Centroids. Centroid Principles Objects center of gravity or center of mass Graphically labeled as.
Feature-based classification of European windstorms - PhD … · 2019-12-12 · 1. k random...
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Feature-based classification of European windstormsPhD-Project: Changes in European windstorm characteristicsChristian Passow
Supervisors:
Univ.-Prof. Dr. Uwe UlbrichUniv.-Prof. Dr. Henning Rust
Freie Universität Berlin
8th European Windstorm workshop, 2019
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Windstorms
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Understanding windstorms
In conclusion:Understanding atmospheric drivers behind windstorms of highsocio-economical importanceÉ Statistically sound risk assessment and managementÉ Improvement of forecast systemsÉ More robust information on future risks of windstorms due to climate
change
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State of the art
Pinto et al. (2009)É Extreme cyclones occur more frequently during strong positive NAO
phase
Donat et al. (2010)É Westerly flow regimes and positive NAO phase associated with the
majority of storm days
Walz et al. (2018)É Drivers may change depending on the region of interest. NAO alone
is not sufficient to assess winter windstorm hazard
Wild et al. (2015)É Meridional temperature gradient between North American continent
and western Atlantic SSTs is positively correlated to windstormfrequency over North Atlantic and Europe
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The Project
É Past studies focused primarily on "more statistical" characteristics,e.g. . . .É Inter-annual variabilityÉ Serial clusteringÉ OccurrenceÉ Trends
É Only a few studies focus on the basic windstorm characteristics suchas . . .É IntensityÉ DurationÉ Spatial extensionÉ Shape
Aim:
1. Quantification of these characteristics
2. Identification and understanding of key parameters determiningthese characteristics
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Region of interest
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Quantification of windstormcharacteristics
É Clustering windstorms based on basic featuresÉ Summary statisticsÉ Storm tracks
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Data & tracking
ERA5:É Fifth generation ECMWF atmospheric reanalysis of the global climateÉ Horizontal resolution: 0.25◦ x 0.25◦
É Period: 1981-2017, extended winter ONDJFMÉ Temporal resolution: 6 hours
Tracking:É WTRACK algorithm (Kruschke, 2014)
É Exceedence of local climatological 98th percentileÉ Nearest-Neighbor searchÉ Storm duration of > 24h and area of > 150.000 km2
É Boundary: full gridÉ Innerbox: EURO-CORDEX region (40.25◦W–75.25◦E,
25.25◦N–75.75◦N)
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Clustering - Preparing the data
Raw WTRACK output:
Feature table:É Duration [h]É First and last sighting (lon,lat)É Maximal area and radius [km]É Mean, minimum and maximum wind speed [m/s]É Mean and maximum SSI
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Clustering - Method
K-Means clustering
1. k random centroids (initialization)2. Observations are assigned to nearest centroid (assignments)
É Squared Euclidean distance
3. New centroids by averaging cluster members (updating)
4. Repeat 2-3 until assignments do not change anymore
SettingÉ k varies from 2-10É Ensemble approach (50 member ensemble)É Best-fit
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Clustering - Results
Cluster No. 1 Cluster No. 2 Cluster No. 3
-0.5
0.0
0.5
1.0
1.5durationfirstdetlonfirstdetlatlastdetlonlastdetlatmaxareamaxradiusmeanvminvmaxvSSImeanSSImax
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Clustering - Results
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Clustering - Results
k Ave. wind speeds [m/s] Peak [m/s] Dur. [h] Area N SSI
Mean Minim. Maxim.
1 17.33 10.35 24.41 38.3 39 422 1420 0.24
2 11.03 5.54 18.41 27.65 35 347 1485 0.40
3 13.11 6.07 23.06 35.75 65 1400 437 1.72
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Identification of key parameters(coming soon)
É Classification taskÉ Supervised learning algorithms: Decision trees, GLMs, . . .
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Challenges - How to represent th field?
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Challenges - How to represent th field?
NE E SE S SW W NW N C AC
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uenc
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NE E SE S SW W NW N C AC
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Cluster No. 2
NE E SE S SW W NW N C AC
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Cluster No. 3
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Challenges - Overlap
1 2 3 4 1 & 2 1 & 3 2 & 3 all
0
500
1000
1500
Cluster types
No.
of d
ays
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Summary
É Clusters suggest different types of windstorms
É Tracks are neither separated in space nor time
É Designing the right data set for the task is no trivial matter
É Better differentiation through careful selection of meteorologicalfields and areas
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References I
Donat, M. G., Leckebusch, G. C., Pinto, J. G., and Ulbrich, U. Examinationof wind storms over Central Europe with respect to circulation weathertypes and NAO phases. International Journal of Climatology, 30(9):1289–1300, 2010. ISSN 08998418. doi: 10.1002/joc.1982.
Kruschke, T. Winter wind storms: Identification, verification of decadalpredictions, and regionalization. PhD thesis, Institute of Meteorology,Freie Universität Berlin, 2014.
Pinto, J. G., Zacharias, S., Fink, A. H., Leckebusch, G. C., and Ulbrich, U.Factors contributing to the development of extreme North Atlanticcyclones and their relationship with the NAO. Climate Dynamics, 32(5):711–737, apr 2009. ISSN 0930-7575. doi: 10.1007/s00382-008-0396-4.URL http://link.springer.com/10.1007/s00382-008-0396-4.
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References II
Walz, M. A., Befort, D. J., Kirchner-Bossi, N. O., Ulbrich, U., andLeckebusch, G. C. Modelling serial clustering and inter-annualvariability of European winter windstorms based on large-scale drivers.International Journal of Climatology, 38(7):3044–3057, jun 2018. ISSN08998418. doi: 10.1002/joc.5481. URLhttp://doi.wiley.com/10.1002/joc.5481.
Wild, S., Befort, D. J., and Leckebusch, G. C. Was the Extreme StormSeason in Winter 2013/14 Over the North Atlantic and the UnitedKingdom Triggered by Changes in the West Pacific Warm Pool? Bulletinof the American Meteorological Society, 96(12):S29–S34, 2015. doi:10.1175/BAMS-D-15-00118.1.
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