Nowcasting thunderstorms in complex cases using radar data Alessandro Hering* Stéphane Sénési # Paolo Ambrosetti* Isabelle Bernard-Bouissières # *MeteoSwiss.

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Presentation transcript:

Nowcasting thunderstorms in complex cases using radar data Alessandro Hering* Stéphane Sénési # Paolo Ambrosetti* Isabelle Bernard-Bouissières # *MeteoSwiss ML/RASA, Locarno-Monti # Météo-France DPREVI/PI, Toulouse

TRT application output (Thunderstorms Radar Tracking) 13dBZ25dBZ40dBZ 55dBZ 330 km 100 km 17:00 21:00

TRT input/output Swiss radar network: 3 volumetric C-band Doppler radars 20 elevation scan (-0.3° / 40°) every 5 min Input: 1. Radar Cartesian composite (3 radars); 5 min vertical maximum projection (from 12 CAPPI between 1 and 12 km) resolution: 2 km on 16 reflectivity classes between 55 dBZ 2. Lightning data (Météorage): CG Output: TRT-objects (attributes: geographical location, area, motion vector, velocity, trajectories, lightning...) Visualisation: : Browser : NinJo workstation (P.Joe, 7.13) Monte Lema, 1625 m 46.04N, 8.83E

TRT cells detection principle TRT developed by MeteoSwiss and Météo-France (RDT) TRT algorithm: modification of RDT satellite-algorithms for Alpine radar images ADAPTIVE REFLECTIVITY THRESHOLDING: cells at individual thresholds dB min = Range [km] Reflectivity [dBZ] ΔdB T ΔdB T = 6dB ΔdB T cell 1 cell 2 cell 3 dB th

TRT tracking principle Method: GEOGRAPHICAL OVERLAPPING of cells - Advection (estimated displacement velocity or cross-correlation) Complex cases / splits / merges considered Good tracking also for small objects t 0 +Δt t0t0 t 0 +adv v(t 0 ) + +

A L P S ITALY FRANCE GERMANY 550 km 600 km MAX > 55 dBZ >55 [dBZ] 36 [dBZ]

TRT drill down product

TRT cell velocity estimates Previous algorithm Improved actual algorithm Unstability of centroid velocity caused by splits, merges, significant area changes New: cross-correlation at object scale (∆area > 30%) and centroid displacement Temporal smoothing filter Better performance in complex cases splits/merges

TRT cell area and CG lightning evolution Cell area evolution at different reflectivities (>45 dBZ) in 3 dB steps; real-time Complex case: splits/merges, significant area changes Total, negative/positive CG lightning

Broadcasting “Thunderstorm Flash-news” During this summer MeteoSwiss started the diffusion of heavy thunderstorms warnings based on TRT and other sources In whole Switzerland for the general public and the authorities Use simple flash-news diffused by local and national radio stations Lead time: min. June-September 2005: 70 Flash-news on 18 warning days Forecasters: substantial TRT contibution to flash-news

Actual Radar image available TRT algorithm (+ other data sources) Analysis by forecaster Edit Flash message Transmission of Flash message SMS, fax... Message Broadcast by radio (local, national) Actions by users (authorities, general public) “Thunderstorm Flash-news”: tipical timing A severe thunderstorm is presently located over Geneva and will probably move in the next 60 minutes to the region of Lausanne. It can produce wind gust over 75 km/h or hail. Time [min] ?

“Thunderstorm Flash”: experience summer 2005 ProblemsSolutions Long cycle (analysis + dissemination)Further process automation Priority setting of TS cellsObjective severity classification (NinJo) Phase estimation / early detectionCells phase classification (NinJo) Wind Gust and Hail forecastLocal adapted algorithms Flash Flood forecast (stationary cells)Rain field extrapolation / accumulation Coarse localization in forecastHigher resolution / shorter forecast time

TRT: Summary and outlook TRT: automatic identification, tracking and monitoring of convective systems using radar and lightning data -Adaptive reflectivity thresholding -Splits / merges, complex cases -Time histories of cell attributes 2003: TRT operational at MeteoSwiss for nowcasting 2005: thunderstorms Flash-news warnings (positive preliminary assessment) 2006: visualisation in the NinJo workstation Outlook: more extensive use of 3D reflectivity data (echo top, VIL, probability of hail,... )