Luca Nisi Supervisor: I. Giunta

1. Context and Scale Oriented Thunderstorm Satellite Predictors Development(COALITION)EUMETSAT FellowshipHost-Institute: MeteoSwiss Luca Nisi Supervisor: I. Giunta Convection Workshop 8-10.10.2009

2. Objectives • Flexible entity-oriented model • Couplings between convective signatures and environment • Strict conservation laws or semi-empirical rules • Data Merging Possible auxiliary sources: • NWP analysis / forecast (INCA1) • Radio-soundings • Ground stations • Climatology • (real-time user interaction) Main sources: • MSG/SEVIRI • MetOp/IASI • Weather radar • Orography (DHM?) • Lightning → “Heuristic model, which produces in real-time probabilistic information about time, space, and intensity evolution of severe convection for use by decision makers (warnings).” INCA1: Integrated Nowcasting through Comprehensive Analysis

3. Example: Object SIGNAL INTERACTION SIGNAL INTERACTION Environment e.g. water vapour (as potential V(x,y,T) e.g.: Radar cell from TRT2 (vert max reflectivity, confinement rules) Approach / Model Environment Object • known links Convective Signature ↔ Environment → physical conservation laws (e.g. water vapour) → translate empirical rules into model (scale!), (e.g. first appearance of precipitation signals) TRT2: Thunderstorm Radar Tracking, developed at MeteoSwiss

4. Intensity Pre-convective environment Convective Initiation Stage ? ? ? time Approach / Model • Evolution of object-environment from last consecutive observations → interpolate through newest observations (“minimize observation errors”) → build deterministic evolution • Backward extrapolation: history → adjust past evolutions according to • additional heuristic information (e.g. link between convection phase) • perturbations of initial state  ensemble → minimize statistical uncertainty and bias over the ensemble (trajectories) • Forward extrapolation: → build probabilistic forecast COALITION forecast Observed Observation Err.

5. ? ? Instability CI LL Convergence Radar cell Syn. forcing Squall line Tot. Prec. Wat. Radar echo Phases: Intensity Pre-convective environment Convective Initiation Stage Mature Convective Stage ? ? ? ? ? time possible application of COALITION forecast Observed

6. Work on-going • technical implementation • building data base  case selection / classification (e.g. ....) • designing / developing algorithm

7. Thank you! References • Hering, A. M., C. Morel, G. Galli, S. Sénesi, P. Ambrosetti, and M. Boscacci, 2004: Nowcasting thunderstorms in the Alpine • region using a radar based adaptive thresholding scheme. In: Proceedings of Third European Conference Radar on • Hydrology (ERAD), Visby (Sweden), Copernicus, 206-211. • König, M. 2009: Convective Initiation: Current Studies on Satellite Retrieved Indicators. Forschungskolloquium MeteoSwiss, • 19. May 2009. • Krakow Convection Workshop, 2007: Recommendations from the working group, EUMETSAT. • Mecikalski, J. R. and K. M. Bedka, 2006: Forecasting convective initiation by monitoring the evolution of moving cumulus in • daytime GOES imagery. Mon. Wea. Rev. (IHOP Special Issue, January 2006), 134, 49-68. • Mecikalsky, J. R., K.M. Bedka, Paech, S.J., Litten, L.A., 2008: A Statistical Evaluation of GOES Cloud-Top Properties for • Nowcasting Convective Initiation. American Met. Soc. (IHOP Special Issue, December 2008), 4899-4914. • Rosenfeld D., Woodley W.L, Lerner A., Kelman G., Lindsey D.T., 2008: Satellite detection of severe convective storms by their • retrieved vertical profiles of cloud particle effective radius and thermodynamic phase. Journal of Geophysical Res., 113, • D04208. • Setvák M., Rabin R.M., Doswell C.A., Levizzani V., 2003: Satellite observations of convective storm top features in the 1.6 and • 3.7/3.9 μm spectral bands. Atmos.Research, 67- 68C, 589-605.