Data Assimilation for Convection

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 Assimilation science Convection permitting data assimilation systems Data Observations of convective events \ observation operators Assimilation benefits Validation / targeting / ensemble generation / quality assessment

2. The MetOVar system is hydrostatic ... ... but inside resolved convection the atmosphere is not AB Rainfall rate kg m-2 s-1 B A S. Vetra Totalθ correlation Hydrostatically balanced θcorrelation

3. Ensembles/Var at convective scales • Way forward:ensemble-Kalman-filter, an ensemble-transform-Kalman-filter, and variationalassimilation 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