1. Developments in echo tracking - enhancing TITAN
Nowcasting Techniques
7.6
ERAD 2014
2 September 2014
Mike Dixon1 and Alan Seed2
1National Center for Atmospheric Research, Boulder, Colorado
2Centre for Australian Weather and Climate Research, Melbourne, Australia

2. Current work on TITAN enhancements
Separating convective regions from stratiform areas prior to storm identification
Applying spatial scaling to storm objects appropriately for forecast lead time
Correcting tracking errors using Optical Flow

3. Example case: convective outbreak in Colorado, 2014/06/08

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

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

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

7. 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 identifies the convective regions based on the reflectivity difference, determining the radius of convective influence as a function of the background value.
Stratiform area

8. 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(dbz2)) 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

9. 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

10. Example of convective partitioning for single radar with extensive bright-band
1 degree PPI for radar near Sydney Australia. Extensive stratiform region to the NE of the radar.
Vertical section (1-2) showing bright-band near the radar and convection further away
Stratiform region
Bright-band

11. Computing the texture and creating the partition for the single-radar case
Mean reflectivity texture over all levels
Convective areas shown in gray, with TITAN storm tracks

12. TITAN storms for all areas (left) and convective areas only (right)
TITAN storms including stratiform areas
TITAN storms on convective areas only

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

15. 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.
2 hr lifetime ~50 km spatial scale
30 min lifetime ~8 km spatial scale
Germann et. al, J Atmos, Vol 63, No 8, August 2006.

16. 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. Computing the spectrum of the reflectivity field shows the spatial frequency of the phenomenon
Reflectivity filtered for features 50 km and larger
Spectrum filtered to retain features of 50 km scale and larger
This includes the stratiform regions. What if we use this procedure on the convective areas only?

18. 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. Comparing convective storm identification at different scales
Identification of smaller-scale convective features, minimum size 30 km2
Identification of features at the 50km spatial scale, minimum size 2500 km2

20. 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.

21. IMPROVING Storm TRACKING USING OPTICAL FLOW

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

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

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

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

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

27. Using TITAN, you can have some fun and animate the event as it unfolds
Thank you

28. THANK YOU

29. Storm identification

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

31. Find regions at lower threshold (in this case 35 dBZ)
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)

32. Grow the valid regions out to the original threshold boundaries
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

33. 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

34. The intial tracking step looks for overlaps between storm envelopes at consecutive times.
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.

35. The secondary tracking step optimizes the pairing between storms not matched by the overlap step.
We minimize a cost function designed to identify likely matches based on location and storm size. Cost = (Distance between centroids) * weight1 + (Difference in Vol1/3) * weight2

36. 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

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

38. 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.

39. Time height profile of maximum reflectivity.
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.

40. SEVERE STORM FORECASTING

41. 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.

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

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

44. This additional information would enhance the utility of the forecast
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)
This additional information would enhance the utility of the forecast

45. 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

46. 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.

47. SEVERE STORM FORECASTING

48. 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.

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

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

51. This additional information would enhance the utility of the forecast
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)
This additional information would enhance the utility of the forecast

52. 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

53. 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.

54. 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

55. DYNAMO field project Duration of storms during Active and Suppressed phases of MJO
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.
Source: Personal communication, Sachin Deshpande,
Indian Institute of Tropical Meteorology (IITM), Pune, INDIA

56. Frequency of occurrence of storms of different scales during DYNAMO
01 Oct 14 Oct Oct – 27 Oct 2011
Larger storm areas and top heights for MCS (during 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 0o 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 km2
Contains region where intense individual cells merge together
Suppressed phase
Active phase
Source: Personal communication, Sachin Deshpande,
Indian Institute of Tropical Meteorology (IITM), Pune, INDIA