1. DIAMET DIAbatic influences on MEsoscale structures in extratropical sTorms Geraint Vaughan, Manchester PI John Methven, Reading PI Doug Parker, Leeds PI Ian Renfrew, East Anglia PI

2. NERC Storm Risk Mitigation Programme http://www.bgs.ac.uk/stormrm/home.html NERC strategic research activity to improve forecasting of storms and their impacts on catchments and coasts £4.9m from 2010-2014 DIAMET - Lead PI, Geraint Vaughan, Manchester  observation, process-level understanding and high- resolution prediction of mesoscale structures in cyclones TEMPEST - Lead PI, Len Shaffrey, Reading  representation of cyclones and stormtracks in climate models and their interaction with climate change DEMON - Lead PI, Paul Bates, Bristol improving ability to forecast floods using a cascade of models from NWP rainfall  hydrology  inundation (street scale)

3. Overarching scientific questions for DIAMET What role do diabatic processes play in generating mesoscale potential vorticity (PV) and moisture anomalies in cyclonic storms? What are the consequences of those anomalies for the weather (rainfall and surface winds)? We focus on two key diabatic processes: latent heating/cooling and air-sea fluxes of heat and moisture.

4. Structure of project WP A. Detailed modelling and measurements WP B. Process Parameterisation WP C. Predictability

5. DIAMET WP A: Mesoscale structure and diabatic effects Three flight campaigns with FAAM aircraft spanning: 14-30 th September 2011 (Cranfield, North of London) 23 rd Nov-15 th Dec 2011 (Exeter) July-August 2012 (Cranfield) New streams of Doppler radar data from Met Office Radar group and data assimilation for convection-resolving model (JCMM) Nested Met Office operational forecasts at varying resolution (currently 40km global, 12km LAM, 1.5km UK) plus hindcasts at varying resolution using tracers to partition effects of model processes on heating and PV.

6. Trough structure 6

7. Accumulated heating tendency and relative vorticity 7 Heating Tendency Vorticity 1 2

8. PV tracer diagnostics Latent heating and cooling Radiative heating and cooling Mixing Convective parameterization scheme Shortwave radiationHorizontal diffusion Microphysics (grid-scale) Longwave radiationVertical diffusion Latent heating part of the boundary layer scheme Gravity wave drag parameterization scheme Rebalancing the cloud fields after convection Mixing part of the boundary layer scheme 8

9. PV tracer diagnostics Latent heating and cooling Radiative heating and cooling Mixing Convective parameterization scheme Shortwave radiationHorizontal diffusion Large-scale cloud (grid-scale) Longwave radiationVertical diffusion Latent heating part of the boundary layer scheme Gravity wave drag parameterization scheme Rebalancing the cloud fields after convection Mixing part of the boundary layer scheme 9

10. PV tracer diagnostics Accumulating physics tendencies following the flow 10 GLOBAL: sources/sinksLAM: sources/sinks

11. PV tracer diagnostics: LAM 11

12. Accumulated heating tendency and relative vorticity 12 Heating Tendency Vorticity 1 2

13. PV SOURCE/SINK ANALYSIS: Pilot project, 24 th Nov 2009 PV sources/sink accumulated from 00 UTC on 22 November 2009, UM 12 km run The strip of high PV along the cold front is diabatically generated. Main contributions are from boundary- layer heating, cloud microphysics, and convection schemes. The high PV strip is involved in the generation of a large- amplitude gravity wave packet. Strip of high PV above surface front Upper-level trough Source: Jeffrey Chagnon

14. T-NAWDEX pre-frontal wave Knippertz et al 2010 Flight of 24 th November 2009

15. DIAMET WP B: Parameterisation of key processes 1. Detailed examination of convection parameterisation in cyclone environment: Can existing parameterisation be adapted to treat elevated convection? Choice of closure timescale for embedded convection Decomposition of bulk mass flux detrainment 2. Quantify contribution of surface and boundary layer fluxes to mesoscale PV anomalies and storm evolution 3. Measure microphysical properties and use them to derive latent heating estimates and improve parameterisations.

16. DIAMET WP C: Predictability and DA for high resolution forecasts 1. Quantify forecast statistics for objectively identified (Hewson/Titley) mesoscale features in the MOGREPS ensemble (Swinbank). Measure distances between forecasts in terms of feature tracks. Characterise dependence of precip forecast skill stratified by mesoscale feature type. 2. Use short-range (6 hr) convection-resolving ensemble (100 members), perturbing parameterisations (with and without stochastic terms) to disentangle model error from IC error. 3. Use ensemble forecasts to assess the nature of balance between variables at high resolution and influence on forecast error stats.

17. C1: UKMO Cyclone database contains feature tracking data for ensemble forecasts dating back to October 2006. 1.How do statistics of mesoscale features embedded within cyclones vary with lead time? 2.How well does the ‘best’ ensemble member shadow the observed (analysis) evolution? 3.How does the forecast skill for weather variables depend on the skill of forecasts of mesoscale features, stratified by feature type? Hewson and Titley, Met.Apps., 2010

18. Current approach assumes that for analysis increments (e.g.): all rotational wind is in geostrophic balance, all pressure is in hydrostatic balance with temperature. These effects show-up especially at small scales (large Ro). 24 members VAR 1.What scales do geostrophic and hydrostatic balances break down? 2.Do the moisture assumptions hold? 3.Can these assumptions affect accurate forecast of storms? 4.How valid is anelastic balance? 5.What is the role of each source of error (init. cond. err, model err. – strong connection with C.2)? 6.What is an alternative model of forecast error stats valid for hi-res models? 7.How robust are these statistics? Ross Bannister and Stefano Migliorini, NCEO and University of Reading C.3: The changing nature of multivariate relations in high-resolution models

19. Partnership with Met Office Participation in field campaigns (staff time, dropsondes, extra instruments) Data assimilation at high resolution and new data streams Surface fluxes, microphysical parameterisation and access to high resolution forecasts Radar group– high resolution radar data Microphysical parameterisation Ensemble Forecasting Group - feature tracking and forecast skill Data Assimilation Group - co-supervision of studentship and DA experiments on Met Office computer.

20. © Crown copyright Met Office Model Domains ModelResolutionVAR Time Window CyclingForecast Length UK4 / UKV 4 km / 1.5km3D-Var33T+36 HRTM 1.5 km3D-Var11T+10

21. © Crown copyright Met Office 1km resolution 2km 5km Purple circles are Doppler radars Need East Anglian Radar And robust quality controlled Data stream in MetDB

22. DIAMET flying programme Autumn 2011 –14-30 September, flying from Cranfield –detachment to Exeter 24 Nov – 14 Dec –8 IOPs planned, 4 double and 4 single flights July-August 2012 –Cranfield-based –Focus on high-impact rainfall –2 double, 3 single flights

23. Pathways to Impact Weather services Met Office Specific project deliverables Exchange of expertise Forecast model advances EUMETCALForecaster training Impact continues downstream through NCAS and NCEO core programmes Schools and public outreach Heather Reid and R Met. Soc Educational resources (web based) Field campaign blog Video conference Leaflets

25. International Weather Research World Meteorological Organisation World Weather Research Programme THORPEX (Improving high impact forecasts, 2-15 days lead) T-NAWDEX North Atlantic Waveguide DIAMET Mesoscale events, < 2 days Diabatic processes research focus