1. Nic Wilson’s M.S.P.M. Research
A Progress Report of Work Completed
15 July 2005

2. Research Topics Case Study Identification Data Ingest TITAN
Flash Extent Density
The Application of Total Lightning to the Auto-nowcaster
Growth/Decay Membership Functions
Future Research and Work

3. Case Study Identification
On 16 May 2005 I visited the Ft. Worth WFO to meet with the SOO, Greg Patrick
He oriented me with their LDAR II total lightning display and their stand-alone ANC box
Possible case studies for my research were browsed and identified

4. Case Studies Used for Research
5 April 2005: A squall line develops along the dryline in the DFW area, producing many hail reports
25 April 2005: 3 distinct supercells track across the DFW area, dropping 2 weak tornadoes
25 May 2005: 2 large multi-cell clusters track southeast across the DFW area
14 June 2005: An overnight MCS tracks south across the DFW area

5. Data Ingest
NetCDF files that are identical to those sent to the Ft. Worth WFO are obtained from Vaisala
The NetCDF files contain: VHF source points, VHF source points divided into 3km layers, flash extent density and flash initiation points
NCAR’s Niles Oien converts the NetCDFs into the mdv (Meteorological Data Volume) format used for 2- and 3-D display at NCAR

6. Data Ingest Con’t
The mdv files are imported into CIDD (Configurable Interactive Data Display) which acts as the Linux display system in the ANC environment
CIDD is a user friendly display application with field, cross-section, movie and overlay capabilities

7. The Auto-nowcaster The ANC has two output components
1.) 60-minute Initiation Likelihood Field
2.) 60-minute Nowcast Field
The following are examples of their output

8. KFWS WSR-88D 13 July 2005 – 20:38 UTC

9. Initiation Likelihood Field

10. 60-minute Nowcast Field

11. TITAN
Thunderstorm, identification, tracking, analysis and nowcasting (TITAN) is currently used within the ANC to identify thunderstorm cells via reflectivity and area thresholds
Maximum dBZ and the normalized area growth rate attributes from both > 35 dBZ and > 45 dBZ cells derived from TITAN are used in the ANC to grow, maintain or dissipate ongoing storms in the 60-minute Nowcast Field

12. TITAN Con’t
The main storm cell attributes at interest for use in the ANC are the following:
Storm Max: Determines the maximum dBZ or FED value in a TITAN identified cell
Normalized Area Growth Rate: A history weighted trend of the storm cell’s area (-1 equates to the storm will decay by half its area in one hour, 0 equates to it maintaining its are and 1 indicates it will double in size in an hour)

13. TITAN Con’t
TITAN will be applied to the total lightning data to track its attributes as is done with the reflectivity data
FED (flash extent density) will be used at the request of Vaisala to identify “lightning cells”
Just like the radar reflectivity, the FED data exists on a cartesian grid which TITAN was created to be run on

14. Flash Extent Density
FED was created by Vaisala last year as a more representative way to display total lightning information
Temporal and spatial constraints are applied to the VHF sources to re-create a lightning flash
If a reconstructed flash passes thru a 1 km^2 grid box then it is given a “hit”
The unit for FED is hits per km^2 per min.

15. Flash Extent Density Con’t
FED is not as sensitive to LDAR II’s drop-off in VHF sources with distance from the network
The FED method helps to normalize the effect of decreasing VHF source detection efficiency with range because flash detection efficiency decreases at a much slower rate with increasing distance from the center of the LDAR II network
Previous lightning cell identification projects have used traditional VHF sources so this research is the first of its kind

16. What Does FED Physically Represent?
Flash initiation points are usually in the area of reflectivity gradient on the outskirts of the main precipitation core
Reflectivity gradients are proxies for gradients in vertical velocity leading toward more charge separation and increased lightning activity
Cloud flashes are favored in the downshear reflectivity gradient where precipitation particles have been advected by the upper-level winds

17. What Does FED Physically Represent? Con’t
The FED identified cells are downshear from the radar identified cells but similar in area
The higher the FED value, the more intense the storm’s updraft and charge separation mechanisms

18. FED Attributes
The maximum FED value was found to have a better correlation with the normalized growth rate than the average FED value
The inherent nature of the FED algorithm favors that the maximum FED value will be a successful indicator of storm strength

19. FED Attributes
The NetCDF data provided by Vaisala is in two-minute segments
The 2-minute data is very beneficial for forecasters to identify short-term variations in a storm’s character, but is too inconsistent and neglects data over the 5-minute period that ANC is run at
Niles Oien created an application to combine the two-minute segments into 4-minutes that are much better for cell identification and trending

20. The Application of Total Lightning to Nowcasting
The application of the LDAR II data to the ANC must fit within the capabilities of TITAN
10 distinct storms from the 4 days worth of archived events were identified for analysis using TITAN

21. TITAN Variations
Two different thresholds of TITAN were run on the FED data, they were chosen for their similarities in area to the ANC’s 35 dBZ and 45 dBZ TITAN storm cells
1.) 0.25 FED: Encompasses all lightning activity observed over the four minutes (.25 FED translates to 1 hit per km.^2 per 4 min.)
2.) 1.0 FED: Encompasses the convective core of lightning activity (1.0 FED translates to 4 hits per km.^w per 4 min.)

22. Data Comparison
WSR-88D radar data comes across every 5 to 6 minutes, while the FED data is available every 4 minutes
This required the data to be re-sampled to 5 minute segments (chosen because the ANC is run every 5 min.) in order to be compared
To do this the radar data was oversampled and the FED data was undersampled

23. Lag Correlation Analysis
To evaluate the effectiveness of the TITAN-derived storm attributes as forecast tools, lag correlations were calculated comparing the 35 dBZ to 0.25 FED cells and 45 dBZ to 1.0 FED cells
The results provide some insight into the optimal time periods to use them as forecast tools and the intrinsic relationship between reflectivity and lightning activity
The following charts illustrate some of the results

24. Lag Correlation Results

25. Lag Correlation Results Con’t

26. Lag Correlation: Potential Skill at Forecasting Future dBZ Intensity

27. Skill Score Analysis
2 x 2 contingency forecast tables were set-up to evaluate the operational performance of the various TITAN-derived normalized growth rates
Analysis was done on forecasts of 15, 30, 45 and 60 minutes using a baseline of 0 as the cut-off for growth or decay
Additionally, the cut-offs (0.3 and 0.5 respectively for 35 and 45 dBZ, to provide a bias toward more decay forecasts) used for the ANC 60 minute forecasts were analyzed to compare their actual performance

28. Skill Score Analysis Con’t

29. Skill Score Analysis Con’t

30. Skill Score Analysis Con’t

31. Skill Score Observations
The poor performance of the 35 dBZ normalized growth rate is a surprise – it currently garners the most weight in the ANC’s growth/decay fuzzy logic
It is being looked into as whether this is an anomaly from the 10 storms I analyzed, or a concerning trend in its performance
The strong performance of the 0.25 FED growth rate suggests that it will help improve the performance of the ANC’s growth/decay fuzzy logic

32. Future Work
Develop new membership functions based on the maximum FED and normalized growth rates
Obtain VHF source point data from Vaisala to create a “lightning top” product for implementation into the ANC
Run statistical analysis with the new membership functions within the ANC to evaluate their performance