Nowcasting Thunderstorms with SIG nificant weather O bject O riented N owcasting S ystem

1. Nowcasting Thunderstorms with SIGnificant weatherObjectOrientedNowcastingSystem Pascal Brovelli, Stéphane Sénési, Etienne Arbogast, Philippe Cau, Sandrine Cazabat, Michel Bouzom, Jérôme Reynaud

2. SIGOONS principles • Nowcasting significant weather events (0-4h) • thunderstorms • later on, fog areas, heavy rain, snow and icing condition, strong wind systems • Based on an object oriented approach • SIGOONS manages Significant Weather Objects (SWO)  Notice that usual weather conditions are not describe • A hybrid system : man-machine mix • Automated generation of end-user products downstream of supervise database WSN05 - Toulouse France

3. Expertise Production User-specific warnings 5’ SWO database Pre-processing Guesses updates CONO Updates Systematicchecking Ground observations SIGOONS: a hybrid system • Automated processes: • Diagnostic of thunderstorm SWO guesses • SWO systematic check against ground observations • Generation of user-specific warnings • Forecaster expert input: • Arbitrate discrepancies WSN05 - Toulouse France

4. Automated Diagnostic of thunderstorm SWO using CONO (1) • CONO tool analyzes radar data in order to automaticaly detect convective cells and/or systems (Convection Nowcasting Objects, extension of the RDT objects, see also Hering et al. and Autones et al.) • Detection by adaptative reflectivity thresholding of radar data • Structure of reflectivity can be complex : smoothing and morphological « closing » operation merges cells and matches the convective system scale • Discrimination of convective systems uses lightning data WSN05 - Toulouse France

5. Automated Diagnostic of thunderstorm SWO using CONO (2) • CONO tool automaticaly tracks convective cells and/or systems • Tracking by overlapping between a cell detected in the present image and cells detected in the previous image using displacement speed • Speed estimate blends move of the cell centroïd and cross-correlation. After tuning, speed diagnostics : • are robust against merges and splits • have smooth variation • improve diagnostic of low group speed on backward regenerating convective systems • CONO intialialize thunderstorm SWO attributes: • horizontal envelope, move speed and lightning activity • rain rate and hail risk WSN05 - Toulouse France

6. SIGOONS Man-Machine Interaction • Challenge: Minimize input by the forecaster • SIGOONS is designed to run automatically • Merge and check automated SWO from the new « slot » with forecast SWO from the previous « slot » : • Tracking allows to propagated forward in all supervise attributes, provided that there is consistency with new observation data • Discrepancy  send a specific, relevant « disagree signal » to the forecaster • Forecaster inputis optional • monitors the automated initialization of sensible weather attributes: wind gust, hail risk, rain accumulation • Choose between different automated diagnostic values • Arbitrate discrepancies • Sets a decay/growth tendency on area, duration, attributes … • Creates objects for convective systems or thunderstorm prone areas WSN05 - Toulouse France

7. Background Grey : Satellite - IR Color : radar mosaïc Lightning SIGOONS display tool Object with mismatch • Fully integrated in the operational Synergie Workstation Speed estimate too high Object with good match Significant ground obs without supporting object WSN05 - Toulouse France

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9. Access to rain rate evolution WSN05 - Toulouse France

10. Significant rain accumulation Access to ground observations collected over the SWO trajectory WSN05 - Toulouse France

11. SIGOONS: human expertise • A significant effort on ergonomic: • Real-time experiments and case studies using man-machine interface prototype (since summer 2004) • Ergonomist studies (see Chabaud et al.) • First study results: • Forecasters feel that the object representation is clear • Forecasters concerns are the workload and their ability to exert expertise: • During the first hour SIGOONS should definitively favour automation • For first to fourth hours ahead, expertise apply to larger scales : thunderstorm systems or thunderstorm-prone areas • Expert input is basically qualitative : “wind gust stronger near the coast” WSN05 - Toulouse France

12. SIGOONS End-user products • 2004 experiment findings : • Warning locations are correct and faithfully translate the nowcast database contents • The sensible weather diagnostic (wind gust, rain accumulation, hail risk…) is still weak • Time consistency and stability must be improved • products delivered in “push” mode, like user-specific warnings (e.g. security services) are much promising WSN05 - Toulouse France

13. SIGOONS : Current status • Automated diagnostic and check run in routine mode • Human expertise: • SWO display tool close to be available for operations to all (7) regional offices forecaster • Experiment on case studies in order to define nowcasting specific tasks • Products • Test with a few customers of a “warning package”, which includes nowcast warnings, and short range forecast • Assessment of the quality of the thunderstorm warnings produced without expert input WSN05 - Toulouse France

14. SIGOONS Outlook • Automated diagnostics: • Use optimal combination of satellite and radar tracking • Improve the conceptual models for automated convection diagnostics: • Data fusion with mesoscale analysis • Identification of convection organization type • Diagnostics for sensible weather attributes • Human expertise • Introduce new objects, better suited to human expertise • thunderstorm-prone area, fog area, surface front ( rain and/or wind )… • Products • Introduce uncertainty on phenomena location and intensity • Design graphical and mobile phone products • Extend the OO approach to : • Tracking objects in HiRes NWP simulation (a.s.a.p re. NWP quality) • Matching of simulated objects with real objects for real-time trend assessment WSN05 - Toulouse France

15. Thank you WSN05 - Toulouse France