Analysis of the Goshen County, Wyoming, tornadic supercell on 5 June 2009 during

VORTEX2 using EnKF assimilation of mobile radar observations

Jim Marquis, Yvette Richardson, Paul Markowski,

David Dowell,

Josh Wurman, Karen Kosiba, and Paul RobinsonPhoto by Sean Waugh

Goals of EnKF analysis• Increase availability of 3-D kinematic and

thermodynamic data (dual-Doppler and in situ obs are spatially/temporally limited).

Paul MarkowskiCourtesy www.vortex2.org

Goals of Data Assimilation• We want a set of realistically evolving analyses (i.e., We are not using DA to initialize a forecast).

• Analyze roles that mesocyclone-scale processes play in tornadogenesis, maintenance, and decay:

- trajectory analysis,- vorticity, momentum budgets,- complete thermodynamic fields,- mid-upper level features,

Model Specifics• WRF-ARW 3.2.1:

-Δx,y = 500 m, 80m < Δz < 2km, [120 x 80 x 20] km3 -LFO microphysics (χrain= 8x106, χgraup= 4x103 m-4 ; ρgraup= 900 kg/m3),-open lateral BCs,-no surface fluxes, no radiation, flat terrain.

Homogeneous environment:

DA Specifics• DART (Anderson et al. 2009):– Ensemble adjustment filter,

– 50 members,– Localization:

• Gaspari-Cohn (1999) = 0 @ r = 6 km– Ensemble initiation:

• 10 randomly placed warm bubbles at model t0 for each member

– Ensemble spread maintained with:• Additive noise added to T, Td, U, V every 5 min where radar

reflectivity is > 25 dBZ, (Dowell and Wicker 2009) • Perturbations smoothed to 4 km (horiz), 2 km (vert) scales

Courtesy www.vortex2.org

σ2obs = (2 m/s)2

• Radar velocities assimilated every 2 minutes

•

• OBAN: Cressman weighting

500 m horizontal grid spacing (for 500m-model grid experiment)

data along conical slices

Experiment timeline

“synthetic Data”

ζradarsDu

al-D

oppl

erEn

KF

Wm/s

Z = 400 m AGL

Dual-Doppler – EnKF (posterior) kinematics comparison

X (km)

Y (k

m)

Comparison of temperature observations and EnKF analyses near the ground:

Z = 50 m AGL

θv(K)

ζ

Mobile mesonet Observations (not assimilated)Surface gust fronts

’

• Greatest differences early in the assimilation period. They get smaller with time.

X (km)

Y (k

m)

Pressure fields

do

mdo pRppWRF diagnostic pressure:

• Want pressure for: ...''1

Bzp

zp

dtdw db

Important to supercells

Pressure diagnosed using individual members vs. retrieval using ensemble mean posteriors kinematic fields (e.g., Gal-Chen 1978,

Hane and Ray 1985):

Sfc gust front z = 250 m

Y (k

m)

X (km)

do

mdo pRpp

Retrieved from horiz. momentum eqns

2211 UTC

Trajectories (storm-rel.) calculated from ens. mean analyses

Ring (radius = 1 km) of 20 parcels centered on peak ζ at z = 700 m at 2211 UTC, integrated backward in time to 2143 UTC

Sfc gustfront

θρ(K)’

(Note: Trajectories curtailed because they encounter edges of dual-Doppler coverage)

X (km)

Y (k

m)

z = 200 m

=

=

=

Tilting Stretching Baroclinicgeneration

Boussinesq, inviscid, coriolis-free 3-D Vorticity Equation:=

𝐵=𝑔𝜃−𝜃𝑒𝑛𝑣

𝜃𝑒𝑛𝑣

Ultimately important for generation of low-level mesocyclone

=

=

=

More verification: Does ensemble mean vorticity along parcel trajectories match predicted (i.e., integrated) tendency?

