Scientific Visualization for Atmospheric Data Analysis in Collaborative Virtual Environments

The three year European research project CROSS DRIVE (Collaborative Rover Operations and Planetary Science Analysis System based on Distributed Remote and Interactive Virtual Environments) started in January 2014. The research and development within this project is motivated by three use case studies: landing site characterization, atmospheric science and rover target selection. Currently the implementation for the second use case is in its final phase. Here, the requirements were generated based on the domain experts input and lead to development and integration of appropriate methods for visualization and analysis of atmospheric data. The methods range from volume rendering, interactive slicing, iso-surface techniques to interactive probing. All visualization methods are integrated in DLR’s Terrain Rendering application. With this, the high resolution surface data visualization can be enriched with additional methods appropriate for atmospheric data sets. This results in an integrated virtual environment where the scientist has the possibility to interactively explore his data sets directly within the correct context. The data sets include volumetric data of the martian atmosphere, precomputed two dimensional maps and vertical profiles. In most cases the surface data as well as the atmospheric data has global coverage and is of time dependent nature. Furthermore, all interaction is synchronized between different connected application  instances, allowing for collaborative sessions between distant experts

Cinema Darkroom: A Deferred Rendering Framework for Large-Scale Datasets

This paper presents a framework that fully leverages the advantages of a deferred rendering approach for the interactive visualization of large-scale datasets. Geometry buffers (G-Buffers) are generated and stored in situ, and shading is performed post hoc in an interactive image-based rendering front end. This decoupled framework has two major advantages. First, the G-Buffers only need to be computed and stored once---which corresponds to the most expensive part of the rendering pipeline. Second, the stored G-Buffers can later be consumed in an image-based rendering front end that enables users to interactively adjust various visualization parameters---such as the applied color map or the strength of ambient occlusion---where suitable choices are often not known a priori. This paper demonstrates the use of Cinema Darkroom on several real-world datasets, highlighting CD's ability to effectively decouple the complexity and size of the dataset from its visualization

Exploring the Invisible : FINDING STRUCTURE IN SCIENTIFIC DATA

In this thesis, I present contributions towards the aim of understanding flow-related scientific data sets by communicating relation, properties, and structure. The individual papers are contributions to three different areas. First, real-world visualization challenges with domain specific tasks. The individual applications are ranging from analyzing transport behavior in a centrifugal pump, to visualization of the impact of volcano eruptions and their atmospheric aftermath, and studying circulation dynamics and eddy movements in the ocean currents of the Red Sea. Although the three individual publications target different domains, they share common demands. Furthermore, the experience shows that combining and adapting different visualization techniques to support experts is essential for these scenarios. Second, technical visualization research with a strong focus on geometry-based, interactive, and explorative techniques. In this area a new type of particle system and a novel geometry-based flow visualization technique based on evolutionary algorithms are presented. With both approaches, areas of interest can be highlighted in a semi-automatic fashion by facilitating user-defined importance measures. Lastly, a method for decoupling definition and tracking of features. Here, the development of a fast but flexible method for defining and tracking cyclonic features in pressure fields using a solid and robust mathematical basis is presented. The initial theoretical work is discussed in context of its practical applications by pointing to relevant follow-up publications. The experience from real-world visualization tasks shows that understanding and gaining insight of scientific data with the help of visualization is an interactive, explorative, and non-linear process. Here, different methods must be combined and adapted such that they complement each other. Through this practice, relation, properties, and structure can be revealed, and a mental model can be created. From the real-world visualization challenges and the contributions in research, demands on techniques and their embedding in a visualization toolkit can be derived. Here, the ideal software is flexible, adaptable, and allows for interactive exploration. Furthermore, the process is benefiting from a semi-automatic approach guiding the domain expert during analysis. These aspects are used as guidelines for the implementation and development work associated with the contributions of this thesis and are presented in a dedicated Chapter.Med denna avhandling presenterar jag bidrag som syftar till att förstå flödesrelaterade vetenskapliga datamängder genom att kommunicera relationer, egenskaper och struktur. De enskilda artiklarna i avhandlingen bidrar med kunskap till tre olika områden. Det första bidraget berör verkliga visualiseringsutmaningar med domänspecifika uppgifter. De individuella tillämpningarna sträcker sig från att analysera transportbeteende i en centrifugalpump till visualisering av effekterna av vulkanutbrott och deras atmosfäriska efterverkningar, samt att studera cirkulationsdynamik och virvelrörelser i Röda havets havsströmmar. Även om de tre enskilda publikationerna riktar sig till olika domäner delar de gemensamma krav, och erfarenheten visar att kombination och anpassning av olika visualiseringstekniker för att stödja experter i deras arbete är avgörande för dessa scenarier. Det andra bidraget är undersökning av teknisk visualiseringsforskning med starkt fokus på geometribaserade, interaktiva och utforskande tekniker. Detta bidrag presenterar en ny typ av partikelsystem samt en grupp metoder baserade på evolutionära algoritmer. Med båda tillvägagångssätten kan områden av intresse lyftas fram på ett halvautomatiskt sätt genom att underlätta användardefinierade betydelseåtgärder. Det tredje bidraget är en metod för att frikoppla definition och spårning av funktioner. Detta bidrag presenterar utvecklingen av en snabb men flexibel metod för att definiera och spåra cyklonegenskaper i tryckfält med hjälp av en solid och robust matematisk grund. Det första teoretiska arbetet sätts också i praktisk tillämpning med en uppföljande publikation. Erfarenheterna från en verklig visualiseringsuppgift visar att förståelse och insikt i vetenskapliga data med hjälp av visualisering är en interaktiv, utforskande och icke-linjär process. Här måste olika metoder kombineras och anpassas så att de kompletterar varandra. Genom att göra så kan relationer, egenskaper och struktur i data avslöjas och en mental modell kan skapas. Utifrån detta kan sedan krav skapas, både krav på visualiseringsteknikerna och krav på hur visualiseringsverktyg ska användas. Programvaran som används bör då vara flexibel, anpassningsbar och möjliggöra interaktiv utforskning. Dessutom drar data-analysprocessen nytta av ett halvautomatiskt tillvägagångssätt som styr domänexporten under analysen. Dessa aspekter har använts som riktlinjer för genomförandet och utvecklingsarbetet i samband med bidragen i denna avhandling

