Preprints
https://doi.org/10.5194/gmd-2021-424
https://doi.org/10.5194/gmd-2021-424

Submitted as: methods for assessment of models 04 Jan 2022

Submitted as: methods for assessment of models | 04 Jan 2022

Review status: this preprint is currently under review for the journal GMD.

A novel method for objective identification of 3-D potential vorticity anomalies

Christoph Fischer1,2, Andreas H. Fink2, Elmar Schömer1, Roderick van der Linden2, Michael Maier-Gerber2, Marc Rautenhaus3, and Michael Riemer4 Christoph Fischer et al.
  • 1Institute of Computer Science, Johannes-Gutenberg University, Mainz, Germany
  • 2Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
  • 3Regional Computing Centre, Visual Data Analysis Group, University of Hamburg, Hamburg, Germany
  • 4Institute for Atmospheric Physics, Johannes-Gutenberg University, Mainz, Germany

Abstract. Potential vorticity (PV) analysis plays a central role in studying atmospheric dynamics and in particular in studying the life cycle of weather systems. The three-dimensional (3-D) structure and temporal evolution of the associated PV anomalies, however, are not yet fully understood. An automated technique to objectively identify 3-D PV anomalies can help to shed light on 3-D atmospheric dynamics in specific case studies, as well as facilitate statistical evaluations within climatological studies. Such a technique to identify PV anomalies fully in 3-D, however, does not yet exist. This study presents a novel algorithm for the objective identification of PV anomalies in gridded data, as commonly output by numerical simulation models. The algorithm is inspired by morphological image processing techniques and can be applied to both two-dimensional (2-D) and 3-D fields on vertically isentropic levels. The method maps input data to a horizontally stereographic projection and relies on an efficient computation of horizontal distances within the projected field. Candidates for PV anomaly features are filtered according to heuristic criteria, and feature description vectors are obtained for further analysis. The generated feature descriptions are well suited for subsequent case studies of 3-D atmospheric dynamics as represented by the underlying numerical simulation, or for generation of climatologies of feature characteristics. We evaluate our approach by comparison with an existing 2-D technique, and demonstrate the full 3-D perspective by means of a case study of an extreme precipitation event that was dynamically linked to a prominent subtropical PV anomaly. The case study demonstrates variations in the 3-D structure of the detected PV anomalies that would not have been captured by a 2-D method. We discuss further advantages of using a 3-D approach, including elimination of temporal inconsistencies in the detected features due to 3-D structural variation, and elimination of the need to manually select a specific isentropic level on which the anomalies are assumed to be best captured. The method is made available as open-source for straightforward use by the atmospheric community.

Christoph Fischer et al.

Status: open (until 01 Mar 2022)

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Christoph Fischer et al.

Model code and software

A novel method for objective identification of 3-D potential vorticity anomalies - Implementation Fischer, Christoph, Fink, Andreas H., Schömer, Elmar, Van der Linden, Roderick, Maier-Gerber, Michael, Rautenhaus, Marc, & Riemer, Michael https://doi.org/10.5281/zenodo.5638561

Video supplement

A novel method for objective identification of 3-D potential vorticity anomalies - Visualizations using Met.3D Fischer, Christoph, Rautenhaus, Marc, Fink, Andreas H., Schömer, Elmar, Van der Linden, Roderick, Maier-Gerber, Michael, & Riemer, Michael https://doi.org/10.5281/zenodo.5639001

Christoph Fischer et al.

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Short summary
Potential vorticity (PV) analysis plays a central role in studying atmospheric dynamics. For example, anomalies in the PV field near the tropopause are linked to extreme weather events. In this study, an objective strategy to identify these anomalies is presented and evaluated. As novel concept, it can be applied to three-dimensional (3-D) data sets. Supported by 3-D visualizations, we illustrate and showcase upsides and potential of this new analysis over existing studies along a case study.