Ens. mean interpolatedto trajectory ∫(𝒕𝒊𝒍𝒕+𝒔𝒕𝒓+𝒃𝒂𝒓𝒐 )𝒅𝒕 ∫(𝒕𝒊𝒍𝒕+𝒔𝒕𝒓 )𝒅𝒕

Lagrangian 3-D vorticity budget for a parcel entering mesocyclone:

z = 200 m

X (km)

Y (k

m)

ρ

Ens. mean interpolatedto trajectory ∫ (𝒕𝒊𝒍𝒕+𝒔𝒕𝒓+𝒃𝒂𝒓𝒐 )𝒅𝒕 ∫(𝒕𝒊𝒍𝒕+𝒔𝒕𝒓 )𝒅𝒕

Lagrangian 3-D vorticity budget for a parcel in the forward flank:

X (km)

Y (k

m)

Summary: Utility of EnKF analysis • EnKF Kinematic Analyses:

- Compare well with dual-Doppler fields.

- Most trajectories seem believable;though, Lagrangian vorticity budgets have some problems in some areas of the storm.

• EnKF Thermo Analyses: - Mixed success with comparisons to in situ obs.

• Pressure fields seem unusable.

Acknowledgements• The EnKF experiments were performed using NCAR CISL

supercomputing facilities (bluefire) with the Data Assimilation Research Testbed (DART) and WRF-ARW software.

• Thanks to: Glen Romine, Lou Wicker, Chris Snyder, Nancy Collins, Jeff Anderson, Don Burgess, Dan Dawson, Robin Tanamachi, Bruce Lee, Cathy Finley, Altug Aksoy, Fuqing Zhang, and Matt parker for consulting.

• Thanks to all VORTEX2 crew for their dedication while collecting data on 5 June 2009.

• This research is funded by NSF grants: NSF-AGS-0801035, NSF-AGS-0801041. The DOW radars are NSF Lower Atmospheric Observing Facilities supported by NSF-AGS-0734001.

Extras

Surface gust front

ζ

Pressure diagnosed with posterior ensemble mean thermodynamic variables vs. Pressure retrieved from posterior ensemble mean kinematic fields (e.g., Hane and Ray 1985, Majcen et al. 2008):

Diag

nose

d p’

Retr

ieve

d p’

• Posterior diagnosed pressure doesn’t seem right.

• Retrieved pressure fields seemingly more realistic, but vertical gradients still not trustworthy.

z = 250 mX (km)

Y (k

m)

do

mdo pRpp

W>0

Pressure fields diagnosed with posterior means, prior means, and individual members :

z = 250 m

Model errors/idealized conditions require DA for a good storm.

Storm structure with/without radar assimilation:

Top row: Series of Posterior ens. mean analyses.

Bottom row: Single member forecasted forward from 2157 (no DA).

ζ

Z = 150 m

X (km)

Y (k

m)

Difference between surface obs and near-surface EnKF analyses of thermodynamic fields:

θe

Time UTC

RMS Innovation

Total Spread

Consistency Ratio

Some obs-space statistics

Comparison of Dual-Doppler – EnKF (ensemble mean) horizontal vorticity

EnKF

Dual

-Dop

Dual

-Dop

pler

EnKF

Horizontal vorticity vector pattern is similar, though magnitude of EnKF vorticity is greater

�⃑�h

ζ

θ(K)’

X (km)

Y (k

m)

Surface & mid-upper-level features

(pre-tornado/tornadogenesis) (tornado mature)

(tornado weakening) (tornado dissipated)

W > 5 m/s(z = 5km)

ζ(z = 300m)Surface gust front

Low-level meso cyclone/tornado stays beneath mid-level updraft

W (m/s)

ζ

Z = 400 m AGL

Dual-Doppler – EnKF (ensemble mean) kinematics comparison(DOW6 & NOXP)

ζ

Wm/s

X (km)

Y (k

m)

θ’(K)

Low-level wmax trace: MM obs:

ζ