/strong vortex” does not always = “Tornado”, and vice versa. The future algorithm should detect many storm-scale vortices of many sizes and strengths (including those < 1 km). Decision makers and future algorithms should integrate all available information. On the radar, a “Big/strong vortex” does not always = “Tornado”, and vice versa. The future algorithm should detect many storm-scale vortices of many sizes and strengths (including those < 1 km). Decision makers and future algorithms should integrate all available information.

20 Sep 2002 Designed to detect storm-scale (1-10 km diameter) 3D vortices. –High POD –Can track and trend incipient vortices through maturity, on to demise for complete time history. Next, the NSSL MDA diagnoses and classifies the vortices. –To determine which are operationally significant. –Includes probabilistic output from Neural Networks. Designed to detect storm-scale (1-10 km diameter) 3D vortices. –High POD –Can track and trend incipient vortices through maturity, on to demise for complete time history. Next, the NSSL MDA diagnoses and classifies the vortices. –To determine which are operationally significant. –Includes probabilistic output from Neural Networks. Mesocyclone Detection Algorithm

20 Sep 2002 Mesocyclone Detection Algorithm Detects 2D features by searching for azimuthal shear “cores” Ranks 2D features based on vortex strength thresholds Vertically-associates 2D feature centroids to produce 3D detections. Time-associates 3D centroids to produce 4D tracks, trends, and extrapolates for forecasts. Classifies 4D detections as “mesocyclones” based on thresholds for base, depth, and strength. Detects 2D features by searching for azimuthal shear “cores” Ranks 2D features based on vortex strength thresholds Vertically-associates 2D feature centroids to produce 3D detections. Time-associates 3D centroids to produce 4D tracks, trends, and extrapolates for forecasts. Classifies 4D detections as “mesocyclones” based on thresholds for base, depth, and strength.

20 Sep 2002 Storm-relative Velocity Reflectivity Mesocyclone 330 o 100 km Range (km) o o o o o o o Azimuth Shear Segments Mesocyclone

20 Sep 2002 Vertical Association 2.4 o 0.5 o 1.5 o Mesocyclone or TVS Storm cloud Cloud base WSR-88D

20 Sep 2002 Time Association previous position current detections a Search Radii c b “first guess”

20 Sep 2002 Time Association previous position Search Radii Associated current position

20 Sep 2002 Time Association previous position 5-min Forecast position Search Radii Associated current position

20 Sep 2002 Tornado (TVS) Detection Algorithm Very similar techniques to those used by MDA for 2D, 3D, and 4D detection and classification. The main differences: –2D feature detection based on gate-to-gate azimuthal shear –Choice of classification thresholds designed for stronger and more intense vortices Very similar techniques to those used by MDA for 2D, 3D, and 4D detection and classification. The main differences: –2D feature detection based on gate-to-gate azimuthal shear –Choice of classification thresholds designed for stronger and more intense vortices

20 Sep 2002 Storm-relative Velocity Reflectivity 330 o 100 km Range (km) o o o o o o o Azimuth Shear Segments TVS

20 Sep 2002 MDA/TDA Limitations Operates using single-radar radial velocity data. Volume scan output 5-6 minutes older than first elevation scan (nearest surface). Reduces lead-time. Very sensitive to artifacts in radar data –Dealiasing errors within storm and non-storm echo (anomalous propagation, ground clutter, chaff, clear air return, first trip “ring”) –Beam broadening, cone-of-silence, radar horizon –Vortex radius to beam width ratio, beam center offsets. Operates using single-radar radial velocity data. Volume scan output 5-6 minutes older than first elevation scan (nearest surface). Reduces lead-time. Very sensitive to artifacts in radar data –Dealiasing errors within storm and non-storm echo (anomalous propagation, ground clutter, chaff, clear air return, first trip “ring”) –Beam broadening, cone-of-silence, radar horizon –Vortex radius to beam width ratio, beam center offsets.

20 Sep 2002 MDA/TDA Limitations Reflectivity data used crudely to filter velocities associated with non-storm echo (single 0 dBZ threshold). Heuristic threshold-based rules. Azimuthal shear is associated with rotation, but also associated with boundaries (aligned parallel to radar radials) and other phenomenon. Reflectivity data used crudely to filter velocities associated with non-storm echo (single 0 dBZ threshold). Heuristic threshold-based rules. Azimuthal shear is associated with rotation, but also associated with boundaries (aligned parallel to radar radials) and other phenomenon.

