An Integrated Geometric and Topological Approach for the Identification and Visual Analysis of Rossby Wave Packets

Abstract

<jats:title>Abstract</jats:title> <jats:p>A new method for identifying Rossby wave packets (RWPs) using 6-hourly data from the ERA-Interim is presented. The method operates entirely in the spatial domain and relies on the geometric and topological properties of the meridional wind field to identify RWPs. The method represents RWPs as nodes and edges of a dual graph instead of the more common envelope representation. This novel representation allows access to both RWP phase and amplitude information. Local maxima and minima of the meridional wind field are collected into groups. Each group, called a υ-max cluster or υ-min cluster of the meridional wind field, represents a potential wave component. Nodes of the dual graph represent a υ-max cluster or υ-min cluster. Alternating υ-max clusters and υ-min clusters are linked by edges of the dual graph, called the RWP association graph. Amplitude and discrete gradient-based filtering applied on the association graph helps identify RWPs of interest. The method is inherently robust against noise and does not require smoothing of the input data. The main parameters that control the performance of the method and their impact on the identified RWPs are discussed. All filtering and RWP identification operations are performed on the association graph as opposed to directly on the wind field, leading to computational efficiency. Advantages and limitations of the method are discussed and are compared against (transform-based) envelope methods in a series of experiments.</jats:p>

Advection-based tracking and analysis of salinity movement in the Indian Ocean

“…Tracks of individual features may be extracted as paths within this time-varying graph. Several other approaches construct a track graph, a directed acyclic graph (DAG) consisting of all potential feature tracks (Bremer et al, 2010;Thomas and Natarajan, 2011;Widanagamaachchi et al, 2012;Doraiswamy et al, 2013;Valsangkar et al, 2019;Pandey et al, 2020;Lukasczyk et al, 2020). The track graph records the correspondences between features in consecutive time steps by considering the spatial proximity of the critical points that represent the features (Skraba and Wang, 2014;Soler et al, 2018), spatial overlap (Sohn and Bajaj, 2006;Saikia and Weinkauf, 2017a,b), or by identifying the matches between the subtrees of the contour trees or merge trees (Bremer et al, 2011;Oesterling et al, 2017;Sridharamurthy et al, 2020;Sridharamurthy and Natarajan, 2023).…”