Visualizing Hurricane Katrina

Computer graphics and interactive techniques in Austalasia and South East Asia, 2006

Hurricane Katrina has had a devastating impact on the US Gulf Coast. Forecasts of such events, coupled with timely response, can greatly reduce casualties and save billions of dollars. In our effort, we use sophisticated surface, flow and volume visu- alization techniques show the storm surge and atmospheric simula- tions simulation results superimposed with actual observations, in- cluding satellite cloud

Proceedings of the …, 2006

Hurricane Katrina has had a devastating impact on the US Gulf Coast, and her effects will be felt for many years. Forecasts of such events, coupled with timely response, can greatly reduce casualties and save billions of dollars. We show how visualizations from storm surge and atmospheric simulations, were used to understand the predictions of how strong, where, and when flooding would occur in the hours leading up to Katrina's landfall. Sophisticated surface, flow and volume visualization techniques show these simulation results interleaved with actual observations, including satellite cloud images, GIS aerial maps and LIDAR showing the 3D terrain of New Orleans. The sheer size and complexity of the data in this application also motivated research in efficient data access mechanisms and rendering algorithms. Our goals were to use the resulting animation as a vehicle for raising awareness in the general populace to the true impact of the event, to create a scientifically accurate representation of the storm and its effects, and to develop a workflow to create similar visualizations for future and simulated hurricanes. Screenings of the animation have been well received, both by the general public and by scientists in the field.

IEEE Computer Graphics and Applications, 2006

H urricanes are the most devastating natural hazard affecting the populated US East and Gulf coasts. One of the major threats associated with hurricanes is storm surge. The storm surge can damage buildings and infrastructure, block escape routes, and drown people by flooding low-lying coastal areas. More than 8,000 people were killed largely from storm-surge flooding when a hurricane made landfall at Galveston, Texas, in 1900. The death toll in the US from storm-surge hazards has declined drastically over the last several decades because of a low hurricane activity and implementation of timely evacuation from storm-surge zones. The evacuation zones are determined through a combination of near-shore elevations and storm surge predicted by the numerical model Sea, Lake, Overland Surges from Hurricanes (Slosh) 1 developed by the National Oceanic and Atmospheric Administration (NOAA). As coastal populations grow and decades of increased hurricane activity occur, 2 the risk of drowning thousands of people living in low-lying areas, such as South Florida and New Orleans, Louisiana, increases as demonstrated by Hurricanes Katrina and Rita in 2005. Several hundred people were killed along the Mississippi coast by the storm-surge flooding induced by Katrina.

2013

Visualising large-scale geospatial data is a demanding challenge that finds applications in many fields, including climatology and hydrology. Due to the enormous data size, it is currently not possible to render full datasets interactively without significantly compromising quality (especially not when information changes over time). In this paper, we present new approaches to render and interact with detail-varying Light Detection and Range (LiDAR) point sets. Furthermore, our approach allows the attachment of large-scale geospatial meta information and the modification of point attributes on the fly. The core of our algorithm is a dynamic GPUbased hierarchical tree data structure that is used in conjunction with an out-of-core, Levelof-Detail (LoD)-Point-based Rendering (PBR) algorithm to modify data on the fly. This combination makes it possible to augment the original data with dynamic context information that can be used to highlight features (e.g., routes, marked areas) or to reshape the entire data set in real-time. We showcase the usefulness of our algorithm in the context of disaster management and illustrate how decision makers can discuss a flood scenario covering a large area (spanning 300 km 2) and discuss hazards, as well as related protection measures, interactively. One of our presented reference point sets includes parts of the AHN2 data set (14 TB of LiDAR data in total). Previous rendering algorithms relied on a long offline preprocessing (several hours) to ensure a quick data display. This step made any changes to the data impossible. With our new approach, we can modify point sets without requiring a new preprocessing run.

