The EnKF Analyses and Forecasts of the 8 May 2003 Oklahoma City Tornadic Supercell Storm By Nusrat Yussouf 1,2 Edward Mansell 2, Louis Wicker 2, Dustan Wheatley 1,2, David Dowell 3, Michael Coniglio 2 and David Stensrud 2 1. Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, OK. 2. NOAA/National Severe Storms Laboratory, Norman, OK. 3. NOAA/ESRL/Global Systems Laboratory, Boulder, CO.

Motivation  Most storm-scale NWP modeling studies assume horizontally homogenous environmental conditions  Much easier to obtain a high-quality analysis of supercell storm than a accurate forecast  Stensrud and Gao (2010): Substantial improvement in storm forecast accuracy when using realistic inhomogeneous mesoscale environment  This work focuses on ensemble data assimilation experiments of tornadic supercell within full mesoscale complexity  In support of Warn on Forecast - a numerical model-based probabilistic convective-scale analysis and forecast system to support warning operations within NOAA A very short-range probabilistic forecasts of tornadic supercell storms

The 8 May 2003 Oklahoma City Tornadic Supercell KOUN Radar Observations at 22:10 UTC NWS Damage Path of OKC Tornado HPC Synoptic Scale Surface Analyses at 18:00 UTC Hu and Xue (2007)

Mesoscale Ensemble WRF-ARW v3.2.1  Mesoscale data assimilation on CONUS domain  18-km horizontal grid spacing; 51 vertical levels  Mean initial and boundary conditions from GFS final analysis 45 member mesoscale ensemble  IC/BC perturbations from WRF-Var (Torn et al. 2006)  Physics Options: - Cumulus: Kain-Fritsch - PBL: MYJ - Microphysics: Thompson - Shortwave Radiation: Dudhia - Longwave Radiation:RRTM - Land Surface: Noah Ensemble Adjusted Kalman Filter (EAKF) approach from the Data Assimilation Research Testbed (DART)

Mesoscale Data Assimilation  Observations assimilated: - Altimeter setting (p) - Temperature (T) - Dewpoint (Td) - Horizontal winds (u and v)  Observation platforms: - METAR, Radiosonde, Maritime and Automated Aircraft from MADIS  Adaptive prior inflation & localization (1600 obs)  Localization half width: 287/4 km for horizontal/vertical  Filter configuration adapted from Glen Romine’s system at NCAR Timeline of mesoscale data assimilation experiment: - Continous cycling for 3 days - Every 6 hour DA: 18 UTC May 5 – 12 UTC May 8 - Every 1 hour DA: 13 UTC May UTC May 9

A 45 member storm-scale ensemble  One-way nested down from mesoscale ensemble analyses at 21Z, May 8  2-km horizontal grid spacing, 450 x 360 km wide, 50 vertical levels  KTLX WSR-88D radar doppler velocity (Vr) and reflectivity (dBZ)  Radar data objectively analyzed to 4-km grid using OPAWS  Both adaptive inflation and additive noise to maintain spread  Adaptive localization (2000 obs)  Observation errors: Vr = 2 m s -1, Z = 5 dBZ  Localization half-width: 12/6 km for horizontal/vertical Timeline of storm-scale data assimilation experiment: - One hour DA every 3 minutes: 21 UTC – 22 UTC, May 8 - One hour ensemble forecast: 22 UTC - 23 UTC, May 8 Storm-Scale Data Assimilation

Storm-Scale Data Assimilation Experimental Design  Three ensemble DA experiments using different bulk microphysics schemes: - Thompson 1.5 moment (Thompson et al. 2004, 2008) Mixing ratio: Qc, Qi, Qs, Qr and Qg Number Concentrations: ice (Ni) and rain(Nr) - NSSL Variable Density Double Moment (NVD-DM; Mansell et al. 2010) Mixing ratio: Qc, Qi, Qs, Qr, Qg and Qh Number Concentrations: Nc, Nr, Ni, Ns, Ng and Nh - NSSL Fixed Density Single Moment (NFD-SM; Gilmore et al. 2004) Mixing ratio: Qc, Qi, Qs, Qr and Qg  Remaining physics options are identical to mesoscale ensemble

Observation-Space Diagnostics: rmsi and total ensemble spread Vr statics are calculated at all observed values over the entire domain Z statistics are calculated where observed Z > 10 dBZ Ensemble spread for reflectivity is consistently smaller than the rmsi Radial velocity ensemble spread is comparable to rmsi Reflectivity rmsi from Thompson is relatively smaller during the later assimilation period rmsi and spread are similar in magnitude for the 3 microphysics Scheme experiments for Vr

Observation-Space Diagnostics: Consistency ratio Consistency ratio = (ens. variance + obs-error variance) / (mean-squared innovation) Reflectivity consistency ratio is well below 1.0

Analyses at 2200 UTC at 1 km AGL Thompson NFD-SM NVD-DM mesocyclone Vorticity contours from to 0.01 at s -1 mesocyclone KTLX Reflectivity Obs. Member 12 Member 14 Member 31 U-V Winds vector (m/s) The areal extent and the reflectivity distribution in the forward flank region is closer to the observation in Thompson and NVD-DM scheme compared to NFD-SM.

Thompson NFD-SM NVD-DM KTLX Reflectivity Obs 15 min Fcst at 2215 UTC 45 min Fcst at 2245 UTC Reflectivity Forecast at 1 km AGL Member 12 Member 14 Member 31 Member 12 Member 14 Member 31

Ensemble Mean Coldpool Analyses and Forecast

1-hr Forecast Probability of Vorticity ( UTC) after 45-min assimilation ≥ s -1 at 150m AGL ≥ s -1 at 1 km AGL Thompson NFD-SM NVD-DM % Probability Observed damage track and times ~22:06 ~22:38 Observed damage track and times ~22:06 ~22:38 Observed damage track and times ~22:06 ~22:38 Observed damage track and times ~22:06 ~22:38 Observed damage track and times ~22:06 ~22:38 Observed damage track and times ~22:06 ~22:38

45-min Forecast Probability of Vorticity ( UTC) after 1-hr assimilation ≥ s -1 at 150m AGL ≥ s -1 at 1 km AGL Thompson NFD-SM NVD-DM % Probability Observed damage track and times ~22:06 ~22:38 Observed damage track and times ~22:06 ~22:38 Observed damage track and times ~22:06 ~22:38 Observed damage track and times ~22:06 ~22:38 Observed damage track and times ~22:06 ~22:38 Observed damage track and times ~22:06 ~22:38

Summary and Future work  The results show promise for short-range, ensemble-based, storm- scale tornadic supercell forecasts initialized from EnKF analyses  The reflectivity structure of the supercell storm using a DM scheme compare better to the observations than that using a SM scheme  Storm-scale ensemble system can predict the track of the strongest rotation with some accuracy in 0-1 hour time frame  Future work:  Vary the microphysical parameters across the ensemble to improve spread  Use of higher resolution grid of 1 km or less

Acknowledgement Glen Romine and Nancy Collins for help with DART Kevin Manross for providing the edited radar data

Additional Slides

Reflectivity Analyses at 2200 UTC

Reflectivity Forecasts at 2230 UTC