Severe Weather Nowcasting System: NSSL Warning Decision Support System WDSS

1. Severe Weather Nowcasting System: NSSL Warning Decision Support System (WDSS)

2. NSSL Warning Research and Development Process

3. The Warning Challenge How do operational warning forecasters distinguish between severe and non-severe, and tornadic and non-tornadic thunderstorms with the information they have?

4. WDSS Severe Weather Applications Concepts WDSSI (1987 – 1999) Single Radar Only Guidance tools “Safety Net” WDSSII (Integrated Information) (2000 – Now) It makes sense that the severe weather detection tools also integrate multi-radar/multi-sensor information! Output should be effectively displayed and managed Highest resolution data (e.g., radar data in native format) Novel ways to represent and “slice and dice” 4D data

5. WDSSI Algorithms Severe Storms Analysis Program (SSAP): Implemented into WSR-88D: SCIT - Storm Cell ID and Tracking algorithm HDA - Hail Detection Algorithm MDA+ - Mesocyclone Detection Algorithm (with Tracking) TDA+ - Tornado (TVS) Detection Algorithm (with Tracking) Others: DDPDA - Damaging Downburst Prediction & Detection Algorithm (not in WSR-88D) ISCIT – Improved Storm Cell ID and Tracking algorithm EHDA - Enhanced Hail Diagnosis Algorithm (with Near Storm Environment (NSE))

6. Storm Cell Identification and Tracking (SCIT) Objectives: To identify the centroids of storms To diagnose the reflectivity structure of the storm and its evolution (trends) To track and forecast their movement ISCIT improves oncell detection andtracking improvements(73% to 97% POD)

7. Storm Cell Identification and Tracking (SCIT) Searches for “gate runs” (segments) using multiple reflectivity thresholds (30, 35, 40,...60 dBZ) on each elevation scan. Correlates “gate runs” into 2D “features” and extracts cores from multiple reflectivity threshold information. ISCIT filters data at both steps

8. Storm Cell Identification and Tracking (SCIT) Vertically associates 2D feature centroids across adjacent elevation scans to detect 3D storm cells. Time associates 3D storm cell centroids across adjacent volume scans to determine tracks, trends (time and time-height), and predict future centroid positions.

9. Storm Cell Identification and Tracking (SCIT)

10. Hail DetectionAlgorithm (HDA) Objectives: Estimate the probability of any size hail associated with a storm Estimate the probability of severe hail (> 2 cm) Estimate the size of the largest hail stones Uses mesoscale model data for thermodynamic information.

11. HDA Probability of Hail (POH) Estimate the probability of any size hail associated with a storm H45 = Height of the 45 dBZ echo AGL (km) H0 = Height of the melting level AGL (km)

12. HDA Severe Hail Index (SHI) Vertically Integrated Liquid (VIL) (Emphasis given to lower dBZ) To remove “hail contamination” Hailfall Kinetic Energy (E) (Emphasis given to higher dBZ) And emphasis to those dBZ above the melting layer

13. HDA Severe Hail Index (SHI) Weighted by thermodynamic profile Obtained manually from nearby sounding, or Obtained automatically from mesoscale model analysis Greater temporal and spatial resolution Prob. Of Severe Hail (POSH; dia > 1.9 cm) and Max. Estimated Hail Size (MEHS) derived from SHI (Witt et al. 1998)

14. HDA Output in Cell Table

15. Enhanced Hail Diagnosis Algorithm (EHDA)

16. Neural networks are used to make predictions for: Probability of severe hail Maximum expected hail size Conditional probabilities for three size categories Coin-size hail (0.75 - 1.5 inch; 2-4 cm) Golf ball-size hail (1.5 - 2.5 inches; 4-6 cm) Baseball-size hail (? 2.5 inches; ? 6 cm) Enhanced Hail Diagnosis Algorithm (EHDA)

17. Mesocyclone Detection Algorithm (MDA) Objectives: To detect all significant vortices in a storm To diagnose the vortex’s strength, depth and its evolution To estimate the probability that the detected vortex will be associated with a tornado or severe weather at the surface in the next 20 minutes

18. Mesocyclone Detection Algorithm (MDA) New paradigm - detect then diagnose Advanced classification schemes (e.g., low-topped meso), and 3D diagnostics (e.g, MSI, Neural Network probabilities). Advanced display concepts (filters, tables). Better detection and diagnostic skill than NEXRAD predecessor. Addition of Near-Storm Environment data increases skill (still under development)

20. MDA Details

21. Mesocyclone Detection Algorithm (MDA)

22. TVS Detection Algorithm (TDA) Objectives: To detect the intense vortices associated with Tornadic Vortex Signatures (TVS) To forecast the movement of TVSs.

23. TDA Details

24. TVS Detection Algorithm (TDA)

25. Damaging Downburst Prediction and Detection Algorithm (DDPDA) Uses multiple-sensor-observed precursors to predict downbursts Uses LDA to develop a prediction equation on training data. Also detects divergent signatures at low-elevations.

26. Damaging Downburst Prediction and Detection Algorithm (DDPDA) Uses radar reflectivity and velocity, and mesoscale model data for downburst precursors (prediction) Strong convergence at 1-6 km AGL Rapidly descending core at high altitudes Mid-altitude rotation Strong storm-top divergence Weak shear, high instability (e.g., Florida, Arizona) Severe (> 50kts) yes/no Uses radar velocity for low-level winds > 30 kts (detection).

