1. Assimilation of Pseudo-GLM Observations Into a Storm Scale Numerical Model Using the Ensemble Kalman Filter Blake Allen University of Oklahoma Edward Mansell NOAA / National Severe Storms Laboratory Funding Provided by NOAA-NESDIS/JCSDA

2. Background: Pseudo-GLM Obs Flash observations were generated from LMA data, using a flash separation algorithm (MacGorman et al 2008) to specify individual flashes. Each flash was then mapped to an approximately 8km x 8km two dimensional grid. For each flash that crossed a grid box, a flash event was added to the box’s total.

3. Example Set Of pGLM Observations -> Flash extent density ~ 80 min -1 -> Flash extent density ~ 1 min -1 Lat Lon May 8, 2003 22:09Z

4. EnKF Setup Ensemble Square Root filter used to produce analyses at 1, 3, or 5 minute intervals with a 40 member ensemble. Pseudo-obs were placed at 6500 m height, with a vertical localization radius of 35 km and a 16 km horizontal localization radius. Observation error was set to 10%-15% of the maximum flash rate in each storm. Thermal bubbles and smooth noise (Caya 2005) were used to initiate convection and create/maintain ensemble spread.

5. Observation Operators First guesses at observation operators were produced by finding a linear fit between flash rate and microphysical variables from simulations using an explicit lightning model while assimilating radial velocity radar data. Various linear relationships between graupel mass and flash rate, graupel volume and flash rate, and Non-inductive charging and flash rate were tested. The best results were found with the relationship Flash extent density = (0.017)*(graupel volume)

6. Observed During STEPS Moved through an area well-sampled by the STEPS LMA Radar data available from CHILL radar Low-shear, low-CAPE environment Evolved through multiple formations throughout its lifetime Model Setup: 1km horizontal resolution, 2-moment microphysics with 4 ice categories. 6 June 2000 Airmass Storm

7. 6 June 2000 Results Ensemble Mean Reflectivity 22:30Z June 6 (1 min. assimilation) Observed Reflectivity 22:30Z June 6 6 June 2000 Airmass Storm

8. 6 June 2000 Results Ensemble Mean Reflectivity 00:00Z June 7 (1 min. assimilation) Observed Reflectivity 00:00Z June 7 6 June 2000 Airmass Storm

9. 6 June 2000 Results Ensemble Mean Reflectivity 01:00Z June 7 (1 min. assimilation) Observed Reflectivity 01:00Z June 7 6 June 2000 Airmass Storm

10. 6 June 2000 Results 5 min pGLM assimilation 1 min pGLM assimilation Vr assim 5 min pGLM assimilation 1 min pGLM assimilation Vr assimilation Min = 0 m/s Max = 35 m/s Min = 0 km 3 Max = 700 km 3 Maximum Updraft Speed 6 June 2000 Airmass Storm Graupel Volume > 0.5 g/kg

11. Moved through an area well-sampled by the Oklahoma LMA Radar data available from KTLX WSR-88D, and previous work has been done using the storm as a test case for assimilation of radar velocity and reflectivity data (Dowell et al 2010). High-shear, high-CAPE environment Produced multiple tornadoes, including a long-tracked F4 that struck Moore OK and other parts of the Oklahoma City metro area. Model Setup: 1km horizontal resolution, 2-moment microphysics with 4 ice categories. 8 May 2003 Supercell

12. 22:09Z 8 May 2003 Supercell Ens. Mean reflectivity Near-surface radar reflectivity around the time of the first tornado Observed Reflectivity 22:09Z Single member reflectivity

13. 8 May 2003 Supercell Vr assimilation 1min pGLM assim 5 min pGLM assimilation Vr assimilation 1min pGLM assimilation 5 min pGLM assimilation Min = 0 km 3 Max = 12000 km 3 Min = 0 m/s Max = 70 m/s Maximum Updraft Speed Graupel Volume > 0.5 g/kg

14. 8 May 2003 Results 8 May 2003 Supercell Probabiliy of Vorticity > 0.016 s -1 at 1.75 km model height Vr assimilation 1 minute pGLM assimilation 8 May 2003 Supercell

15. Conclusions 1.) EnKF assimilation of pseudo-GLM data, using a single observation operator, can produce analyses that capture the basic reflectivity structure of multiple storm types, and can produce maximum updraft speeds comparable to those obtained when assimilating Doppler radar radial velocity. 2.) In the supercell case, using high temporal resolution data captured the development of the low-level mesocyclone. 3.) At lower temporal resolutions, the strength of both storms dropped later in the runs, although this may be case specific – 8 May had a capping inversion and 6 June had weak instability. 4.) Since the GLM will produce observations over a much larger area than radar observations are available, these results show promise that EnKF assimilation of GLM data can be useful stand-in for assimilation of radial velocity data in areas where radar data is lacking or of poor quality. Summary / Conclusions

16. References A. Caya et al 2005: A comparison between the 4DVAR and the ensemble Kalman filter techniques for radar data assimilation. Monthly Weather Review D. Dowell et al 2010: Ensemble Kalman Filter Assimilation of Radar Observations of the 8 May 2003 Oklahoma City Supercell: Influences of Reflectivity Observations on Storm- Scale Analyses. Monthly Weather Review D. MacGorman et al 2008: The Thunderstorm Electrification and Lightning Experiment. BAMS Acknowledgements Thank you to Kristin Calhoun for converting LMA data to pseudo-GLM observations, and to David Dowell for providing radar data for the 6 June case. Funding Provided by NOAA-NESDIS/JCSDA Questions?

18. EnKF Assimilation of Pseudo-GLM Observations (8 May 2003 Moore, OK Supercell) Run A: Flash Rate = 0.017( Graupel Volume ) Run B: Flash Rate = 0.039( NI charge sep. rate) Simulated Reflectivity Probability of Vorticity > 0.016 s -1 Observation Operator Comparison