Evolutionary Lines for Flow Visualization

In this work we explore evolutionary algorithms for selected a visualization application. We demonstrate its potential using an example from flow visualization showing promising first results. Evolutionary algorithms, as guided search approach, find close-to-optimal solutions with respect to some fitness function in an iterative process using biologically motivated mechanisms like selection, mutation and recombination. As such, they provide a powerful alternative to filtering methods commonly used in visualization where the space of possible candidates is densely sampled in a pre-processing step from which the best candidates are selected and visualized. This approach however tends to be increasingly inefficient with growing data size or expensive candidate computations resulting in large pre-processing times. We present an evolutionary algorithm for the problem of streamline selection to highlight features of interest in flow data. Our approach directly optimizes the solution candidates with respect to a user selected fitness function requiring significantly less computations. At the same time the problem of possible under-sampling is solved since we are not tied to a preset resolution. We demonstrate our approach on the well-known flow around an obstacle as case with a two-dimensional search space. The blood flow in an aneurysm serves as an example with a three-dimensional search space. For both, the achieved results are comparable to line filtering approaches with much less line computations

Topology-based Analysis for Multimodal Atmospheric Data of Volcano Eruptions

Many scientific applications deal with data from a multitude of different sources, e.g., measurements, imaging and simulations. Each source provides an additional perspective on the phenomenon of interest, but also comes with specific limitations, e.g. regarding accuracy, spatial and temporal availability. Effectively combining and analyzing such multimodal and partially incomplete data of limited accuracy in an integrated way is challenging. In this work, we outline an approach for an integrated analysis and visualization of the atmospheric impact of volcano eruptions. The data sets comprise observation and imaging data from satellites as well as results from numerical particle simulations. To analyze the clouds from the volcano eruption in the spatiotemporal domain we apply topological methods. Extremal structures reveal structures in the data that support clustering and comparison. We further discuss the robustness of those methods with respect to different properties of the data and different parameter setups. Finally we outline open challenges for the effective integrated visualization using topological methods

Concepts of Hybrid Data Rendering

We present a concept for interactive rendering of multiple data sets of varying type, including geometry and volumetric data, in one scene with correct transparency. Typical visualization applications involve multiple data fields from various sources. A thorough understanding of such data often requires combined rendering of theses fields. The choice of the visualization concepts, and thus the rendering techniques, depends on the context and type of the individual fields. Efficiently combining different techniques in one scene, however, is not always a straightforward task. We tackle this problem by using an A-buffer based approach to gather color and transparency information from different sources, combine them and generate the final output image. Thereby we put special emphasis on efficiency and low memory consumption to allow a smooth exploration of the data. Therefore, we compare different A-buffer implementations with respect to memory consumption and memory access pattern. Additionally we introduce an early-fragment-discarding heuristic using inter-frame information to speed up the rendering.

