Clustering of Windstorms

Contact: [email protected] London 04.07.2012 1

University of Cologne Joaquim G. Pinto IGMK Clustering of Wind and Earthquake Events

Outline:

 What is clustering?

 Clustering of windstorms based on track data

 Clustering of windstorms within Pan- model

 Take-away messages

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University of Cologne Joaquim G. Pinto IGMK European Storm Climatology: Top 15 Events

Storm Storm Economic Damage Storm Name Fatalities rank Year Actual (USD) 1 1999 Lothar 137 11,350,000,000 2 2007 Kyrill 47 10,000,000,000 1990 season 3 1990 Daria 97 7,000,000,000 1999 season 4 2010 Xynthia 64 6,100,000,000 5 1999 Martin 90 6,000,000,000 6 2009 Klaus 28 6,000,000,000 7 2005 Erwin 18 5,505,000,000 8 1976 Capella 0 5,000,000,000 9 1987 Great Storm of 1987 23 4,000,000,000 10 1990 Vivian 50 3,500,000,000 11 1999 Anatol 27 3,000,000,000 12 2002 Jeanett 38 2,531,000,000 13 1995 Thalia 28 2,310,000,000 14 1990 Wiebke 67 2,260,000,000 15 1990 Herta 30 1,960,000,000

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University of Cologne Joaquim G. Pinto IGMK What is Clustering?

Within one week (11.01.2007-18.01.2007), three large storms affected Western Europe

Franz: 11-12.1 Hanno/Per: 13-14.1 Kyrill: 17.-18.1

Jan./Feb. 1990; Dec. 1999

Two processes: •Steering by large-scale patterns (e.g. NAO) •Secondary

Sources: Wetter3.de; Bjerkness and Solberg, 1922 4

University of Cologne Joaquim G. Pinto IGMK Methodology

Source: Pinto et al. (2009), Clim. Dyn; N.Bellenbaum, Bachelor Thesis; Mailler, (2006), PhD Thesis 5

University of Cologne Joaquim G. Pinto IGMK Quantification of clustering

Dispersion

Ψ =0: serial randomness Ψ <0: serial regularity Ψ >0: serial clustering

January – March 2007 Intensity

Source: Pinto et al. (2009), Clim. Dyn;, N.Bellenbaum, Bachelor Thesis 6

University of Cologne Joaquim G. Pinto IGMK Dispersion of cyclone statistics, all systems vs. vorticity > 95th percentile NCEP ERA40

Ψ <0: serial regularity; Ψ =0: serial randomness; Ψ >0: serial clustering Source: N.Bellenbaum, Bachelor Thesis 7

University of Cologne Joaquim G. Pinto IGMK Pan-European windstorm model: historical event set

2007

1999

1993 1990

SEP OCT NOV DEC JAN FEB MAR APR Source: K.Born, M. Karremann 8

University of Cologne Joaquim G. Pinto IGMK Clustering / Cyclone families

Wind signatures (and thus losses) cannot be separated over parts of the domain (e.g. ) [m/s]

2 – Lothar 3 - Martin

Source: P. Ludwig 9

University of Cologne Joaquim G. Pinto IGMK Clustering / Cyclone families

Wind signatures (and thus losses) cannot be separated over parts of the domain (e.g. North Sea) [m/s]

1 - Dimitri 3 – Freddy 2 – Erwin 4 - Gero

Source: P. Ludwig 10

University of Cologne Joaquim G. Pinto IGMK Take away messages

 The occurrence of windstorms does not happen purely by chance. As their development depends on specific large-scale atmospheric conditions, which may endure several days (weeks), „cyclone families“ may occur.

 The clustering of European windstorms is a natural process. It is stronger at the exit region of the storm track, and for intense storms.

 A complete separation between events (in space and time) is not always possible, both in loss and meteorological terms. Thus, the consideration of „cyclone families“ as one loss event may be helpful recommended.

 Clustering is being considered and modelled in the new Impact Forecasting Pan-European windstorm model

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University of Cologne Joaquim G. Pinto IGMK Pan-European Windstorm model

• AB Impact Forecasting team cooperates with the Institute of Geophysics and Meteorology of the University of Cologne to develop event set. • The research team at University of Cologne includes eight researchers. • A 3-year cooperation project with 2 major components: – Winter storms modelling solutions will deliver: • A realistic climatology of about 250 historical storms (footprints and tracks) at high resolution using RCM (completed 2012). • A stochastic events set of more than 10,000 synthetic events extracted from 4,700 years of GCM runs (completed 2012) – Summer storms and hailstorms solutions (scheduled for 2013) • Both the stochastic event set and the historical footprints will be implemented in the ELEMENTS platform

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University of Cologne Joaquim G. Pinto IGMK