1. DIAMET Second project meeting, 9/6/11 NERC Storm Risk Mitigation Programme DIAMET - Lead PI, Geraint Vaughan, Manchester TEMPEST - Lead PI, Len Shaffrey, Reading DEMON - Lead PI, Paul Bates, Bristol SRM1 – London 2+3/11/10 DIAMET1 – Manchester 23/11/10 SRM2 – Wallingford3/2/11 TEMPEST1 – Reading8/2/11 DIAMET2 – ReadingNow! DEMON1 – Bristol7/7/11 SRM annual 1 - ? 2+3/11/11

2. Integration activities linking DIAMET, DEMON and TEMPEST Project 1 PI: Len Shaffrey (TEMPEST) Co-Is: John Methven (DIAMET), Kevin Hodges (DEMON, TEMPEST) Researcher – Kevin Pearson Project 2 PI: David Mason co-Is: Paul Bates, Hannah Cloke, Sarah Dance and Kevin Hodges FLoAT – Flood action team following model used for FREE. Grabbing of flood extent data from satellites and aircraft during SRM programme.

3. Objectives of Integration Project 1 1. Provide the impacts project (DEMON) with simulations of selected cyclonic storms associated with heavy rainfall, using a range of models using climate to convection-resolving NWP resolutions. [HIGEM simulations of DIAMET/DEMON cases] 2. Evaluate how physical processes influence cyclonic storm development using Potential Vorticity (PV) tracer diagnostics in the climate to NWP resolution cyclonic storm case studies. [PV tracer diagnostics in HIGEM] 3. Develop a series of impact-relevant metrics to evaluate the extent to which climate models can represent cyclonic storms with the potential to produce severe flooding. [identification of storms from climate simulations similar to those  flooding] 4. Understand how systematic errors in storm behaviour arise in climate models by applying a storm compositing methodology to the PV tracer diagnostics from a multi-decadal climate simulation of HiGEM. [run PV tracers, re-initialising before ~100 pre-selected storms to apply DIAMET methodology to a climate simulation]

4. International activities WMO THORPEX – focus on 2-15 day high impact forecasts –Three working groups: PDP (predictability and dynamical processes) DAOS (data assimilation and observing systems) TIGGE/GIFS (THORPEX interactive grand global ensemble) –European science meeting 24-27/5/11 –PDP WG 21/6/11 –DAOS WG 27/6/11 T-NAWDEX experiment still planned but further away (2013/2014) However, DLR will be moving their LIDAR on Falcon aircraft for Aug/Sept 2012 for collab with us and HYMEX (in Med). –Andreas Dornbrack, Christoph Kiemle, Heini Wernli

5. Links to the other consortia Upscale and to climate A.5, C.1: importance of representing mesoscale processes for synoptic scale forecasts A.1, A.3S: identifying the most important diabatic processes for mesoscale PV anomalies and their evolution Downscale and to impacts C.1, A.4S: relating skill in high res precip forecasts to mesoscale structure + identification of shadow MOGREPS forecast (closest to obs trajectory) C.2: use of DA to infer more suitable parameter values + estimating model error on spread in precipitation forecasts C.3, C.4S: towards better DA scheme for high resolution simulations used by hydrological models Both A.2: new observations of 3D mesoscale structure coinciding with high resolution ground-based network (precip and surface winds). B.1, B.2, B.3: development and implementation of parameterisations within the UM framework

6. Questions from AO that “Deliverable 1” should address (a) How are potential vorticity and moisture anomalies generated in cyclonic storms, what is their morphology, and what consequences do they have for the weather? (b) How can these physical processes be parameterized for NWP, both at very high horizontal resolution (~1 km) and at resolutions that are more appropriate for longer-range prediction (10-20 km)? (c) What is the real-world structure of cyclonic storms at the meso- convective scale? (d) How can we improve numerical weather prediction models and data- assimilation methods for better “nowcasts” and forecasts of cyclonic storms at meso to convective scale?

7. DIAMET DIAbatic influence on Mesoscale structures in ExTratropical storms WP A. Detailed modelling and measurements WP B. Parameterisations WP C. Predictability Questions a and c Question b Questions a and d

8. Structure of proposal: scientific elements High Resolution case study modelling Field campaign Analysis of past data Predictability: Analysis of ensemble forecast database Dynamical consequences of PV anomalies: case studies, idealised simulations, ensembles Understanding and parameterising convection, air- sea interaction, microphysics Data assimilation: improved balance conditions and error covariance All leading to deeper understanding and improved predictions

9. Work Package A Structure of mesoscale anomalies and their consequences A.1 (Jeffrey Chagnon) – origin of mesoscale PV anomalies within cyclonic storms, partitioned by process (as represented by MetUM). A.2 (Doug Parker, Leeds) – measurement and analysis of mesoscale structures in storms. Two field campaigns with aircraft and high res ground-based network (autumn 2011, summer 2012). A.3S (Peter Knippertz, Leeds) – modelling microphysical sensitivities of storm dynamics. A.4S (David Schultz, Manchester) - statistics of mesoscale precipitation structures, especially rainbands. A.5 (Sue Gray) – dynamical consequences of mesoscale PV anomalies and heating on cyclonic storm development.

10. Work Package B Physical processes and their parameterisation B.1 (Bob Plant) – Improving convective parameterisation with emphasis on initiation at mid-levels, the influence of shear flow and interaction between convection and cloud schemes (within UM). B.2 (Ian Renfrew, UEA) – air-sea fluxes and their influence on storm development B.3 (Tom Choularton, Manchester) – microphysical processes – aiming to calculate microphysical process rates from measurements of liquid and ice size distribution and then latent heating rate

11. Work Package C Predictability C.1 (John Methven/Tom Frame) – ensemble prediction of mesoscale features tracked within Met Office MOGREPS ensemble “mesoscale features” = fronts/cyclones identified by Hewson method Relate predictability of detailed weather events (precip + high surface winds in higher res forecasts) to mesoscale features C.2 (Stefano Migliorini) – investigate and simulate sources of model error that most affect forecast skill in “convection resolving” forecasts Using 100 member high res ensembles for T+3hr C.3 (Ross Bannister) – nature of multivariate relations in high-resolution models C.4S (Peter Jan van Leeuwen) – next generation DA methods for the convective scale (En4DVAR in simplified setting).

12. Links to the other consortia Upscale and to climate A.5, C.1: importance of representing mesoscale processes for synoptic scale forecasts A.1, A.3S: identifying the most important diabatic processes for mesoscale PV anomalies and their evolution Downscale and to impacts C.1, A.4S: relating skill in high res precip forecasts to mesoscale structure + identification of shadow MOGREPS forecast (closest to obs trajectory) C.2: use of DA to infer more suitable parameter values + estimating model error on spread in precipitation forecasts C.3, C.4S: towards better DA scheme for high resolution simulations used by hydrological models Both A.2: new observations of 3D mesoscale structure coinciding with high resolution ground-based network (precip and surface winds). B.1, B.2, B.3: development and implementation of parameterisations within the UM framework