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.
Highlights
20 Weather systems and extreme weather events result from nontrivial three-dimensional (3-D) interactions in the atmosphere
This study presents a novel algorithm for the objective identification of Potential vorticity (PV) anomalies in gridded data, as commonly output by numerical simulation models
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
Summary
20 Weather systems and extreme weather events result from nontrivial three-dimensional (3-D) interactions in the atmosphere. Reducing atmospheric processes of importance to concise visual depictions facilitates combination of multiple aspects of atmospheric dynamics in 105 a comprehensive 3-D display well suited for rapid exploration of the considered numerical simulation data (e.g., Rautenhaus et al, 2015a; Kern et al, 2019) We demonstrate such analysis by incorporating the identified PVA features into the 3-D meteorological visualization framework “Met.3D” (Rautenhaus et al, 2015b), which we use to shed light on the 3-D structure of PVAs encountered during an extreme precipitation event previously investigated by Van der Linden et al (2017).
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