Storm-scale Vortex Detection and Diagnosis

1. Storm-scale Vortex Detection and Diagnosis Real-Time Mining of Integrated Weather Information Meeting 20 September 2002 gstumpf@ou.edu

2. INTRODUCTION • 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.

3. 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).

4. 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?

5. 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.

6. NEW ALGORITHMS DEVELOPED IN THEMID 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.

7. Mesocyclone Detection Algorithm • 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.

8. 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.

9. Reflectivity Storm-relative Velocity 330o 330o Mesocyclone Mesocyclone 100 km 100 km 99.75 -4.5 -20.0 -20.5 22.0 21.5 19.5 17.5 99.50 -9.0 -22.0 -22.5 23.0 23.5 22.5 20.0 99.25 -12.0 -22.0 -25.5 24.0 24.5 24.0 20.5 99.00 -20.5 -25.0 -26.5 26.0 27.0 27.0 21.0 Shear Segments 98.75 -15.0 -25.0 -27.0 24.5 28.0 28.5 20.5 Range (km) 98.50 -7.5 -18.5 -23.5 21.5 29.5 30.5 21.0 98.25 -8.5 -19.5 -23.5 19.5 28.0 29.0 20.5 98.00 -5.5 -19.5 -23.0 14.5 27.5 28.5 20.0 97.75 -5.5 -11.0 -20.5 15.5 26.5 27.5 20.0 329.5o 330.5o 331.5o 332.5o 333.5o 334.5o 335.5o Azimuth

10. o 2.4 o Storm cloud 1.5 o 0.5 WSR-88D Mesocyclone or TVS Cloud base Vertical Association

11. Time Association Search Radii current detections a b c “first guess” previous position

12. Time Association Search Radii Associated current position previous position

13. Time Association Search Radii Associated current position 5-min Forecast position previous position

14. 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

15. TVS Reflectivity Storm-relative Velocity 330o 330o TVS 100 km 100 km 99.75 -4.5 -20.0 -20.5 22.0 21.5 19.5 17.5 99.50 -9.0 -22.0 -22.5 23.0 23.5 22.5 20.0 99.25 -12.0 -22.0 -25.5 24.0 24.5 24.0 20.5 99.00 -20.5 -25.0 -26.5 26.0 27.0 27.0 21.0 Shear Segments 98.75 -15.0 -25.0 -27.0 24.5 28.0 28.5 20.5 Range (km) 98.50 -7.5 -18.5 -23.5 21.5 29.5 30.5 21.0 98.25 -8.5 -19.5 -23.5 19.5 28.0 29.0 20.5 98.00 -5.5 -19.5 -23.0 14.5 27.5 28.5 20.0 97.75 -5.5 -11.0 -20.5 15.5 26.5 27.5 20.0 329.5o 330.5o 331.5o 332.5o 333.5o 334.5o 335.5o Azimuth

16. 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.

17. 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.

18. THEVDDA • 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.).

19. 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.

20. 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.

21. 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).

22. F1 Colorado Mini-supercell F4 Oklahoma Supercell F1 Phoenix Mini-MINI-supercells Tornado location All at same Zoom factor!

23. Low-topped mini-supercells KLWX Sterling VA 30 April 1994

24. Not all mini-supercells are low-topped! Cone of Silence KPUX Pueblo 22 June 1995

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

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

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

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

29. 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

30. 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.

31. Mesocyclone Convergence Radial Shear (LLSD) Simulated WSR-88D Velocity Azimuthal Shear (LLSD) CyclonicShear Divergence Anticyclonic Shear Linear Least Squares Derivative (simulated data)

32. Convergence Cyclonic Shear Divergence Linear Least Squares Derivative (actual data) Radial Shear (LLSD) Actual WSR-88D Velocity Azimuthal Shear (LLSD) Anticyclonic Shear Meso TVS

33. 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: 0.5o to +0.5o • Other “special” simulations: • Mesocyclones with rear-flank downdrafts. • Mesocyclones with embedded TVS. • Straight gust front boundaries

34. Storm-relative velocity Pure Rankine Vortex (Meso) Meso with Rear- Flank Downdraft Meso with embedded TVS Straight Gust Front

35. 1 SRV with NSSL MDA 2D features Pure Rankine Vortex (Meso) Meso with Rear- Flank Downdraft Meso with embedded TVS Straight Gust Front

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

37. Z X 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.

38. 1 km x 1° grid Z X VDDA feature extraction and forecasting

39. 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.

40. 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). LLSD Convergence

41. 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.