expert is needed to work with it. The approach could reach a larger user group, if the workflow were supported by a software wizard that enables users to use it without previous knowledge. This task is an issue for future work (see section 5.2).
Although the workflow has been developed for meteorological data, the employed techniques and software can be applied for scientific data from other domains, too (see section 5.2). As several scientific fields face the same problems of growing data size, heterogeneity, and complexity, this workflow could be helpful for generating meaningful visualizations of other data.
5.2
Future work
The workflow developed in this thesis can be applied for case studies from other domains, because the representations of the underlying data are identical. One example from the field of hydrology is the comparison of multiple groundwater flow simulations and visually added observation data from wells and boreholes. Another application example is the domain of geothermal energy to compare various consumption scenarios.
To support collaborative work, the visualization application could be extended by a multiplayer mode. This allows users at different locations to examine the same visualization and react on the others interactions. Usage scenarios could be project group meetings over the internet, online lectures for students, and online presentations of researchers.
Extension of interaction functionality is another field for future work. For example, the amount of additional data can be extended by adding metadata information to the 3D objects and providing statics about the distribution of values. In addition, analysis tools could be provided by the GUI, e.g. to enable the user to measure distances of objects and differences of values.
A software wizard for the workflow is one possibility to make the workflow applicable also for users that are not very experienced with data in- tegration and visualization. An approach is to provide a framework for those users that supports them during the steps of data integration, representa- tion and abstraction, and presentation and interaction. The data integration could be supported by providing a conjunction of software applications that can be operated by a basic GUI. Thereby the automated conversion from input data to a data format usable in the visualization software could be real- ized. During the workflow step representation and abstraction, the user can be supported by recommending representations, data reduction methods, and encoding methods for the values according to the data characteristic.
Afterwards, an semi-automated setup of the visualization application based on the representations and their metadata could be performed.
Important achievements
The most essential achievements of this thesis are listed in the following:
1. A concept for generating 3D visualizations for meteorological data was developed that employs abstraction methods and creates distinctive representations for multiple variables. The concept has been developed with respect to the nature of the data and is based on the results of the work with experts of meteorology as well as conventions and guidelines.
2. A workflow was developed as the basis for generating meaningful 3D visualizations of meteorological data. The data base consists of het- erogeneous, complex, and large data sets from various sources. The workflow includes methods for data integration, abstraction and rep- resentation, as well as presentation and interaction.
3. With the combination of existing visualization and presentation ap- plications and the implementation of extensions for these, a broad range of existing algorithms could be used for the development of the visualization scenes. This is in contrast to existing approaches, which mostly use stand-alone solutions and provide only a fraction of these features.
4. Various visualization methods for multifaceted data have been pro- posed and implemented including methods for temporal data (static and dynamic displaying), multimodal data (calculated differences, overlaid view), multirun data (side-by-side view, overlaid view with outlines, combination of side-by-side and outline view), and multivari- ate data (data representations according to their nature, encoding the values).
vironment, 3D projector, and head mounted display. The stereoscopic impression that these devices generate is necessary for an effective ana- lysis of multifaceted data. Besides the presentation with 3D devices, the visualization scenes are also presentable with a common PC to make them accessible by a broad user group.
6. A case study focusing on climate data was conducted that explored the possibilities of integrated visualization of various environmental data (e.g., hydrology, ecology) by using the OpenGeoSys Data Explorer framework.
7. A combined visualization of observation and simulation data was done in another case study that focused on weather data of different areas and various scales. Phenomena such as convection and the distribu- tion of heat fluxes have been analyzed with the help of the generated visualizations.
8. An application (MEVA – Multifaceted Environmental Data Visualiz- ation Application) was developed as part of a case study and is based on a game engine. It implements visualization methods for multifa- ceted data as well as a GUI for interacting with the visualization scene including controls for time, camera, and 3D objects.
