The EnKF Analyses and Forecasts of the 8 May 2003 Oklahoma City Tornadic Supercell Storm By Nusrat...

The EnKF Analyses and Forecasts of the The EnKF Analyses and Forecasts of the 8 May 2003 Oklahoma City Tornadic 8 May 2003 Oklahoma City Tornadic

Supercell StormSupercell Storm

By

Nusrat Yussouf1,2

Edward Mansell2, Louis Wicker2 , Dustan Wheatley1,2, David Dowell3,

Michael Coniglio2 and David Stensrud2

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.

MotivationMotivation 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 The 8 May 2003 Oklahoma City Tornadic SupercellSupercell

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 EnsembleMesoscale 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 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 8 - 0 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 AssimilationStorm-Scale Data Assimilation

Storm-Scale Data Assimilation Storm-Scale Data Assimilation Experimental DesignExperimental 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 spreadObservation-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 ratioObservation-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 AGLAnalyses at 2200 UTC at 1 km AGL

Thompson NFD-SM NVD-DM

mesocyclone

Vorticity contours from 0.001 to 0.01 at 0.001 s-1

mesocyclonemesocyclone

KTLX Reflectivity Obs.

Member 12 Member 14Member 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 m

in F

cst

at 2

215

UT

C45

min

Fcs

t at

224

5 U

TC

Reflectivity Forecast at 1 km AGLReflectivity Forecast at 1 km AGL

Member 12 Member 14Member 31

Member 12 Member 14Member 31

Ensemble Mean Coldpool Analyses and ForecastEnsemble Mean Coldpool Analyses and Forecast

1-hr Forecast Probability of Vorticity 1-hr Forecast Probability of Vorticity (2145-2245 UTC) after 45-min assimilation(2145-2245 UTC) after 45-min assimilation

≥ 0

.00

15

s-1 a

t 1

50

m A

GL

≥ 0

.00

3 s

-1 a

t 1

km

AG

L

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 45-min Forecast Probability of Vorticity (2200-2245 UTC) after 1-hr assimilation(2200-2245 UTC) after 1-hr assimilation

≥ 0

.00

15

s-1 a

t 1

50

m A

GL

≥ 0

.00

3 s

-1 a

t 1

km

AG

L

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 workSummary 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

AcknowledgementAcknowledgement

Glen Romine and Nancy Collins for help with DARTKevin Manross for providing the edited radar data

Additional SlidesAdditional Slides

Reflectivity Analyses Reflectivity Analyses at 2200 UTCat 2200 UTC

Reflectivity ForecastsReflectivity Forecastsat 2230 UTCat 2230 UTC