Lagrangian Analysis and Modeling of Correlated Weather and Climate Extremes
PD Dr. Christian Franzke (email@example.com)
PD Dr. Richard Blender (firstname.lastname@example.org
Dr. Frank Lunkeit (email@example.com)
Alexia Karwat (firstname.lastname@example.org)
Extreme weather and climate events are of great general societal interest since they cause significant economic damages and fatalities each year. The description and prediction of extremes is involved since these events can be correlated. European windstorms, for example, occur more often in bunches than if they were independent events (Franzke 2013, Blender et al. 2015). The observed clustering of extremes is caused by meteorological processes like Rossby waves and downstream development. Blender et al. (2015) have shown that the return times between severe windstorms are non-exponential and have to be modeled by a fractional Poisson process instead of the standard Poisson model for rare events. Extreme events are typically examined from a Eulerian perspective by determining the extreme value characteristics at particular stations or grid points. Here we propose a different and novel approach by taking a Lagrangian view on extremes to grasp their accumulated impact. The vortex tracking (Blender et al. 1997, Sienz et al. 2010) is combined with an analysis of the extreme value characteristics and the clustering properties. The return times between two extreme events are scientifically relevant and important for stakeholders and the insurance industry.
We aim to understand and predict severe weather and climate events by combining the Lagrangian cyclone view with newly developed concepts for the serial clustering of extreme events. We will elucidate the driving mechanisms of the serial clustering of cyclones.
In preparation: https://www.climxtreme.de/