Speaker
Description
In the BMBF-funded ClimXtreme CoDEx project, we use advanced data compression techniques to analyze and characterize high-dimensional spatio-temporal weather extremes. By reducing the number of degrees of freedom, we improve the signal-to-noise ratio, facilitating a more precise assessment and detailed characterization of extreme weather events.
Here, we present a novel approach using wavelet decomposition to capture and analyze the complex spatio-temporal characteristics of precipitation extremes. Wavelet decomposition has proven to be highly effective in uncovering underlying frequency structures in time series data and is well-suited for analyzing two-dimensional patterns. Previous applications to spatial precipitation fields demonstrate their benefit for a better understanding and improved description of precipitation events.
We extend these methods to capture both spatial and temporal characteristics, providing a comprehensive description of three-dimensional precipitation fields across space and time.
We show that this approach is effective in capturing the diverse spatio-temporal features of precipitation extremes, enabling a more targeted and nuanced description of processes driving extreme weather phenomena. Our applications include comparisons of various datasets for their their representation of extreme precipitation events, with a focus on high-resolution data such as radar observations and simulations on convective-permitting scales. We also analyze and describe recent precipitation extremes in Germany, including the May/June 2024 flooding in southern Germany and the Ahr flooding in 2021.
| Presenting Author | Svenja Szemkus |
|---|---|
| Email Address of Presenting Author | sszemkus@uni-bonn.de |
| Affiliation of Presenting Author | Institute of Geosciences, Meteorology Section, University of Bonn, Germany |
| Address of Presenting Author | Auf dem Hügel 20, 53121 Bonn |
| Session | Precipitation and Hydrological Models: Extreme precipitation events |
| Preferred Contribution Type | Oral Presentation |