Approaches to Disaster Management - Examining the Implications of Hazards, Emergencies and Disasters, 2013

2007

Three-dimensional visualization of GIS data is emerging as a powerful tool through which to better understand severe weather events such as tropical storms and hurricanes. The integration of scientific data, satellite imagery, and GIS data in a 3D virtual environment provides scientists with a unique opportunity to explore hurricanes in a detailed and comprehensive way. Currently, several initiatives are utilizing recent advances in computing technology to create 3D simulations of tropical storm events. Their efforts promise not only to improve the understanding of the science of storms but to advance the effective visualization of geographic data. Both are central to supporting risk management mitigation and reducing the huge economic costs associated with tropical cyclones and hurricanes. Severe Tropical Cyclone Larry hit the north east coast of Australia in March, 2006, and took its place as one of the costliest natural disasters in Queensland’s history [9]. Larry presents an exc...

Urban informatics, 2024

As climate change intensifies, resulting in more severe rainfall events, coastal cities globally are witnessing significant life and property losses. A growingly crucial component for flood prevention and relief are urban storm flood simulations, which aid in informed decision-making for emergency management. The vastness of data and the intricacies of 3D computations can make visualizing the urban flood effects on infrastructure daunting. This study offers a 3D visualization of the repercussions of hurricane storm surge flooding on Galveston, TX residences, illustrating the impact on each structure and road across varied storm conditions. We employ target detection to pinpoint house door locations, using door inundation as a metric to gauge potential flood damage. Within a GIS-based framework, we model the damage scope for residences exposed to varying storm intensities. Our research achieves three core goals: 1) Estimating the storm inundation levels on homes across different storm conditions; 2) Assessing first-floor elevations to categorize housing damages into three distinct groups; and 3) Through visualization, showcasing the efficacy of a proposed dike designed to shield Galveston Island from future storm surge and flood events.

The large and ever-increasing amounts of multidimensional, time-varying and geospatial digital information from multiple sources represent a major challenge for today's analysts. We present a set of visualization techniques that can be used for the interactive analysis of geo-referenced and time sampled data sets, providing an integrated mechanism and that aids the user to collaboratively explore, present and communicate visually complex and dynamic data. Here we present these concepts in the context of a 4 hour flood scenario from Lisbon in 2010, with data that includes measures of water column (flood height) every 10 minutes at a 4.5 m x 4.5 m resolution, topography, building damage, building information, and online base maps. Techniques we use include web-based linked views, multiple charts, map layers and storytelling. We explain two of these in more detail that are not currently in common use for visualization of data: storytelling and web-based linked views. Visual storyte...

2007

Visualization of large realistic forests, various countries, floods, fire spreads and landslides is very difficult and requires using of perfect modern graphic methods which allow rendering them as scenes in real time. This paper describes the creation of 3D visualization tool for visualization of natural disasters. A lot of international projects oriented on natural disasters utilise grid computing and within grid solution raises requirement of visualization service for presentation of the results. Such service requires unified standards like integration of input data formats and especially creation of unified visualization tool. It should integrate visualization requests of any kind of application oriented on computing of natural disasters. In case it is grid computing it has to be established also submit workflow, which controls execution of this visualization service. Development of all executable modules and solution of all above mentioned grid computing specific problems was subject of our scientific work presented in this article. The 3D visualization tool as well as submit workflow were tested on the applications solved in project MEDIGRID and on natural disasters applications solved in our institute [12].

2018

Climate change has serious implications on our environment. Examples of such natural risks are massive rainfalls and the rise of ocean levels. Millions of people are exposed to the risk of extreme floods and storms. It is therefore crucial to develop analytical tools that allow us to evaluate the threats of floods and to investigate the influence of mitigation and adaptation measures, such as stronger dikes, adaptive spatial planning, and flood disaster plans. The objective of our work is to present a flood management system that aims to model and visualize floods. It provides realistic images to help users in understanding and interpreting these disaster scenarios. In order to investigate the applicability in practice, we illustrated the use of our system for real-world data in a case study for the city of Paris, France.