Automated Thunderstorm Identification and Tracking with GLM Data

1. Automated and Objective Thunderstorm Identification and Tracking using Operational Geostationary Lightning Mapper (GLM) Data 1 Kelley Murphy, Dr. Lawrence Carey, Dr. Christopher Schultz, Nathan Curtis 2 3 , 4 2 1 Earth System Science Center, UAH, Huntsville, AL Department of Atmospheric Science, UAH, Huntsville, AL Earth Science Branch, Marshall Space Flight Center, Huntsville, AL Natural Environments Branch, Marshall Space Flight Center, Huntsville, AL 2 3 4 Photo by Daniel Linek; IG: keepaneyetothesky

2. VILFRD • Schultz et al. (2016) created a new method for objectively identifying and tracking thunderstorms using a combination of radar & lightning data Vertically Integrated Liquid (VIL) & Flash Rate Density(FRD) are combined to define storm features • FLCT5 = averaged 5 min FRD • Values can range from 0 – 200 and are unitless BACKGROUND GOALS & METHODS RESULTS ONGOING/FUTURE WORK

3. VILFRD • Quantity is tracked using kmeans clustering within w2segmotionll in WDSS-II, resulting in a fully automated feature ID and tracking method (see: V. Lakshmanan, K. Hondl, and R. Rabin, “An efficient, general-purpose technique for identifying storm cells in geospatial images,” J. Atmos. Oceanic Technol., vol. 26, no. 3, pp. 523-37, 2009) • w2segmotionll set up in Schultz et al. (2016): - identify features by looking for VILFRD pixels of 100, decrementing by 20 until a floor value of 20 VILFRD - scale 5: features must be 162 BACKGROUND GOALS & METHODS RESULTS ONGOING/FUTURE WORK

4. Optimization for GLM BEFORE • Prior to GOES East’s launch, the lightning component of VILFRD incorporated GLM proxy flashes (Bateman 2013) - converted NALMA flashes into a “best guess” of what GLM would see once operational - proxy flashes were transformed to match the lower spatial resolution of GLM NOW • GOES GLM data is readily available • Transition the feature ID and tracking method to the GLM data stream, & optimize the method for the operational GLM data BACKGROUND GOALS & METHODS RESULTS ONGOING/FUTURE WORK

5. Optimization for GLM What were some initial ideas & observations? • Feature contour size influences storm statistics BACKGROUND GOALS & METHODS RESULTS ONGOING/FUTURE WORK

6. Optimization for GLM What were some initial ideas & observations? • Feature contour size influences storm statistics • The size of a single tracked feature varied more than anticipated with time - Could affect lightning variable trends (ie. flash rate density, optical energy) and algorithm applications (lightning jump) BACKGROUND GOALS & METHODS RESULTS ONGOING/FUTURE WORK

7. Optimization for GLM What changes could be implemented? • Create & test alternate versions of the feature ID and tracking method: - Minimize variability in feature boundary size - Ensure information is not missed outside of the feature contour BACKGROUND GOALS & METHODS RESULTS ONGOING/FUTURE WORK

8. Optimization for GLM What changes could be implemented? • Create & test alternate versions of the feature ID and tracking method: - Minimize variability in feature boundary size - Ensure information is not missed outside of the feature contour How? • Changes to w2segmotionll parameters ** • Changes to VILFRD formulation - Change type of data input BACKGROUND GOALS & METHODS RESULTS ONGOING/FUTURE WORK

9. Method Comparison Original Method New Method ▪ w2segmotionll: - start at 100 VILFRD - decrementing by 20 until 20 VILFRD ▪ w2segmotionll: - start at 70 VILFRD - decrementing by10 until 20 VILFRD ▪ no change to VILFRD formula ▪ scale 5 (must be 162 ) BACKGROUND GOALS & METHODS RESULTS ONGOING/FUTURE WORK

10. Method Comparison Case day: 03/03-04/19, 18 – 02 UTC: Tracked Feature #5 Original Method # of total tracked features: 41 New Method # of total tracked features: 43 BACKGROUND GOALS & METHODS RESULTS ONGOING/FUTURE WORK

11. Method Comparison Original Method BACKGROUND GOALS & METHODS RESULTS ONGOING/FUTURE WORK

12. Method Comparison New Method BACKGROUND GOALS & METHODS RESULTS ONGOING/FUTURE WORK

13. Method Comparison Original Method New Method BACKGROUND GOALS & METHODS RESULTS ONGOING/FUTURE WORK

14. Method Comparison Original Method New Method BACKGROUND GOALS & METHODS RESULTS ONGOING/FUTURE WORK

15. Summary • A more stable feature boundary leads to a more representative flash rate and other lightning property trends associated with that feature • This may help when using the algorithm for applications: - lightning jump (decrease false alarms?) - “An important finding was the GLM jumps seen as the storms weakened and seen in non-severe weak convection” • Sometimes, the features using the new method contained slightly less lightning than those tracked with the original Curtis (2018) Master’s Thesis BACKGROUND GOALS & METHODS RESULTS ONGOING/FUTURE WORK

16. Ongoing/Future Work How? • Changes to w2segmotionll parameters • Changes to type of data input - Groups instead of flashes BACKGROUND GOALS & METHODS RESULTS ONGOING/FUTURE WORK

17. Ongoing/Future Work • Lightning only? Original Method New Method BACKGROUND GOALS & METHODS RESULTS ONGOING/FUTURE WORK

18. Ongoing/Future Work • Building a database of case days & tracked features - large sample analyses; compare original method to new methods • Case days include other storm modes (ie. linear convection) • Applications: lightning jump BACKGROUND GOALS & METHODS RESULTS ONGOING/FUTURE WORK

19. Ongoing/Future Work • Look at other lightning variables and trends BACKGROUND GOALS & METHODS RESULTS ONGOING/FUTURE WORK

21. Future Work • Lightning only? Original Method New Method BACKGROUND GOALS & METHODS RESULTS CONCLUSIONS/FUTURE WORK