20 Sep 2002 THE VDDA The next-generation Vortex Detection and Diagnosis Algorithm (VDDA) will be designed detect a broader spectrum of storm-scale vortices (including TVSs as well - merging MDA and TDA). The VDDA will integrate new ideas learned from the new radar data, and data from radar reflectivity and other sensors (e.g. near-storm environment, satellites, etc.). The next-generation Vortex Detection and Diagnosis Algorithm (VDDA) will be designed detect a broader spectrum of storm-scale vortices (including TVSs as well - merging MDA and TDA). The VDDA will integrate new ideas learned from the new radar data, and data from radar reflectivity and other sensors (e.g. near-storm environment, satellites, etc.).

20 Sep 2002 VDDA Considerations Current algorithm paradigms are that a TVS is a gate-to-gate signature, and that a mesocyclone is not. These assumptions do not hold true, especially at near and far ranges. –A “mesocyclone” at far ranges can exhibit gate-to-gate shear. –A tornado (or tornado cyclone) at near ranges can be sampled across more than two adjacent radar azimuths. Current algorithm paradigms are that a TVS is a gate-to-gate signature, and that a mesocyclone is not. These assumptions do not hold true, especially at near and far ranges. –A “mesocyclone” at far ranges can exhibit gate-to-gate shear. –A tornado (or tornado cyclone) at near ranges can be sampled across more than two adjacent radar azimuths.

20 Sep 2002 VDDA Considerations The radar characteristics of the signature are a function of : –Vortex core radius to beamwidth ratio (i.e., distance and diameter of vortex). –Rotational Velocity –Offset between the radar beam centroid and the vortex centroid. The radar characteristics of the signature are a function of : –Vortex core radius to beamwidth ratio (i.e., distance and diameter of vortex). –Rotational Velocity –Offset between the radar beam centroid and the vortex centroid.

20 Sep 2002 VDDA Considerations Observational studies have shown that a variety of vortex scales (~0 to 10 km) can be associated with tornadoes, tornado cyclones, and mesocyclones. Not all tornadoes are associated with the classic definition of a supercell –Supercells with small horizontal dimensions (mini-supercell). –Supercells with small vertical dimensions (low-topped supercells). –Tropical-cyclone mesocyclones (TC-mesos). –Bow echo tornadoes (along the leading edge). Observational studies have shown that a variety of vortex scales (~0 to 10 km) can be associated with tornadoes, tornado cyclones, and mesocyclones. Not all tornadoes are associated with the classic definition of a supercell –Supercells with small horizontal dimensions (mini-supercell). –Supercells with small vertical dimensions (low-topped supercells). –Tropical-cyclone mesocyclones (TC-mesos). –Bow echo tornadoes (along the leading edge).

20 Sep 2002 F1 Phoenix Mini-MINI-supercells F1 Phoenix Mini-MINI-supercells All at same Zoom factor! Tornado location Tornado location F1 Colorado Mini-supercell F1 Colorado Mini-supercell F4 Oklahoma Supercell F4 Oklahoma Supercell

20 Sep 2002 Low-topped mini-supercells KLWX Sterling VA 30 April 1994

20 Sep 2002 Not all mini-supercells are low-topped! Cone of Silence KPUX Pueblo 22 June 1995

20 Sep 2002 KMLB Melbourne FL 11 Nov 94 Tropical Cyclone Mesos (low-topped and mini) T.C. Josephine

20 Sep 2002 KEVX Eglin FL 4 Oct 1995 Here’s a TC-meso that is low-topped, but NOT mini! Hurricane Opal

20 Sep 2002 Leading-edge tornadoes (shallow, short-lived) KLSX St. Louis 15 April 1994

20 Sep 2002 Leading-edge tornadoes (shallow, short-lived) KLSX St. Louis 15 April 1994

20 Sep 2002 VDDA feature extraction and forecasting Develop new 2D and 3D vortex feature extractor utilizing testing on analytically- modeled vortices (with sampling limitations), boundaries, etc. –Least-squares shear derivatives (LLSD) –Statistics-based image processing (K- means) –Advanced motion estimation Develop new 2D and 3D vortex feature extractor utilizing testing on analytically- modeled vortices (with sampling limitations), boundaries, etc. –Least-squares shear derivatives (LLSD) –Statistics-based image processing (K- means) –Advanced motion estimation

20 Sep 2002 LLSD A linear least-squares fit of radial velocity bins in the neighborhood of a gate. The number of data bins in the neighborhood depends on the range from the radar. A “constant” kernel size means more data bins at close ranges with polar grids. Fit to a linear combination of azimuth and range Coefficient for azimuth is an estimate of azimuthal shear or rotation Coefficient for range is an estimate of the radial shear or divergence/convergence. A linear least-squares fit of radial velocity bins in the neighborhood of a gate. The number of data bins in the neighborhood depends on the range from the radar. A “constant” kernel size means more data bins at close ranges with polar grids. Fit to a linear combination of azimuth and range Coefficient for azimuth is an estimate of azimuthal shear or rotation Coefficient for range is an estimate of the radial shear or divergence/convergence.

