Reading, 13 June 2013

Workshop onWorkshop on

Convection in the high Convection in the high resolution Met Office modelsresolution Met Office models

TimetableTimetable

10.00 Arrival and coffee10.15 Robin Hogan: Introduction and DYMECS update10.30 Thorwald Stein: Evaluation of 3D cloud structure from DYMECS10.45 Lee Hawkness-Smith: Nowcasting and data assimilation11.00 Andrew Barrett: Convection over orography11.15 Marion Mittermaier: Verification of precipitation11.30 Paul Field/Jonathan Wilkinson: Cloud physics11.45 Chris Holloway: Organisation of convection12.00 Alison Stirling: Parameterization of convection12.15 Pete Clark: Idealised simulations12.30 Lunch13.30 Discussion groups14.45 Tea break15.00 Summary of discussion15.55 Close

Please keep to time! 10-12

minute talks and 3-5 minutes for questions/discuss

ion

Dynamical and microphysical Dynamical and microphysical evolution of convective storms evolution of convective storms

(DYMECS)(DYMECS)

Operational radar network

Track storms in real time and automatically scan Chilbolton

radar

Derive structure of hundreds of storms on 40 days

Evaluate the structure of clouds in the model

Robin Hogan, John Nicol, Kirsty Hanley, Thorwald Stein, Bob Plant, Humphrey Lean, Carol

Halliwell

• How well do the high-resolution models simulate surface rainfall?

• Operational Met Office radar estimates surface rainfall every 5 minutesRadarRadar

1.5-km model1.5-km model 200-m model200-m model

Performance on 25 August 2012Performance on 25 August 2012• 200-m model predicts

best average rainfall• But all models

underestimate rainfall in the afternoon

• 200-m model predicts number of small storms best

• 1.5-km model underestimates small storms but is much better at large storms

RadarRadar

200-m 200-m modelmodel

1.5-km model1.5-km model

Kirsty Hanley

Storm distributions with mixing Storm distributions with mixing lengthlength

• Mixing length plays key role in determining number of small storms

• Enables 200-m model (with too many small storms) to behave more like observations, or a lower resolution model

• But optimum mixing length varies from case to case• What controls the number of large storms?

1.5-km model 500-m model

Kirsty Hanley

Estimating updraft Estimating updraft magnitudemagnitude and and scalescale from radar RHI scans from radar RHI scans

• Use radar radial wind and continuity equation, setting tangential convergence in each column to a constant such that we have zero vertical wind at ground and cloud-top

• Tests on slices through model implies errors of ±2 m s-1

Distribution of vertical Distribution of vertical velocity from 500 m modelvelocity from 500 m model

• Retrieval good, although peak updrafts underestimated• In evaluating models statistically, we can either

– Use a mapping function derived from model to correct tail– Compare retrievals to the same method applied in the model

True Retrieved

John Nicol

Evaluation of Evaluation of magnitudemagnitude of of updrafts updrafts

• Agreement in terms of distribution is amazingly good!!!

Radar500-m model

Retrieval applied to model and observations

True model versus

“mapped” observations

John Nicol

Height distribution in several Height distribution in several modelsmodels

• Mean updraft speed (w > 1 m/s) versus altitude

Mapped retrieval200-m model500-m model1.5-km model(dashed: with graupel)

John Nicol

Evaluation of Evaluation of widthwidth of of updraftsupdrafts

• Model updrafts shrink with resolution– 200-m model has about the right width– Does 100-m model shrink further or stay the same?– How does Smagorinsky mixing length affect model?

Observations200-m model500-m model1.5-km model

Retrieval in both observations and model:wmin=0.5 m/s; wmax>3.0m/s

True model versus mapped observations: wmin=1.0 m/s; wmax>5.0m/s

Discussion pointsDiscussion points

• DYMECS takes a statistical approach, CSIP and COPE a case study approach; how can we best exploit the advantages of each?

• What controls updraft scale and magnitude, in models and reality?

• How can we evaluate convective organisation of storms in models?• Organisation unaffected by model settings tried: how to improve

it?• Can models distinguish single cells, multi-cell storms, squall

lines & quasi-stationary storms? Can we evaluate this from observations?

• What is next frontier in evaluating storm-resolving models? Hail occurrence? Turbulence intensity? Lightning location?

• What is the next frontier in improving storm-resolving models? Stochastic backscatter? Aerosol-cloud interactions? TKE schemes?

• Can we use DYMECS-type observations to diagnose parameters that should be used in convection parametrizations?

• What collaborative proposals should be written? What further observations are needed (post-COPE)?

At each height bin (1-km depth), derive mapping function (black) from 1D estimate (red) to truth (blue)

dBZ

w