1. TITAN - identifying and tracking convective storms as objects 1 Heuristic Probabilistic Forecasting Workshop Munich, Germany 30-31 August 2014 Mike Dixon 1 and Alan Seed 2 1 National Center for Atmospheric Research, Boulder, Colorado 2 Centre for Australian Weather and Climate Research, Melbourne, Australia

2. Identifying and tracking storms as 3-D objects Why? – –Nowcasting of severe weather – –Storm studies – –Climatology studies What? Storm identification and properties – –3D identification based on a reflectivity threshold – –The dual threshold technique – –Identifying convective regions – –Scaling objects appropriately for forecast lead time How? Storm tracking – –Storm overlap – –Optimized matching – –Estimating the motion – –Correcting motion using optical flow field tracking 2

3. STORM IDENTIFICATION 3

4. Primary identification – find contiguous regions with reflectivity exceeding a specified threshold (in this case 35 dBZ) 4

5. Secondary dual-threshold identification This allows us to split up storms that have just ‘touched’ instead of actually merged Find regions at lower threshold (in this case 35 dBZ) Within those regions, find sub-regions at the higher threshold (in this case 40 dBZ) 5

6. Dual-threshold identification Deciding which sub-regions to use and growing the sub-regions to the original outline Find valid regions – i.e. those with significant sub-regions at the higher threshold Grow the valid regions out to the original threshold boundaries 6

7. Sometimes, for clarity and simplicity, it is preferable to represent storms as ellipses instead of polygons Polygon representation for storms can be complicated Ellipses are easier on the eye 7

8. Handling mixed convective / stratiform situations (a) Identify the convective regions within the radar volume (b) Constrain the storm identification to the convective regions only 8

9. Example of scan with large regions of stratiform / bright-band, along with embedded convection 9 Vertical section along line 1-2 Column-max reflectivity Bright-band Convection Convective area Stratiform area

10. Titan tends to merge both the convective and stratiform regions into a single storm identification. Therefore we need to isolate the convective regions. 10 Merged convective and stratiform regions

11. The Steiner et. al (1995) method for convective partitioning was tested. However, it seemed to over-identify convective areas. The Steiner method computes the difference between the reflectivity at a point and the ‘background’ reflectivity defined as the mean within 11 km of that point. The method estimates the convective regions based on the reflectivity difference, determining the radius of convection as a function of the difference value. 11 Stratiform area

12. A modified method was developed, based on the ‘texture’ of reflectivity surrounding a grid point. ‘Mean texture’ of reflectivity – mean over the column of texture = sqrt(sdev(dbz 2 )) computed over a circular kernel 5km in radius, for each CAPPI height. Convective (cyan) vs Stratiform (blue) partition computed by thresholding texture at 15 dBZ 12

13. Storm identification on all regions compared with using the convective regions only Storms identified using a 35 dBZ threshold. The storms include the regions of bright-band, leading to erroneously large storm areas Storms using the same 35 dBZ threshold but including only the convective regions 13

14. Spatial scaling appropriate for longer-term nowcasts - investigating approaches for a 2-hour lead time.  For nowcasts of 30 to 60 minutes, the scale of storms as measured by the radars is appropriate.  For longer lead time forecasts, say 1 hour to 2 hours, we want to identify and track only larger scale features, so we need a technique to isolate those features. 14

15. From Seed (2003) event lifetime vs. spatial scale based on computed median correlation time for precipitation events 15 A. Seed, J Appl Meteor, Vol 42, No 3, March 2003. ~50km 2 hrs 30 mins ~12km

16. From Germann et. al (2006), for an expected lifetime of 2 hours, the spatial scale should be between 32 and 64 km. We choose to test with a spatial scale of 50 km. 16 Germann et. al, J Atmos, Vol 63, No 8, August 2006. 2 hr lifetime ~50 km spatial scale 30 min lifetime ~8 km spatial scale

17. Computing the spectrum of the reflectivity field shows the spatial frequency of the scene Reflectivity over a 1200km x 1200 km grid 2D FFT-based spectrum of reflectivity field 17

18. Filtering the spectrum allows us to isolate the larger scale features. Reflectivity filtered for features 50 km and larger Spectrum filtered to retain features of 50 km scale and larger 18 This includes the stratiform regions. What if we use this procedure on the convective areas only?

19. Applying the 50km spatial filter to the convective regions highlights the larger scale convective features Convective reflectivity regions Convective reflectivity filtered for features 50 km and larger 19

20. Comparing convective storm identification at different scales Identification of smaller-scale convective features, minimum size 30 km 2 Identification of features at the 50km spatial scale, minimum size 2500 km 2 20

21. STORM TRACKING 21

22. The intial tracking step looks for overlaps between storm envelopes at consecutive times. 22 The current storm outline is in white, while the storm locations from the previous scan are in yellow. If multiple storms overlap a single storm, the largest overlap is used.

