Hail frequency in central Europe estimated from 2D/3D radar data and the relation to atmospheric characteristics
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1 Hail frequency in central Europe estimated from 2D/3D radar data and the relation to atmospheric characteristics Michael Kunz, Elody Fluck, Sven Baumstark, Jan Wandel, Stefan Ritz, Peter Geissbuehler, Sebastian Schemm, Manuel Schmidberger Institute of Meteorology and Climate Research (IMK-TRO) Center for Disaster Management and Risk Reduction Technology (CEDIM) Benke, 2014 KIT The Research University in the Helmholtz Association
2 Hailstorm Andreas, 28 July 2013 Andreas, 28 July 2013 (Kunz et al., 2017) 2
3 Severe hailstorms Germany 27 July 28 July 6 August Hannover Wolfsburg 14 cm Data: Stuttgart 2-4 cm 6-7 cm > 8 cm Total loss: 4.2 bn Insured loss: 3.3 bn only July: 2.8 bn (Swiss Re, 2014) 3
4 Questions Hail hazard assessment? Pre-convective conditions? Frontal vs. non-frontal hail? 4
5 Methods: Hail estimation from radar 2D lat ( ) height supercell formation; Z (dbz) 3D triggering lon ( ) Methods: (1) 2D: Maximum Reflectivity (Mason, 1971) X = 55 dbz (2) 3D: Vertical Distance (Waldvogel, 1979) Y (km) (Puskeiler et al., 2016) Cell tracking & advection & clutter corr. Calibration & verification: insurance losses 5
6 Radar-based hailclimatologies France, Belgium, Lux. 2D; Germany 3D; (Fluck, 2017; Schmidberger, 2017) Days with hail signals (2D/3D radar reflectivity) 6
7 Hail signals vs orography Location of hail hotspots: preferably over/downstream of mountains Fr = U N H 0.6 mean of 70 hail events (Kunz and Puskeiler, 2010, modified) (Fluck, 2017) 7
8 Hail probability vs orography Semi-idealized COSMO-DE simulations (2.8 km) Initialization: ambient conditions prior to damaging hailstorms (Fr ~ 0.6) Flow convergence Convective available potential energy CAPE DIV CON (Köbele, 2014) 8
9 Hail signals vs orography Elevation vs. number of haildays (normalized) (Fluck, 2017) 9
10 Track characteristics Hailstorm tracks vs hail size (diameter from ESWD)? Mapping hail reports ESWD with radar-detected hailstorm tracks outside: ESWD report 2 hailstreak R 10 km inside: ESWD report 1 (Baumstark, 2017) 10
11 diameter D in cm diameter D in cm Pre-convective conditions Hailstorm tracks vs hail size (diameter from ESWD)? < >150 length in km direction in Mapping hail reports ESWD with radar-detected hailstorm tracks outside: ESWD report 2 hailstreak R 10 km inside: ESWD report 1 (Baumstark, 2017) 11
12 Pre-convective conditions Combination hailstreaks with ESWD data (D_max) Composite of ambient conditions around hailstreaks centered in the domain Hail footprints from radar Mean (percentiles) of all events distance from the center (km) 12
13 distance from the center (km) Pre-convective conditions Mean fields around hailstreaks Lapse rate (1.5_5.5 km) 0-6 km wind shear distance from the center (km) K m s -1 distance from the center (km) 13
14 Max diameter haistones (ESWD) Pre-convective conditions Composite Lapse rate fct(l, D) < 3 cm 16.7 K 3-4 cm K 5 cm 17.6 K < 50 km km > 100 km track length 14
15 Max diameter haistones (ESWD) Pre-convective conditions Composite 0-6 km wind shear fct(l, D) < 3 cm 9 m s cm m s -1 5 cm 17.7 m s -1 < 50 km km > 100 km track length 15
16 D 5 cm, L>100 km D<3 cm, L<50 km Pre-convective conditions Effect of temporal/spatial resolution: ERA_Interim ( x ~ 80 km, 6 hrs.) vs CoastDatIII ( x ~ 10 km; 1 hr) Shear Lapse rate ERA CoastDat ERA CoastDat 16
17 Pre-convective conditions Categorical verification: Large hail & long track vs. small hail & tracks wind shear in m s -1 events: D 4 cm; L 100 km non-events: D < 4 cm; L < 75 km 17
18 lat ( ) Ambient conditions: Fronts Hailstreaks vs cold fronts? Track L lon ( ) Cold fronts 1 1 ; 6-hourly; ERA-Interim-Reanalysis (Schemm et al., 2015) thermal frontal parameter 0 (Baumstark, 2017) 18
19 lat ( ) lat ( ) Ambient conditions: Fronts Relative probability of frontal hailstreaks % lon ( ) 0.5 x 0.5 ; ERA-Interim / Radar-based streaks SHJ lon ( ) 0 (Baumstark, 2017) 19
20 lat ( ) lat ( ) Hailtracks: frontal vs non-frontal Track length: L frontal L non-frontal (90% percentiles) lon ( ) 0.5 x 0.5 ; ERA-Interim / Radar-based streaks SHJ lon ( ) (Baumstark, 2017) 20
21 lat ( ) lat ( ) Hailtracks: frontal vs non-frontal Orientation (median) non-frontal frontal lon ( ) 0.5 x 0.5 ; ERA-Interim / Radar-based streaks SHJ lon ( ) 21
22 Backward Trajectories 500 hpa e 850 hpa - ERA-Interim / LAGRANTO (Lagrangian Analysis Tool) hail events from ESWD - Trajectories 120 hrs backwards - Hail events: ESWD reports (Busch, 2013) 22
23 Backward Trajectories e Ensemble mean: equival. poential temp. e and specific humidity q v q v hours prior the hail events - ERA-Interim / LAGRANTO (Lagrangian Analysis Tool) hail events from ESWD - Trajectories 120 hrs backwards - Hail events: ESWD reports hours prior the hail events (Busch, 2013) 23
24 Conclusions Hail hazard assessment from radar is robust and physically plausible High spatial variability of radar-derived hail signals: large-scale: increase with distance to the sea local-scale: hot spots preferably downstream of the mountains Pre-convective conditions Lapse rate (maximum SE of event); ~ insensitive to diameter / length 0-6 km wind shear; both diameter and track length decisive Shear already captured by low temporal / spatial resolution Streaks vs Fronts 15-50% of all streaks are related to a cold front; large spatial differences Frontal streaks exhibit different characteristics: longer, direct. to the west, 24
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