27. SSAP algorithms: SCIT, HDA, TDA, MDA, DDPDA Signature detection based on single-radar data. Disadvantages of single-radar algorithms: Products generated at end of volume scan Only 5-6 minute updates – storm evolution is fast Poor sampling within cone-of-silence and at far ranges Products all keyed to individual radar volume scan and radar domain (azimuth/range/elevation) Legacy WDSSI Algorithms

28. The Warning Challenge To reduce the uncertainty and improve the accuracy of a prediction, a warning forecaster will integrate more information about a storm as viewed by other radars and other sensors:

29. New Severe Weather Algorithm Requirements

30. Multiple radars provide one answer

31. WDSSII Multiple Radar SSAP Runs traditional SSAP algorithms using multiple radar input (and NSE data). Can use adjacent radars to fill cones-of-silence. Outputs information rapid intervals (60 second updates); can be as fast as individual elevation scan updates using “virtual volume scans”. “Virtual Volume” also works in single-radar mode if coverage or outages dictate. More accuracy and increased lead time!

32. WDSSII Multiple Radar SSAP Multiple-sensor: Uses mean wind information from mesoscale model to “drift” 2D features in space and time. Products keyed to 4D earth-relative coordinate system (lat, lon, height, time). Radar data is time-synchronized and corrected to UTC. Designed to be VCP independent, and can be integrated with other “gap-filling” radar platforms (TDWR, ASR, PAR, SMART-R, NETRAD, foreign radars, commercial radars).

33. MR-SSAP Rapid Update 20 May 2000 KTLX (OKC)

34. MR-SSAP Rapid Update 20 May 2000 KSRX (Fort Smith)

35. MR-SSAP Rapid Update 20 May 2000 KINX (Tulsa)

36. WDSSII MR-SSAP Rapid Update 20 May 2000 Three Radars

37. MR-SCIT Cell Table

38. WDSSII 3D Multiple-radar grid applications Mosaic data from multiple radars to create a 3D Cartesian lat/lon/ht grid. Uses time-weighting and inverse distance weighting schemes. Advect older data in multiple ways Run algorithms on rapidly-updating 3D grids: 3D reflectivity field for VIL, echo top, LRM, hail 3D velocity derivative fields for vortex (rotation) and wind shift and windstorm (convergence/divergence) detection. Easy to integrate other sensor information (NSE, satellite, lightning, etc.).

39. WDSSII Multiple-Radar 3D Reflectivity Mosaic

40. WDSSII Gridded Hail Products A new paradigm in hail information delivery Improves public service by giving them geo-spatial information on hail size versus a simple yes/no. Geospatial info also facilitates improved verification. Coupled with NSSL motion estimation algorithm, capability exists to predict short-term hail swaths.

41. Gridded Hail Products Gridded hail diagnoses and hail accumulation products Integrates NSE data from mesoscale models

42. Gridded Hail Products Gridded hail diagnoses and hail accumulation products Integrates NSE data from mesoscale models

43. Vortex Detection and Diagnosis (VDDA) Linear-Least Squares Derivatives (LLSD) of velocity Rotation and Divergence Multi-radar mosaic

44. Vortex Detection and Diagnosis (VDDA) Linear-Least Squares Derivatives (LLSD) of velocity Rotation and Divergence Multi-radar mosaic

45. Vortex Detection and Diagnosis (VDDA) Linear-Least Squares Derivatives (LLSD) of velocity Rotation and Divergence May 3 1999 Tornado Paths from shapefile Multi-radar mosaic

46. Multi-scale Storm Segmentation Algorithms Use of multi-scale statistical (versus heuristic) approaches for storm, vortex, and boundary detection. Also can be used for Infrared satellite feature identification (e.g., storm cold cloud tops) or lightning flash density.

47. A novel method of performing multi-scale segmentation of radar reflectivity data using statistical properties within the radar data itself. The method utilizes a K-Means clustering of texture vectors computed within the reflectivity scan. Uses, besides the actual reflectivity value within a gate, the distribution of reflectivity values around that gate. Multi-scale Storm Segmentation Algorithms

48. Motion Estimation Sophisticated technique using statistical segmentation and error analysis. Can be used on dBZ, IR satellite, VIL, lightning density, etc. Produces high-resolution motion field that can be used to predict hail, precipitation, rotation, lightning, etc.

49. Multiple-sensorLightning Prediction Under Development Using Neural Network for growth and decay Input data include multiple radars (MR), lightning density, and mesoscale model analyses. Uses WDSSII Motion estimation for future location

50. Radars other thanWSR-88D Terminal Doppler Weather Radar (TDWR) Research Polarimetric Radar Dopplers-On-Wheels Other Radars (commercial, ASR, PAR, etc).

51. WDSS-II The pace of this innovative development would not have been possible without the WDSSII as an effective R&D tool and testing platform. These are ideas that have been “in the queue” for many years at NSSL, only now coming to fruition. There are many more ideas that have yet to be implemented (some from NWS folks). Represents a quantum leap in the improvement of warning and situational awareness technology.

52. Questions?