Evaluation of three-dimensional cloud structures in DYMECS Robin Hogan John Nicol Robert Plant Peter Clark Kirsty Hanley Carol Halliwell Humphrey Lean.

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Presentation transcript:

Evaluation of three-dimensional cloud structures in DYMECS Robin Hogan John Nicol Robert Plant Peter Clark Kirsty Hanley Carol Halliwell Humphrey Lean Thorwald Stein (UK Met Office)

50-km domain 200-m model 1.5-km model 1:1 aspect ratio

What we want to know about cloud structures How does cloud top height relate to a. Time (life time or time of day) b. Surface area 2 3 What is the probability of an anvil and what are typical anvil factors? 1 How does the typical storm width vary with height? z R 4 How do ice cloud reflectivities relate to the precipitation rate?

Storm structure from radar Distance east (km) Distance north (km) Radar reflectivity (dBZ) 40 dBZ 0 dBZ 20 dBZ

1. Median equivalent radius with height – all 2012 Observations UKV 1500m Model storms too wide (or not enough small storms) Observed cores are deeper (40dBZ in ice part)

“Shallow” “Deep” ObservationsUKV 1500m200m 1. Median equivalent radius with height – 25 th August 2012 Lack of anvils? (see 3) 500m Drizzle from nowhere? (see 4) Convergence? ✔ ✖

2a. Cloud top height evolution with time of day Models fail to reproduce sharp increase in median cloud top height at noon. Tallest storms (90 th pct) are not deep enough compared to observations.

2b. Cloud top height variation with storm size ObservationsUKV 1500m 200m 500m Models and observations show larger storms have higher cloud tops. Models have too many medium-sized storms with low cloud tops. Median height 25 th /75 th percentile

Observations UKV 1500m 200m 3. Anvil probability 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=0 o C R

3. Anvil probability PDF of anvil factor Dmax/D3km 6 3 z T=0 o C D Define anvil as cloud above 6km with diameter larger than storm diameter at 3km (500m above the melting layer). Dmax Suggests exponential distribution of anvil factors for the UK in model and observations

4. Ice cloud and precipitation 1.5-km1.5-km + graupel 200-m1.5-km new PSD Observations Conditioned on average reflectivity at m below 0 o C. Reflectivity distributions for profiles with this mean Z dBZ are shown. Model: High rainfall rate from storms lacking ice or have ice cloud dBZ<0

Discussion points Are microphysics parameterisation schemes fit for high-resolution (200m or less)? What are the appropriate regions for evaluation of convective cloud features (e.g. anvil – tropics) and do we have the observations? What are the observational needs for high-resolution model evaluation? Should we monitor convection for relatively rare events e.g. hail, and how (new dual-pol radars in network)? Should a radar forward operator Z(IWC,T) adopt model assumptions on particle size distributions, or adopt a physical or empirical based approach? “is 0 dBZ really 0 dBZ”