20 Sep 2002 Linear Least Squares Derivative (simulated data) Mesocyclone Simulated WSR-88D VelocityAzimuthal Shear (LLSD) Cyclonic Shear Anticyclonic Shear Radial Shear (LLSD) Convergence Divergence

20 Sep 2002 Linear Least Squares Derivative (actual data) Actual WSR-88D VelocityAzimuthal Shear (LLSD) TVS Meso Cyclonic Shear Anticyclonic Shear Radial Shear (LLSD) Convergence Divergence

20 Sep 2002 Simulated radial velocity data of a variety of phenomenon Follows the method of Wood and Brown (1997). Simulate symmetric vortices of varying strength, size, and radar sampling: –Varying Ranges: 2 to 200 km –Varying Rotational Velocities: 5 to 50 m/s –Varying Diameters: 0.25 to 6 km –Varying Beam/Vortex center offsets: 0.5 o to +0.5 o Other “special” simulations: –Mesocyclones with rear-flank downdrafts. –Mesocyclones with embedded TVS. –Straight gust front boundaries Follows the method of Wood and Brown (1997). Simulate symmetric vortices of varying strength, size, and radar sampling: –Varying Ranges: 2 to 200 km –Varying Rotational Velocities: 5 to 50 m/s –Varying Diameters: 0.25 to 6 km –Varying Beam/Vortex center offsets: 0.5 o to +0.5 o Other “special” simulations: –Mesocyclones with rear-flank downdrafts. –Mesocyclones with embedded TVS. –Straight gust front boundaries

20 Sep 2002 Storm-relative velocity Straight Gust Front Meso with Rear- Flank Downdraft Meso with embedded TVS Pure Rankine Vortex (Meso)

20 Sep 2002 SRV with NSSL MDA 2D features Straight Gust Front Meso with Rear- Flank Downdraft Meso with embedded TVS Pure Rankine Vortex (Meso) 1

20 Sep 2002 Azimuthal and Radial shear Straight Gust Front Meso with Rear- Flank Downdraft Meso with embedded TVS Pure Rankine Vortex (Meso)

20 Sep 2002 VDDA feature extraction and forecasting Combine rotation and divergence fields from multiple radars into 3D mosaicked grid. Rapidly-updating grid provides greater lead time. Use multi-scale statistical texturing techniques (e.g., Kmeans) to extract 2D and 3D “cores” of rotation. Combine rotation and divergence fields from multiple radars into 3D mosaicked grid. Rapidly-updating grid provides greater lead time. Use multi-scale statistical texturing techniques (e.g., Kmeans) to extract 2D and 3D “cores” of rotation. X Z

20 Sep 2002 VDDA feature extraction and forecasting X Z 1 km x 1° grid

20 Sep 2002 VDDA feature extraction and forecasting Diagnose properties of the rotation cores to determine probability that they are associated with severe weather or tornadoes. Instead of centroid extrapolation, use statistical motion estimator to forecast vortex locations, could provide more lead time. Diagnose properties of the rotation cores to determine probability that they are associated with severe weather or tornadoes. Instead of centroid extrapolation, use statistical motion estimator to forecast vortex locations, could provide more lead time.

20 Sep 2002 Multiple-source integration Use information from multiple-radar reflectivity data (vertical profiles or VIL), near-storm environment, and IR satellite to filter out non-storm echo prior to rotation core extraction (instead of just 0 dBZ thresholds). Integrate information from BWER, hook echo ID, boundary ID (which also uses LLSD), total lightning data (CG and IC), and near-storm environment data for vortex diagnoses (e.g., Neural Networks). Use information from multiple-radar reflectivity data (vertical profiles or VIL), near-storm environment, and IR satellite to filter out non-storm echo prior to rotation core extraction (instead of just 0 dBZ thresholds). Integrate information from BWER, hook echo ID, boundary ID (which also uses LLSD), total lightning data (CG and IC), and near-storm environment data for vortex diagnoses (e.g., Neural Networks). LLSD Convergence

20 Sep 2002 Current VDDA Work Testing process to compute LLSD at different scales. Simulated vortices with random noise (1000 trials), and various vortex diameters. Compared to azimuthal shear. Testing process to compute LLSD at different scales. Simulated vortices with random noise (1000 trials), and various vortex diameters. Compared to azimuthal shear.