Visualization of 40 Years of Tropical Cyclone Positions and Their Rainfall

Correos de investigadores: [email protected] || [email protected] || [email protected] || [email protected] article focuses on a visualization of tropical cyclone track data occurring over a 40- year period (1970–2010) and their relationship with (extremely) heavy rainfall reported by 88 Central American weather stations. The purpose of the visualization is to associate the paths of tropical cyclones in oceanic areas with heavy rainfall inland. Thus, the potential for producing a set of rainfall patterns might somehow help in predicting where different impacts like flooding might occur when tropical cyclones develop in specific oceanic regions. The visualization will serve as a key tool for CIGEFI scientists to apply in their work to determine critical positions of the tropical cyclones associated with extremely heavy rainfall events at daily timescales.Universidad de Costa Rica/[805-B9-454]/UCR/Costa RicaUniversidad de Costa Rica/[805-C0-610]/UCR/Costa RicaUniversidad de Costa Rica/[EC-497]/UCR/Costa RicaUniversidad de Costa Rica/[805-A4-906]/UCR/Costa RicaUniversidad de Costa Rica/[805-C0-074]/UCR/Costa RicaUniversidad de Costa Rica/[805-A1-715]/UCR/Costa RicaUniversidad de Costa Rica/[805-B0-810]/UCR/Costa RicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI)UCR::Vicerrectoría de Docencia::Ciencias Básicas::Facultad de Ciencias::Escuela de FísicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigación en Ciencias del Mar y Limnología (CIMAR

3D simulation in flooding Providence

This thesis use 3d data visualization to provide the scenarios of how global climate changes will influence people’s life if we don’t take actions as soon as possible, which provide non-professional people an easy way to understand the urban issues and engage them into the environmental protection. My proposal is to visualize the flooding issues in Providence by using kinds of simulation tools, including 3d model, augmented reality(AR), animation in order arise the awareness of climate change and the significance of human’s actions to protect the living environments. These simulations also provide the support for the designers and policy-makers to adjust management strategies when they are thinking about the long-term urban planning

Submerse: Visualizing Storm Surge Flooding Simulations in Immersive Display Ecologies

We present Submerse, an end-to-end framework for visualizing flooding scenarios on large and immersive display ecologies. Specifically, we reconstruct a surface mesh from input flood simulation data and generate a to-scale 3D virtual scene by incorporating geographical data such as terrain, textures, buildings, and additional scene objects. To optimize computation and memory performance for large simulation datasets, we discretize the data on an adaptive grid using dynamic quadtrees and support level-of-detail based rendering. Moreover, to provide a perception of flooding direction for a time instance, we animate the surface mesh by synthesizing water waves. As interaction is key for effective decision-making and analysis, we introduce two novel techniques for flood visualization in immersive systems: (1) an automatic scene-navigation method using optimal camera viewpoints generated for marked points-of-interest based on the display layout, and (2) an AR-based focus+context technique using an auxiliary display system. Submerse is developed in collaboration between computer scientists and atmospheric scientists. We evaluate the effectiveness of our system and application by conducting workshops with emergency managers, domain experts, and concerned stakeholders in the Stony Brook Reality Deck, an immersive gigapixel facility, to visualize a superstorm flooding scenario in New York City

Abstract visualization of large-scale time-varying data

The explosion of large-scale time-varying datasets has created critical challenges for scientists to study and digest. One core problem for visualization is to develop effective approaches that can be used to study various data features and temporal relationships among large-scale time-varying datasets. In this dissertation, we first present two abstract visualization approaches to visualizing and analyzing time-varying datasets. The first approach visualizes time-varying datasets with succinct lines to represent temporal relationships of the datasets. A time line visualizes time steps as points and temporal sequence as a line. They are generated by sampling the distributions of virtual words across time to study temporal features. The key idea of time line is to encode various data properties with virtual words. We apply virtual words to characterize feature points and use their distribution statistics to measure temporal relationships. The second approach is ensemble visualization, which provides a highly abstract platform for visualizing an ensemble of datasets. Both approaches can be used for exploration, analysis, and demonstration purposes. The second component of this dissertation is an animated visualization approach to study dramatic temporal changes. Animation has been widely used to show trends, dynamic features and transitions in scientific simulations, while animated visualization is new. We present an automatic animation generation approach that simulates the composition and transition of storytelling techniques and synthesizes animations to describe various event features. We also extend the concept of animated visualization to non-traditional time-varying datasets--network protocols--for visualizing key information in abstract sequences. We have evaluated the effectiveness of our animated visualization with a formal user study and demonstrated the advantages of animated visualization for studying time-varying datasets

