1. Edward Mansell National Severe Storms Laboratory Donald MacGorman and Conrad Ziegler National Severe Storms Laboratory, Norman, OK Funding sources in the Office of Naval Research, NSF, NSSL, the National Research Council, and the Oklahoma State Regents Lightning at NSSL: Numerical Modeling and Data Assimilation

2. Storm electrification modeling: Basic understanding of electrification processes Lightning-storm relationships Lightning data assimilation (COAMPS) On mesoscale (>10km), control convection parameterization scheme. Storm-scale EnKF radar data assimilation Modeling Activities/Capabilities

3. Storm Model (COMMAS) Full dynamic, microphysical, and electrical simulation model Collisional charge separation, explicit small ion processes, branched lightning. Two-moment bulk microphysics: Predict particle concentrations (and mass) for all hydrometeors (droplets, rain, ice crystals, snow, graupel, hail) and simple bulk CCN. MPI capable [Mansell et al. 2002, 2005, (2009 in review), also Fierro et al, Kuhlman et al.]

4. Supercell Simluation

5. Small Storm Simluation

6. Lightning and Charge Structure Inferred charge structure from lightning sources + + – Model-simulated charge structure and lightning West -30km -25km East Altitude (km)

7. Sensitivity to CCN concentration Volume

8. Simulated Lightning Rate Correlations Isolated cells: 0.7 multiple cells: 0.5

9. Total Flash Rate Correlation Coefficient with ParameterIsolated StormsStorm Systems Maximum Elec. Field0.080.10 Graupel Volume0.690.50 Updraft Mass Flux (-10°C)0.820.39 Updraft Volume (>10 m s -1 )0.730.29 Cloud Ice Mass Flux (- 30°C) 0.790.65 Cloud Ice Mass0.250.36 Rain Mass0.640.63 Maximum Updraft0.300.06

10. Assimilating Lightning Data o Unambiguous indicator of [electrified] convection o Mixed-phase ice precipitation (graupel) is present o Can complement radar data assimilation: Fill in radar voids: Mountains, oceans, other countries, radar drop-outs Potential use of flash rate relationships (storm intensity, e.g., updraft volume/mass flux) and satellite-based (e.g., GOES) total lightning detection (total flash rate in particular) [Mansell, Ziegler, and MacGorman, 2007]

11. Method is similar to Rogers et al. (2000) for radar assimilation. Force/suppress Kain-Fritsch based on presence/absence of lightning. Add up to 1.0 g/kg of moisture to get deep convection (10m/s updraft, 7km cloud depth). Allow KF scheme to generate precipitation rates and latent heating and evaporative cooling. (Other methods can be used to adjust or impose latent heating rates based on rainfall relations) LMA sources KS CO NE Case study with COAMPS on 20-21 July 2000

12. Test caseSpin-up period: Obs. Precip vs. Control

13. Spin-up period: Obs. Precip vs. Assimilation

14. Spin-up period: Control vs. Assimilation

15. Surface Temperature (C) Warm-start Model Conditions: ControlAssimilation

16. 01 UTC 21 July

17. 02 UTC 21 July

18. 0-6 hr Precip: Obs and forecasts Control Fcst from ltg. assim.

19. Summary Cloud modeling investigates basic aspects of lightning production by storms. Lightning data assimilation can be an effective means to initialize the effects of prior/current convection: Cold outflows better placed by forcing KF. Good potential for rapid update cycling and providing conditions for higher-resolution nest. Assimilation-based forecast shows more skill than control forecast for first few hours. Does little to correct errors in large scale fields, so range of forecast improvement limited ( < 6 hr).

20. Issues Must consider lightning location accuracy in terms of model resolution. What does a “flash” represent in the observing system? (large variations in flash extent) Method tied to data source. For resolved convection or convection-permitting EnKF, need to relate lightning to model variables or derived quantities. And/Or use lightning mainly to initiate deep convection.