23. The secondary tracking step optimizes the pairing between storms not matched by the overlap step. 23 We minimize a cost function designed to identify likely matches based on location and storm size. Cost = (Distance between centroids) * weight1 + (Difference in Vol 1/3 ) * weight2

24. Identifying mergers and splits Mergers occur if the forecast location of two or more storms lie within the observed envelope of a single storm at the next time step Splits occur if the observed location of two or more storms at the next time step lie within the forecast envelope of a single storm 24

25. Example of a storm split Storm envelope at time 1 Storm splits into 4 parts at time 2 25

26. Forecasts are based on the linear extrapolation of previous locations, weighted by age (higher weight for more recent scans). Storm size is forecast to grow or decay based on size history. Forecast using ellipses for small-scale storms. The past locations are shown in yellow. Forecast using polygons for larger-scale features. Each red outline is a 5-minute forecast, out to 30 minutes. 26

27. Similarly we can make forecasts of the storm area or volume, based on extrapolation of recent history. And we can plot the time history of storm properties. Forecasts of the storm area and volume, for each scan in the storm track lifetime. Time height profile of maximum reflectivity. 27

28. Sometimes we get tracking errors in challenging situations 28 Example of radar scanning at 10 minute intervals, with fast moving storms. This can lead to problems with correct tracking.

29. Using a field tracked such as Optical Flow allows us to estimate the ‘background’ movement of the echoes. 29

30. Example of tracking errors. Neither storm in the NE quadrant is correctly tracked. 30 In this case no overlap occurs because of small storm sizes, long time between scans and fast movement.

31. By applying the Optical Flow vectors to storms with short histories, we can improve both tracking the forecast accuracy. 31

32. How well did we do with forecasting the line filtered using a 50 km spatial filter? Forecast at 23:05 UTC on 2014/06/08. Shown are 4 x 30 minute forecasts, to 2 hours. 2-hour verification at 01:05 UTC on 2014/06/09. This demonstrates that we can have some success forecasting large-scale features at longer lead times. 32

33. SEVERE STORM FORECASTING 33

34. Severe storm forecasting.  In the nowcasting role, a tool such as Titan appears to be better suited to severe storm forecasting than precipitation forecasting.  One primary advantage of the ‘storm as an object’ approach is that severe weather attributes such as mesocyclones, hail, heavy rain, turbulence and lightning can be ‘attached’ to the storm object and carried along with the warning. 34

35. Example 1: Thunderstorm Interactive Forecast Service (TIFS) Australian Bureau of Meterology 35 TIFS takes TITAN results and presents them to the user in a suitable form for forecasting

36. Example 2: Automated Weather Alert System, State of Sao Paulo, Brazil 36 Warnings based on Titan forecasts are automatically generated for individual counties

37. Advanced dual-polarization radars can provide additional information about the severe event RHI from SPOL S-band radar, showing severe storm vertical section (RHI) Same RHI showing the NCAR dual- polarization Particle ID product, with a hail core (yellow) and heavy rain (red) 37 This additional information would enhance the utility of the forecast

38. Storm properties can include:  Geometric and reflectivity- derived properties: –Location –Area –Tops / vertical extent –Maximum reflectivity –Rate of growth –VIL  Properties derived from other applications: –Presence of hail –Hail size –Heavy rain –Precipitation flux –Presence of lightning –Lightning rate –Presence of mesocyclone (supercell flag?) –Tornado –Turbulence 38

39. Storm Studies and Climatology  A tool such as Titan can help to condense vast quantities of radar data into a more manageable size.  Having done so, it is possible to study both individual storm cases, and to analyze long periods to determine the climatology of convection and severe weather. 39

40. You can summarize an event by displaying all of the tracks making up that event. 40 Magenta lines show the movement vectors of the storm centroids. Yellow ellipses depict the storm extent.

41. You can have some fun and animate the event as it unfolds 41

42. Example of a Climatology Study  The following 2 slides show the results of a climatology study of the Madden-Julian Oscillation (MJO), using data taken by the NCAR S-Pol radar in the Maldives during the DYNAMO field experiment, October 2011 to January 2012.  These results are thanks to Sachin Deshpande, of the Indian Institute of Tropical Meteorology (IITM), Pune, INDIA 42

43. Storms were identified & tracked by TITAN throughout its lifetime. The majority of storms were of short durations, i.e. predominantly less than 2 hours. Active MJO periods showed long lived storms compared to suppressed MJO periods. Short lived storms are due to shallow and isolated convection associated with SubMCS. Long lived storms are due to organized convection associated with MCSs. DYNAMO field project Duration of storms during Active and Suppressed phases of MJO Source: Personal communication, Sachin Deshpande, Indian Institute of Tropical Meteorology (IITM), Pune, INDIA

44. Frequency of occurrence of storms of different scales during DYNAMO Larger storm areas and top heights for MCS (during 15-30 Oct) as compared to SubMCS (during 1-14 Oct). Contribution of small sized storms to total population is more compared to large size storms but the contribution of MCSs is maximum to the total storms when area is considered. Shallow convective echoes (SCEs) Etops lower than 1 km below 0 o level Deep convective cores (DCCs) ETops : at least 8 km Includes young and vigorous cells with strong updrafts Wide convective cores (WCCs) Horizontal area: at least 800 km 2 Contains region where intense individual cells merge together 01 Oct 14 Oct 2011 15 Oct – 27 Oct 2011 Source: Personal communication, Sachin Deshpande, Indian Institute of Tropical Meteorology (IITM), Pune, INDIA Suppressed phaseActive phase

45. 45 THANK YOU