The three-dimensional structure of convective storms

Robin HoganJohn NicolRobert PlantPeter Clark

Kirsty HanleyCarol HalliwellHumphrey Lean

Thorwald Stein (t.h.m.stein@reading.ac.uk)

(UK Met Office)

The DYMECS approach: beyond case studies

NIMROD radar network rainfall

Track storms in real time

and automatically

scan Chilbolton radar

Derive properties of hundreds of storms on ~40 days:Vertical velocity3D structureRain & hailIce water contentTKE & dissipation rate

Evaluate these properties in model varying:ResolutionMicrophysics schemeSub-grid turbulence parametrization

Storm structure from radarStorm structure from radar

Distance east (km)

Dis

tance

nort

h

(km

)

Radar

reflect

ivit

y

(dB

Z)

40 dBZ

0 dBZ

20 dBZ

“Shallow”

“Deep”

Observations UKV 1500m 200m

Median storm diameter Median storm diameter with heightwith height

Lack of anvils?

Drizzle from nowhere?

Vertical profiles ofVertical profiles ofreflectivity reflectivity

1.5-km 1.5-km + graupel

200-m 500-m Observations

Conditioned on average reflectivity at 200-1000m below 0oC.

Reflectivity distributions forprofiles with thismean Z 40-45 dBZ are shown.

Model:High rainfall rate from

shallow storms.Or ice cloud dBZ<0

Observations UKV 1500m 200m

Missing anvils?Missing anvils?• Define anvil as cloud above 6km with

diameter larger than storm diameter at 3km.

• More than 40% of storms above 6km have anvil (model and observations).

A selection of individual profiles shows anvil factors will be small (close to 1)

6

3

z

T=0oC

R

Missing anvils?Missing anvils?

PDF of anvil factorDmax/D3km

6

3

z

T=0oC

R

• Define anvil as cloud above 6km with diameter larger than storm diameter at 3km.

Dmax

Updraft Updraft retrievalretrieval

• Hogan et al. (2008)– Track features in radial

velocity from scan to scan

Chapman & Browning (1998)– In quasi-2D features (e.g.

squall lines) can assume continuity to estimate vertical velocity