Topology-Based Feature Design and Tracking for Multi-center Cyclones

Engelke, Wito; Masood, Talha Bin; Beran, Jakob; Caballero, Rodrigo; Hotz, Ingrid

doi:10.1007/978-3-030-83500-2_5

Cited by 12 publications

(11 citation statements)

References 21 publications

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“…Different aspects of visualization in meteorology have been addressed, including feature-based visualization, flow visualization and 3D interactive visual analysis techniques, all relevant to the techniques discussed in our work. Recent examples in the literature include 3D visual analysis of severe weather (e.g., of supercell thunderstorms [46] and polar lows [41]), and feature-based (mostly 3D) techniques to track cyclone paths [15] as well as more generic scalar extremal features [31], and to analyse potential vorticity banners [3], jet stream flow [8,32], atmospheric fronts [33], and -as in this study-WCB trajectories [53]. Visual analysis of simulation uncertainty in meteorology has mostly been addressed in the context of visualizing output from simulation ensembles [52,66], e.g., to characterize uncertainty in NWP with respect to uncertainty in initial values.…”

Section: Related Workmentioning

confidence: 99%

Visualization of Model Parameter Sensitivity along Trajectories in Numerical Weather Predictions

Neuhauser¹,

Hieronymus²,

Kern³

et al. 2022

Preprint

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Fig. 1. Visual analysis of the sensitivity of a state variable to selected model parameters (red names in curve-plot overlay) along warm conveyor belt trajectories in the extratropical cyclone "Vladiana", to assess uncertainties of parameterizations in numerical weather prediction models. State variable (blue) and maximum sensitivity (red) are color coded in view-aligned bands along trajectories. Embedded curve-plot shows statistical summaries of state variables and sensitivities. Selected sensitivities and state variables (in top to bottom order) are assigned to pie charts in the 3D view in clockwise order. Pie charts show values of multiple sensitivities or state variables along trajectories at selected time steps, or (as shown in Fig. 10) aligned to the time of ascent of each trajectory. View-aligned pie charts enable an effective comparison of sensitivities on multiple trajectories. The blue color on the ground shows surface precipitation.

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Section: Related Workmentioning

confidence: 99%

Visualization of Model Parameter Sensitivity along Trajectories in Numerical Weather Predictions

Neuhauser¹,

Hieronymus²,

Kern³

et al. 2022

Preprint

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“…For a survey on the substantial history of structure extraction and visualization, see reviews on geometric‐topological objects [MLP∗10, PPF∗11], on coherent set detection [HFB∗17], on feature tracking in meteorological contexts [CBJ∗14, CDJ15, VMN∗18,EMB∗21], and on the related problem of image segmentation [EBJS17]. Most approaches in visualization refer to structures defined by single scalar or vector fields, e.g., level sets or ridges of Lyaponov exponents, or application‐related structures, like centerlines of vortices [SWH05a] or jet streams [KHS∗18].…”

Section: Related Workmentioning

confidence: 99%

An Interactive Approach for Identifying Structure Definitions

Mikula

Dörffel

Baum

et al. 2022

Computer Graphics Forum

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Our ability to grasp and understand complex phenomena is essentially based on recognizing structures and relating these to each other. For example, any meteorological description of a weather condition and explanation of its evolution recurs to meteorological structures, such as convection and circulation structures, cloud fields and rain fronts. All of these are spatiotemporal structures, defined by time‐dependent patterns in the underlying fields. Typically, such a structure is defined by a verbal description that corresponds to the more or less uniform, often somewhat vague mental images of the experts. However, a precise, formal definition of the structures or, more generally, of the concepts is often desirable, e.g., to enable automated data analysis or the development of phenomenological models. Here, we present a systematic approach and an interactive tool to obtain formal definitions of spatiotemporal structures. The tool enables experts to evaluate and compare different structure definitions on the basis of data sets with time‐dependent fields that contain the respective structure. Since structure definitions are typically parameterized, an essential part is to identify parameter ranges that lead to desired structures in all time steps. In addition, it is important to allow a quantitative assessment of the resulting structures simultaneously. We demonstrate the use of the tool by applying it to two meteorological examples: finding structure definitions for vortex cores and center lines of temporarily evolving tropical cyclones. Ideally, structure definitions should be objective and applicable to as many data sets as possible. However, finding such definitions, e.g., for the common atmospheric structures in meteorology, can only be a long‐term goal. The proposed procedure, together with the presented tool, is just a first systematic approach aiming at facilitating this long and arduous way.

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“…This corresponds to a set of extrema falling in a level set adding (or respectively subtracting) a local offset threshold from its dominant extremum. This feature definition has been used for cyclone definition [7,20] and also in the medical context of building a patient-specific brain atlas [24]. There are other more application-specific feature definitions using critical points.…”

Section: Related Workmentioning

confidence: 99%

“…For tracking in the visualization, this method was used as Combinatorial Feature Flow Fields as introduced by Reininghaus et al [25]. Since then, it has been modified and used in several applications [7,11,20]. For a more detailed summary of topology-based tracking, see the review article by Yan et al [38].…”

Section: Related Workmentioning

confidence: 99%

“…Now, split events can also be captured and we have some notion of strength of connection between minima in consecutive steps, with an indication of "strong" connection between m i of m j if the they are mapped to each other via the forward and backward maps, and "weak" connection if only one of the forward or backward map establishes a connection between the pair of minima. These gradient-based tracking approaches either explicitly compute the integral lines of point features [20], or retrieve that information from precomputed ascending/descending manifolds [7]. Our approach follows the latter, where the manifolds can efficiently be computed via path compression [15].…”

Section: Introductionmentioning

confidence: 99%

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Towards Benchmark Data Generation for Feature Tracking in Scalar Fields

Nilsson

Lukasczyk

Masood

et al. 2022

2022 Topological Data Analysis and Visualization (TopoInVis)

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Feature tracking is a common task in visualization applications, where methods based on topological data analysis (TDA) have successfully been applied in the past for feature definition as well as tracking. In this work, we focus on tracking extrema of temporal scalar fields. A family of TDA approaches address this task by establishing one-to-one correspondences between extrema based on discrete gradient vector fields. More specifically, two extrema of subsequent time steps are matched if they fall into their respective ascending and descending manifolds. However, due to this oneto-one assignment, these approaches are prone to fail where, e.g., extrema are located in regions with low gradient magnitude, or are located close to boundaries of the manifolds. Therefore, we propose a probabilistic matching that captures a larger set of possible correspondences via neighborhood sampling, or by computing the overlap of the manifolds. We illustrate the usefulness of the approach with two application cases.

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Topology-Based Feature Design and Tracking for Multi-center Cyclones

Cited by 12 publications

References 21 publications

Visualization of Model Parameter Sensitivity along Trajectories in Numerical Weather Predictions

Visualization of Model Parameter Sensitivity along Trajectories in Numerical Weather Predictions

An Interactive Approach for Identifying Structure Definitions

Towards Benchmark Data Generation for Feature Tracking in Scalar Fields

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