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Winter Storms in Switzerland - Analysis of Weather Classes

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Winter Storms in Switzerland - Analysis of Weather Classes Winter storms in Switzerland - Analysis of weather classes Master’s Thesis Faculty of Science University of Bern presented by Mikhael¨ Schwander 2013 Supervisor: Prof. Dr. Stefan Bronnimann¨ Institute of Geography of the University of Bern and Oeschger Centre for Climate Change Research, University of Bern Co-Supervisor: Prof. Dr. Olivia Romppainen-Martius Institute of Geography of the University of Bern and Oeschger Centre for Climate Change Research, University of Bern Advisors: Dr. Christoph Welker Peter Stucki Institute of Geography of the University of Bern and Oeschger Centre for Climate Change Research, University of Bern Abstract This study investigates windstorms in Switzerland in the 1981-2012 period. The aim was to identify weather patterns that are conducive to high-wind days. For this, two automatic weather type classifications are used (GWT and CAP) to classify days with high-winds. These days have previously been identify using wind gust measurements from ground weather stations. Based on one of the classifications (CAP27), wind regions of Switzerland are determined. The outcome of the CAP27 classification is analyzed using composite plots of several weather parameters. The analysis focuses on winter storms and foehn storms. The results show a pre- dominance of westerly flows over Switzerland during high-wind days. A low pressure system over Northern Europe and a high pressure system over the Azores generate a tight pressure gradient and strong winds over Switzerland. A shift of the anticyclone further north on the Atlantic generates a northwesterly flow. A small shift in the flow’s direction can significantly change the regions affected due to the particular topography of the Alps. The main differences are observed between the Plateau, the Alps and the Alpine South side. South-north oriented valleys have the particularity to be very sensitive to foehn winds in case of a southerly flow over the Alps. A more detailed analysis of composites (using the ERA-Interim reanalysis data set) shows the difficulty to model the influence of the Alps on the large-scale flow. A higher- resolution reanalysis could probably a better estimation of the flow in Switzerland. Also ex- treme windstorms result from a combination of several factors that need to be analyzed more precisely because they cannot be identified on composites. Contents Abstract i Table of Contents i 1 Introduction 1 1.1 Motivation . 1 1.2 State of research and aim of study . 2 2 Data 5 2.1 Weather stations . 5 2.2 Weather types classifications . 5 2.3 ERA-Interim . 8 3 Methods 9 3.1 Definition of high-wind events . 9 3.2 Definition of wind regions . 10 3.3 Definition of foehn events . 12 3.4 Analysis of high-wind events . 12 4 Results 13 4.1 High-wind days classification . 13 4.1.1 GWT . 13 4.1.2 CAP . 15 4.2 Switzerland’s wind regions . 18 4.2.1 Clustering outcome . 18 4.2.2 Application of weather types classification CAP27 on regions . 20 i CONTENTS ii 4.3 Foehn stations high-wind days classification . 22 4.4 Weather parameters for Switzerland [CAP27] . 23 4.4.1 Sea Level Pressure et 500 hPa Geopotential Height . 23 4.4.2 850 hPa Wind . 25 4.4.3 300 hPa Wind (jet stream) and Potential Vorticity . 26 4.4.4 North Atlantic Oscillation - East Atlantic Pattern . 27 4.4.5 Sea Level Pressure and 500 hPa Geopotential Height Differences . 28 4.4.6 850 hPa Wind Differences . 30 4.4.7 Sea Level Pressure Anomaly . 31 4.4.8 Sea Level Pressure and 500 hPa Geopotential Height Standard Deviation 31 4.4.9 Sea Surface Temperature Anomalies . 32 4.5 Weather parameters for foehn stations [CAP27] . 32 4.5.1 Sea Level Pressure et 500 hPa Geopotential Height . 32 4.5.2 850 hPa Wind . 34 4.5.3 300 hPa Wind (jet stream) and Potential Vorticity . 34 4.5.4 North Atlantic Oscillation - East Atlantic Pattern . 35 4.5.5 Sea Level Pressure et 500 hPa Geopotential Height Differences . 35 4.5.6 Sea Level Pressure Anomaly . 37 4.6 Specific events . 38 4.6.1 26 December 1999 . 38 4.6.2 26 January 1995 . 42 4.6.3 29 January 1986 . 45 5 Discussion 49 5.1 Evaluation of the Weather Type Classifications and Clustering . 49 5.1.1 GWT . 