A COMPARISON OF THE LIGHTNING JUMP ALGORITHM USING TOTAL LIGHTNING VERSUS CLOUD-TO-GROUND FLASHES.

Transcription

1 A COMPARISON OF THE LIGHTNING JUMP ALGORITHM USING TOTAL LIGHTNING VERSUS CLOUD-TO-GROUND FLASHES. Rigo, T. (1), C. Farnell (1, 2) (1) Servei Meteorologic de Catalunya (2) University of Barcelona. Faculty of Geography and History.

2 Introduction Scheme of the talk - Severe weather phenomena in Catalonia - Lightning data: the operational LLS and flash detection - The lightning jump algorithm: LJF17 - Different configurations of the lightning data - Results and conclusions 2 Severe weather def.

3 The data base of severe weather phenomena of the SMC Definition of severe weather Hail > 2 cm diameter Wind gusts or downbursts exceeding the 25 m/s Tornadoes/waterspouts * * Future goal: only those with mesocyclone observation in radar imagery 3 SW DDBB

4 The data base of severe weather phenomena of the SMC Some points on respect the DDBB We have considered not only severe weather registers. Small hail (0.5 to 2 cm) and moderate winds are included in the database. Sources: Barcelona - SMC campaigns (~2017, H), - hail-pads (~2004, H), - SMC spotters (~2009, H/W/T), - SMC AWS (~2006, W), - written press (~2001, H/W/T), - TV (~2003, H/W/T), - social media (~2012, H/W/T), - agricultural departments (~2001, H/W) 4 SW DDBB

5 The data base of severe weather phenomena of the SMC Some points on respect the DDBB Begins at Most of registers have been validated with radar imagery and lightning data First years (<2009) clearly incomplete: necessity to search for more registers voluntaries in the room? Issues: - not homogeneous; - highly depending on: * the density of population, * the time (day/night) of the event, * the reliability of the observation (human tend to increase the measurement); * the identification of fake cases (social media) 5 Map distr.

6 The data base of severe weather phenomena of the SMC Map of affectation (number of registers per pixel) All events Severe weather 6 XDDE

7 Lightning data: description of the LLS and registers The Lightning Location System: XDDE First campaign at 2003 Operative since 2006 (DDBB also begins) The LLS is composed by 4 detectors (VHF & LF antennas), being capable of identify separately intra-cloud (IC) and cloud-to-ground (CG) flashes Location of flashes (by triangulation) is quite precise (<1km) inside the area covered by the 4 sensors (problems in the boundary regions) Multiplicity of flashes (IC and CG) can be considered or not 7 XDDE/LMA

8 Lightning data: description of the LLS and registers Map of the network Multiplicity in ICs and CGs LMA XDDE Only 2D, instead of 3D (-) Larger area covered than LMA (+) Less sources than LMA (1/25) (-) But more than LF or VLF LLS (10/3) (+) 8 LJ

9 The lightning jump algorithm adapted to Catalonia Description of the algorithm (more info in Farnell et al.) Continuity in time (>14 min) and in space DFDRT now > 2 * σ Two levels: (1 Total Lightning Multiplicity) for small hail, heavy rainfall and moderate winds, (2 Total Lightning without Multiplicity) for severe weather 9 LJ op

10 The lightning jump algorithm adapted to Catalonia Operational example - blue dots show multiplicity position evolution (level 1) - red dots, associated with non-multiplicity (level 2) Only with lightning data No thresholds of lightning rate Radar for forecasting the future trajectories Also for identification of the convective mode 10 W types

11 Different configurations of the lightning data used 4 types of data were used for the period TYPE Operational Flashes Multiplicity WTM YES CG+IC YES WTN YES CG+IC NO WCM NO CG YES WCN NO CG NO Tested in 830 days with observations (DDBB). 11 W distr

12 Different configurations of the lightning data used 4 types of data were used for the period CG Mult CG No Mult?? All registers Severe weather IC+CG Mult IC+CG No Mult 12 Time distr

13 Different configurations of the lightning data used Monthly distributions 13 examp

14 Why the CGs only are not enough? 6 examples of how different can be an event in Catalonia 14 Validation anim

15 Different configurations of the lightning data used The validation: some examples (radar animations) 15 Validation

16 Different configurations of the lightning data used The validation: some examples (obs vs warns) 16 Flash evol.

17 Why the CGs only are not enough? Lightning evolution WCM and WTN have similar number of sources But the proportion of warnings is of only 33% in the case of WTN, on respect the WCM This rate varies depending on the event 17 Behaviour prior W

18 Why the CGs only are not enough? Conclusions The time continuity indicates that the WCM configuration reaches the 14 threshold less times that WTN CGs not presents generally a continuity in severe thunderstorms ICs are the cause of this continuity The standard deviation shows the variation of the flash rate during the period prior to the alert CGs not shows large variability ICs are highly responsible of the increase 18 Final

19 Why the CGs only are not enough? Final remarks CG flashes are not enough for nowcasting severe weather Only in few cases the WCN and WCM configurations produce LJ warnings Spatial and time distributions not agree with severe weather and small hail registers The number of sources for WCM is larger than the WTN, but the continuity and the jump not occur as frequent IC and CG+ flashes increases prior to the severe weather, but the CGdecreases 19 Final

20 Why the CGs only are not enough? Final remarks We have shown that LJ algorithm can run operationally in a VHF+VF LLS network, with less sources than LMA, but with larger coverage of detection We encourage to the Weather Services to improve their networks with VHF detectors, if they want to forecast severe hail Final question: how useful is a 90 warning? 20

21 Hail team at

22 Different configurations of the lightning data used Some notes about the 4 types of data were used Configurations considering only CG flashes produced few warnings, on respect the maps of registers and the monthly distributions The CG distributions do not agree with the maps of density of affectations Configuration TM produces more alerts than the expected, for severe weather, but agrees with total registers Configuration TN seems to reproduce better the map of severe weather registers Important: 1 warning can be related with more than 1 register! 22

23 Why the CGs only are not enough? Behavior before the warnings WCM and WTN have similar boxplots But two parameters are key factors for triggering the warnings (respecting the LJF17 configuration) 23 Concl

24 Different configurations of the lightning data used The validation: some examples (obs vs warns) 24 Validation using POH