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DYMECS: Dynamical and Microphysical Evolution of Convective Storms (NERC Standard Grant) University of Reading: Robin Hogan, Bob Plant, Thorwald Stein, Kirsty Hanley, John Nicol Met Office: Humphrey Lean, Carol Halliwell

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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 velocity 3D structure Rain & hail Ice water content TKE & dissipation rate Evaluate these properties in model varying: Resolution Microphysics scheme Sub-grid turbulence parametrization

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Nimrod radar1.5-km model 500-m model200-m model Kirsty Hanley

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Nimrod radar1.5-km model 500-m model200-m model Kirsty Hanley Too many Too few

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Storm size distribution Smagorinsky mixing length plays a key role in determining number of small storms 1.5-km model 500-m model Kirsty Hanley

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20 April 201225 Aug 2012 200-m model best 500-m model best 200-m model best 1.5-km model best Kirsty Hanley

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Vertical profile First 60% of storms by cloud- top height Next 30% Top 10% Thorwald Stein Ice density too low? Higher reflectivity core Observations 1.5-km model 1.5-km + graupel

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Vertical profile First 60% of storms by cloud- top height Next 30% Top 10% Observations 200-m model 500-m model Thorwald Stein

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Estimation of vertical velocities from continuity Vertical cross-sections (RHIs) are typically made at low elevations (e.g. < 10 °) Radial velocities provide accurate estimate of the horizontal winds Assume vertical winds are zero at the surface Working upwards, changes in horizontal winds at a given level increment the vertical wind up to that point Must account for density change with height John Nicol Key uncertainty in models is convective updraft intensity and spatial scale Can we estimate updrafts from Doppler wind sufficiently well to characterize the distribution of intensity and spatial scale?

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Vertical wind (m/s) Retrieved vertical wind (m/s) Retrieval error (m/s) Reflectivity (dBZ) Horizontal wind (m/s) Estimating retrieval errors from the Unified Model John Nicol

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dBZ u (m/s) w (m/s) 12:45 07 August 201116:37 07 August 2011 John Nicol

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Scientific and modelling questions What is magnitude and scale of convective updrafts? How do two observational methods compare to model at various resolutions? What model configurations lead to the best 3D storm structure and evolution, and why? How good are predictions of hail occurrence and turbulence? How is boundary-layer grey zone best treated at high resolution, and what is the role of the Smagorinsky length scale? Does BL scheme diffuse away gust fronts necessary to capture triggering of daughter cells and if so how can this be corrected? Can models distinguish single cells, multi-cell storms & squall lines, and the location of daughter cells formed by gust fronts? What are the characteristics common to quasi-stationary storms in the UK from the large DYMECS database? Can we diagnose parameters that should be used in convection schemes from observations?

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The blob analysis Met Office 1.5 km model NIMROD radar network rainfall Rain rate (mm h -1 ) Radar observations Forecast plan-view of rainfall Does the surface rain rate look right in a couple of cases? If not, how do we fix the model? 16.00 on 26 August 2011

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WP 3. Derive properties from radar scans Cloud area, cloud-top height versus time into cell lifecycle Surface rain rate, drop size, hail intensity from polarization variables (Hogan 2007) Ice water content using radar reflectivity and temperature (Hogan et al. 2006) TKE and dissipation rate from Doppler spectral width (Chapman and Browning 2001) Updrafts…

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Updrafts? 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

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WP4. Statistical analysis of observed storms Alan Grant (2007) suggested the following testable relationships in convection parameterization: where up is the mean in-cloud dissipation rate w up is the cumulus vertical velocity scale L up is the horizontal length scale of the updrafts A up is the fractional area of some horizontal domain occupied by cumulus updrafts (equal to the cloud-base mass flux in a convection scheme divided by w up ) D cld is the depth of the convective cloud layer CAPE is the convective available potential energy

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WP6. Modelling case studies & sensitivity tests We use MONSooN so can share jobs between University and Met Office Horizontal resolution –Down to 100 m; model currently predicts smaller cells as resolution increases Sub-grid mixing scheme –Test 2D & 3D Smagorinsky, prognostic TKE and a stochastic backscatter scheme –Evaluate rate of change of cloud size with time, and TKE Microphysical scheme –Test single- and double-moment liquid, rain, ice, snow, graupel and possibly hail, as well as interactive aerosol-cloud microphysics

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