Atmospheric impact of volcano eruptions

The analysis of data that captures volcanic eruptions and their atmospheric aftermath plays an important role for domain experts to gain a deeper understanding of the volcanic eruption and their consequences for atmosphere, climate and air traffic. Thereby, one major challenge is to extract and combine the essential information, which is spread over various, mostly sparse data sources. This requires a careful integration of each data set with its strength and limitations. The sparse, but more reliable measurement data is mainly used to calibrate the more dense simulation data. This work combines a collection of visualization approaches into an exploitative framework. The goal is to support the domain experts to build a complete picture of the situation. But it is also important to understand the individual data sources, the wealth of their information and the quality of the simulation results. All presented methods are designed for direct interaction with the data from different perspectives rather than the sole generation of some final images

Scientific Visualization for Atmospheric Data Analysis in Collaborative Virtual Environments

The three year European research project CROSS DRIVE (Collaborative Rover Operations and Planetary Science Analysis System based on Distributed Remote and Interactive Virtual Environments) started in January 2014. The research and development within this project is motivated by three use case studies: landing site characterization, atmospheric science and rover target selection. Currently the implementation for the second use case is in its final phase. Here, the requirements were generated based on the domain experts input and lead to development and integration of appropriate methods for visualization and analysis of atmospheric data. The methods range from volume rendering, interactive slicing, iso-surface techniques to interactive probing. All visualization methods are integrated in DLR’s Terrain Rendering application. With this, the high resolution surface data visualization can be enriched with additional methods appropriate for atmospheric data sets. This results in an integrated virtual environment where the scientist has the possibility to interactively explore his data sets directly within the correct context. The data sets include volumetric data of the martian atmosphere, precomputed two dimensional maps and vertical profiles. In most cases the surface data as well as the atmospheric data has global coverage and is of time dependent nature. Furthermore, all interaction is synchronized between different connected application  instances, allowing for collaborative sessions between distant experts

Scientific Visualization for Atmospheric Data Analysis in Collaborative Virtual Environments

The three year European research project CROSS DRIVE (Collaborative Rover Operations and Planetary Science Analysis System based on Distributed Remote and Interactive Virtual Environments) started in January 2014. The research and development within this project is motivated by three use case studies: landing site characterization, atmospheric science and rover target selection. Currently the implementation for the second use case is in its final phase. Here, the requirements were generated based on the domain experts input and lead to development and integration of appropriate methods for visualization and analysis of atmospheric data. The methods range from volume rendering, interactive slicing, iso-surface techniques to interactive probing. All visualization methods are integrated in DLR’s Terrain Rendering application. With this, the high resolution surface data visualization can be enriched with additional methods appropriate for atmospheric data sets. This results in an integrated virtual environment where the scientist has the possibility to interactively explore his data sets directly within the correct context. The data sets include volumetric data of the martian atmosphere, precomputed two dimensional maps and vertical profiles. In most cases the surface data as well as the atmospheric data has global coverage and is of time dependent nature. Furthermore, all interaction is synchronized between different connected application  instances, allowing for collaborative sessions between distant experts

CROSS DRIVE: A Collaborative Virtual Reality Workplace for Space Science Data Exploitation and Rover Operations Engineering

The CrossDrive Project develops Distributed and Collaborative Infrastructure based on advanced Immersive Virtual Reality tools for the analysis and management of Scientific Data and Operational Activities of planetary spacecraft. The Collaborative Workspace encompasses advanced technological solutions for central storage processing, 3D visualisation and Virtual Presence in Immersive Virtual Reality environments, to support Space Data Analysis and Space Operations. Science objectives are share and correlate Atmospheric data, analysis and simulations based on the actual main Mars’ satellites (MEX and MRO); compare and correlate data for Geology and Geodesy; benchmark satellite and ground based Mars atmospheric measurements