49 5.1.2 CAP and evaluation of the weather stations clustering . 50 5.2 Analysis of the Weather Parameters for the CAP27 WTC . 52 5.2.1 Winter storms . 52 5.2.2 Foehn storms . 54 5.3 Analysis of Three Windstorms . 55 5.3.1 26 December 1999 . 55 CONTENTS iii 5.3.2 26 January 1995 . 56 5.3.3 29 January 1986 . 57 6 Conclusion 58 Appendix 60 Bibliography 67 List of Tables 4.1 HWDs values corresponding to figures 4.3, 4.5 and 4.11 . 17 iv List of Figures 2.1 Distribution of the 137 SwissMetNet stations . 6 2.2 GWT prototypes patterns . 8 3.1 Daily median winter wind gust values for Switzerland . 10 3.2 Winter high-wind days for Switzerland . 10 3.3 Clustering of stations based on the CAP27 classification . 11 4.1 Classification of the HWDs according to the GWT10-z500 and GWT10-msl WTCs . 13 4.2 Classification of the HWDs according to the GWT18-z500 and GWT18-msl WTCs . 14 4.3 Classification of the HWDs according to the GWT26-z500 and GWT26-msl WTCs . 15 4.4 Classification of the HWDs according to the CAP9 and CAP18 WTCs . 16 4.5 Classification of the HWDs according to the CAP27 WTC . 16 4.6 Clustering of Swiss weather stations into 3 groups based on the CAP27 WTC . 18 4.7 Clustering of Swiss weather stations into 6 groups based on the CAP27 WTC . 19 4.8 CAP27 classification corresponding to figure 4.7 clustering for the regions North 1 and North 2 . 20 4.9 CAP27 classification corresponding to figure 4.7 clustering for regions Alps 1 and Alps 2 . 21 4.10 CAP27 classification corresponding to figure 4.7 clustering for regions West and South . 22 4.11 Classification of the HWDs according to the CAP27 for the foehn stations . 22 4.12 z500 and SLP composites of all HWDs for CAP27 types 10, 15, 20, 21, 24 and 27......................................... 24 4.13 850 hPa wind composites of all HWD for CAP27 types 10, 15, 20, 21, 24 and 27 25 v LIST OF FIGURES vi 4.14 300 hPa wind and PV composites of all HWD for CAP27 types 10, 15, 20, 21, 24 and 27 . 27 4.15 NAO and EA phases for the HWDMs . 28 4.16 Differences between the composites of all days and the composites of all HWDs for CAP27 types 10, 15, 20, 21, 24 and 27 . 29 4.17 Differences between the composites of all days and the composites of all HWD for CAP27 types 10, 15, 20, 21, 24 and 27 . 30 4.18 Sea level pressure anomalies for the CAP27 types 10, 15, 20, 21, 24 and 27 . 31 4.19 Standard deviation of the SLP and z500 HWDs composites for the CAP27 types 10, 15, 20, 21, 24 and 27 . 32 4.20 z500 and SLP composites of all foehn stations HWDs for CAP27 types 4, 17, 19, 21, 24 and 27 . 33 4.21 850 hPa wind composites of all foehn stations HWDs for CAP27 types 4, 17, 19, 21, 24, 27 . 34 4.22 300 hPa wind and PV composites of all foehn stations HWDs for CAP27 types 4, 17, 19, 21, 24 and 27 . 35 4.23 NAO and EA phases for the foehn stations HWDMs . 36 4.24 Differences between the composites of all days and the composites of all HWDs (for the foehn stations) for CAP27 types 4, 17, 19, 21, 24 and 27 . 37 4.25 Sea level pressure anomalies for the foehn stations HWDs in CAP27 types 10, 15, 20, 21, 24 and 27 . 38 4.26 z500 and SLP on the 26.12.1999 . 39 4.27 850 hPa wind on the 26.12.1999 . 40 4.28 300 hPa on the 26.12.1999 . 40 4.29 PV on the 26.12.1999 . 41 4.30 SLP and t850 on the 26.12.1999 . 41 4.31 Sea surface temperature anomalies on the 26.12.1999 and 26.01.1995 . 41 4.32 Sea level pressure anomalies on the 26.12.1999, 26.01.1995 and 29.01.1986 . 42 4.33 z500 and SLP on the 26.01.1995 . 42 4.34 850 hPa winds on the 26.01.1995 . 43 4.35 300 hPa wind on the 26.01.1995 . 44 4.36 PV on the 26.01.1995 . 44 4.37 SLP and t850 on the 26.01.1995 . 45 4.38 z500 and SLP on the 29.01.1989 . 46 LIST OF FIGURES vii 4.39 850 hPa wind on the 29.01.1989 . 46 4.40 300 hPa wind on the 29.01.1989 . 47 4.41 PV on the 29.01.1989 . 47 4.42 SLP and t850 on the 29.01.1989 . 48 Chapter 1 Introduction 1.1 Motivation Windstorms in Switzerland are among the most extreme weather events in terms of natural and socio-economics impacts. Infrastructures and forests are affected almost every winter by storms. Most of the time storm related damage are not significant, but the impacts of the most intense storms can be disastrous, e.g. “Vivian” (February 1990) or “Lothar” (December 1999). Forests are quite vulnerable to to high winds.
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