Visualizing the Impacts of Extreme Weather Events Using 3D Visualization to Aid Pre-Event Risk Communication

Weather is everywhere. Rapidly expanding capabilities in information technology present an opportunity to develop better warning products for extreme weather events. In the wake of recent extreme events from Superstorm Sandy to Hurricane Matthew, the National Oceanic and Atmospheric Administration and the National Weather Service have repeated the call for improving techniques for disseminating critical information to the general public during potential weather disasters. This research uses 3D GIS and geovisualization to improve the communication of expert risk information to the general public

Intelligent Data Analytics using Deep Learning for Data Science

Nowadays, data science stimulates the interest of academics and practitioners because it can assist in the extraction of significant insights from massive amounts of data. From the years 2018 through 2025, the Global Datasphere is expected to rise from 33 Zettabytes to 175 Zettabytes, according to the International Data Corporation. This dissertation proposes an intelligent data analytics framework that uses deep learning to tackle several difficulties when implementing a data science application. These difficulties include dealing with high inter-class similarity, the availability and quality of hand-labeled data, and designing a feasible approach for modeling significant correlations in features gathered from various data sources. The proposed intelligent data analytics framework employs a novel strategy for improving data representation learning by incorporating supplemental data from various sources and structures. First, the research presents a multi-source fusion approach that utilizes confident learning techniques to improve the data quality from many noisy sources. Meta-learning methods based on advanced techniques such as the mixture of experts and differential evolution combine the predictive capacity of individual learners with a gating mechanism, ensuring that only the most trustworthy features or predictions are integrated to train the model. Then, a Multi-Level Convolutional Fusion is presented to train a model on the correspondence between local-global deep feature interactions to identify easily confused samples of different classes. The convolutional fusion is further enhanced with the power of Graph Transformers, aggregating the relevant neighboring features in graph-based input data structures and achieving state-of-the-art performance on a large-scale building damage dataset. Finally, weakly-supervised strategies, noise regularization, and label propagation are proposed to train a model on sparse input labeled data, ensuring the model\u27s robustness to errors and supporting the automatic expansion of the training set. The suggested approaches outperformed competing strategies in effectively training a model on a large-scale dataset of 500k photos, with just about 7% of the images annotated by a human. The proposed framework\u27s capabilities have benefited various data science applications, including fluid dynamics, geometric morphometrics, building damage classification from satellite pictures, disaster scene description, and storm-surge visualization

Drones, Virtual Reality, and Modeling: Communicating Catastrophic Dam Failure

Dam failures occur worldwide and can be economically and ecologically devastating. Communicating the scale of these risks to the general public and decision-makers is imperative. Two-dimensional (2D) dam failure hydraulic models inform owners and floodplain managers of flood regimes but have limitations when shared with non-specialists. This study addresses these limitations by constructing a 3D Virtual Reality (VR) environment to display the 1976 Teton Dam disaster case study using a pipeline composed of (1) 2D hydraulic model data (extrapolated into 3D), (2) a 3D reconstructed dam, and (3) a terrain model processed from UAS (Uncrewed Airborne System) imagery using Structure from Motion photogrammetry. This study validates the VR environment pipeline on the Oculus Quest 2 VR Headset with the criteria: immersion fidelity, movement, immersive soundscape, and agreement with historical observations and terrain. Through this VR environment, we develop an effective method to share historical events and, with future work, improve hazard awareness; applications of this method could improve citizen engagement with Early Warning Systems. This paper establishes a pipeline to produce a visualization tool for merging UAS imagery, Virtual Reality, digital scene creation, and sophisticated 2D hydraulic models to communicate catastrophic flooding events from natural or human-made levees or dams

Coastal population vulnerability to sea level rise and tropical cyclone intensification under global warming

This research focuses on developing a framework for first order estimates of locations at risk to Tropical Cyclones (TC) and elevated water levels in coastal regions. The International Best Track Archive for Climate Stewardship (IBTrACS) 64 knot wind radii data identifies locations in the North Atlantic (NA) basin hit by hurricane strength storms. Geographic Information System (GIS) temporal and spatial analysis of IBTrACS data identifies impact zone locations where multiple TCs have occurred. Aster 30 m elevation data identifies locations within 5 and 10 m of sea level that may become inundated by TC storm surges. Population density and land cover data maps are created to identify urban and food production areas. Overlay maps are created of the coastal inundations, population, land cover, and hurricane track impact zones. Mapping results show the Bahamas and Cuba are most susceptible to the effects of tropical cyclone and storm surge inundation