Polar Cyclone Identification from 4D Climate Data in a Knowledge-Driven Visualization System

Abstract

Abstract:Arctic cyclone activity has a significant association with Arctic warming and Arctic ice decline. Cyclones in the North Pole are more complex and less developed than those in tropical regions. Identifying polar cyclones proves to be a task of greater complexity. To tackle this challenge, a new method which utilizes pressure level data and velocity field is proposed to improve the identification accuracy. In addition, the dynamic, simulative cyclone visualized with a 4D (four-dimensional) wind field further validated the identification result. A knowledge-driven system is eventually constructed for visualizing and analyzing an atmospheric phenomenon (cyclone) in the North Pole. The cyclone is simulated with WebGL on in a web environment using particle tracing. To achieve interactive frame rates, the graphics processing unit (GPU) is used to accelerate the process of particle advection. It is concluded with the experimental results that: (1) the cyclone identification accuracy of the proposed method is 95.6% when compared with the NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data; (2) the integrated knowledge-driven visualization system allows for streaming and rendering of millions of particles with an interactive frame rate to support knowledge discovery in the complex climate system of the Arctic region.

Web-Scale Multidimensional Visualization of Big Spatial Data to Support Earth Sciences—A Case Study with Visualizing Climate Simulation Data

“…HurricaneVis [28] is a desktop visualization platform that focuses on scalar data from numerical weather model simulations of tropical cyclones. Leveraging the power of graphics cards, multivariate real-time 4D visualization can also be achieved [29]. These works demonstrate a great advantage in data visualization over traditional approaches that rely solely on 2D maps and scatter plots.…”

“…For instance, Fetchclimate [30] and the USGS (United States Geological Survey) National Climate Change Viewer (UCCV) [31] provide solutions for environment information retrieval and mapping. Similar online visualization applications have also been applied to other geoscience disciplines such as hydrology [32][33][34], oceanography [35], and polar [20,29], etc. Open source packages, such as Cesium [36] or NASA's new WebWorldWind [37], are also exploited to construct web-based environmental applications [38,39].…”

Association Rules-Based Multivariate Analysis and Visualization of Spatiotemporal Climate Data

“…Because Arctic cyclones significantly influence the warming and melting of the Arctic icecap [29], we illustrate the proposed visualization system with a polar cyclone and select the four variables most relevant to the formation and intensification of the cyclone to study the intervariable association: (1) air temperature (T), the temperature difference creates a pressure imbalance, which is the driving force for the formation of cyclone; (2) wind speed (S), the wind speed indicates the strength of the cyclone; (3) geopotential height (G), can be used for locating troughs and ridges; and (4) atmospheric water vapor (V), which releases heat when condensing in the atmosphere that helps to fuel the cyclone. Key techniques that support this knowledge-driven visualization pipeline are described in the following sections.…”

“…Although several previous studies have been conducted on vector data visualization, the pursuit of effective representation of time-varying vector flow remains an active research topic [32,33]. We select two visualization strategies, streamlines and volume rendering, to simulate the wind field data because streamlines yield the pattern of a cyclone, and volume rendering supports the internal data exploration in comparison to other existing techniques, such as particle tracking [29] and line integral convolution [34]. We accelerate the two approaches with GPUs to achieve better performance.…”

Experimental and Numerical Investigation of Optimized Blade Tip Shapes: Part II — Tip Flow Analysis and Loss Mechanisms

“…Copyright © 2018 ASME boundary layer interaction [29] or polar cyclone eye identification from climate data [32]. However, this method has not been previously used on turbomachinery flows and we deemed necessary to test its vortex-tracking performance against a well-known, widely used method: the λ 2 criterion.…”