1. Data Assimilation for Convection Univ: R. Bannister, S. Migliorini, P.-J. Vanleeuwen, S. Dance, S. Rennie, N. Pounder, D. Clifford, … MetO: M. Dixon, S. Ballard, D. Simonin, … Students: S. Vetra, R. Petrie, A. Fowler, … Good models will not achieve their predictive potential without appropriate data assimilation Data Observations of convective events \ observation operators Assimilation science Convection permitting data assimilation systems Assimilation benefits Validation / targeting / ensemble generation / quality assessment

2. The MetO Var system is hydrostatic...... but inside resolved convection the atmosphere is not Rainfall rate kg m -2 s -1 A B Hydrostatically balanced θ correlation Total θ correlation S. Vetra

3. Ensembles/Var at convective scales Way forward: ensemble-Kalman-filter, an ensemble-transform- Kalman-filter, and variational assimilation at ‘hi-res’ based on the current Met Office system 1.5 km resolution over 70 vertical levels over southern UK 23 forecasts + control Development of reduced-rank-Kalman-filter in a convecting toy model The above methods recognize the flow- dependent (ie convection dependent) nature of the assimilation problem

4. Challenges ahead What are the consequences of using a non-convection-permitting data assimilation system at hi-res? How ‘non-hydrostatic’ and ‘non-geostrophic’ are forecast errors at different positions and scales? What is the detailed design and performance of the new ‘convection permitting’ data assimilation methodologies? Ensemble Kalman filter Ensemble transform Kalman filter Variational data assimilation Reduced-rank Kalman filter What is the effect of hi-res observations (radar)? Important collaboration with Met Office Spin-up work on MetO/NERC MONSooN supercomputer