List of Figures
2.1 The visualization pipeline [60] describes the process from raw data to rendered image. . . 7 2.2 Categorization of the techniques used to visualize multifa-
ceted data after Kehrer and Hauser [71]. . . 9 2.3 Screenshot of the OpenGeoSys Data Explorer that aims at
supporting users setting up their models and visualizing sim- ulation results. . . 13 2.4 Error detection with OGS Data Explorer: borehole data
(bars) are visually compared with surface data to detect in- correct values. One borehole sticks out of the surface very high, which is a sign for an incorrect value [114]. . . 14 2.5 High immersion virtual reality: CAVE with a surround pro-
jection [152]. . . 15 2.6 Scheme of the setup of the TESSIN VISLab at the UFZ [162]. 16 2.7 The climate system is complex and includes the interaction
of several components such as atmosphere, vegetation, land surface, ocean, and ice [117]. . . 17 2.8 The heat fluxes on the surface consist of latent, sensible, and
ground heat flux. . . 19 2.9 The convection process is driven by density differences and
leads to air mass circulation [2]. . . 20 2.10 A Supercell has an asymetric structure and is an extremely
powerful cell [2]. . . 21
3.1 The proposed workflow consisting of data integration, rep- resentation and abstraction, as well as presentation and in- teraction with included tasks and used software (cf. [51]). These steps are analogous to the visualization pipeline shown in figure 2.1. . . 26 3.2 Iterative development process. . . 26 3.3 From input data to target data type: using various software
applications to transform the data. . . 28 3.4 Import netCDF file as mesh using intensity as material (left
hand side) and import it as mesh using intensity as elevation (right hand side). . . 29 3.5 Shape file of a river with added altitude on a DEM (using the
OGS Data Explorer). . . 30 3.6 The scheme for the workflow step representation and abstrac-
tion shows the multiple attributes and their categories. . . 31 3.7 Visualizing time via stacking disks: each disk represents the
difference of simulated and observed 30-year average temper- ature of one month. The bigger semi-transparent disk around it shows the 30-year average temperature difference of a year. 34 3.8 Visualizing time via animation: for multifaceted data the dis-
play of multiple time steps is done sequentially. . . 34 3.9 For the region of Germany, differences between simulation
and observation data are calculated and displayed as bars. The texture represents the DEM. . . 35 3.10 Differences of the observation data and simulations of two
different climate models (REMO and CLM) are calculated and displayed as bars. The thin bars represent the differences between REMO simulation and observation, and the thicker semi-transparent bars represent the differences between CLM simulation and observation. . . 36 3.11 Overlaid view of simulation (clouds: semi-transparent white
isovolumes) and observation data (precipitation amount at a weather station: spheres). . . 36 3.12 Overlaid view of static data from different sources: DEM,
river (tubes), and cities (boxes). . . 37 3.13 Side-by-side view of multirun data (streamlines of wind and
mass fraction of clouds, rain, graupel, ice, and snow). On the left hand side simulation data with a resolution of 3 km, and on the right hand side simulation data with a resolution of 1 km. . . 38
List of Figures 75
3.14 Overlaid view showing mass fraction of clouds: on the left hand side without outlines, and on the right hand side with outlines . . . 39 3.15 Overlaid view using transparency and showing mass fraction
of clouds: on the left hand side without outlines, and on the right hand side with outlines that overlap in some regions. . . 39 3.16 Overlaid view with outlines for the special case of streamlines.
From left to right: streamlines of simulation run with 3 km (blue), added streamlines of simulation run with 1 km (green), and added streamlines of simulation run with 333m (red). . . 40 3.17 Combination of side-by-side and outline view: on the left
hand side, the clouds of the 3 km run are shown and out- lines of the clouds of the 1 km run. On the right hand side, it is inversely. . . 41 3.18 Multivariate data with the variables mass fraction of clouds
(white isovolume), rain (cyan isovolume), wind (streamlines), humidity (slices), and sensible (yellow arrows), latent (blue arrows), and ground (orange arrows) heat flux. . . 41 3.19 Encoding values by color: surface temperature and temper-
ature of the atmosphere (mapped on streamlines) using the same color scale, but different color ranges. . . 43 3.20 Encoding value by scaling: the size of the arrow represents
the value of a variable at a certain position. . . 43 3.21 Encoding values by opacity: isosurfaces with different opacity
represent multiple percentage of air humidity. . . 44 3.22 Categorization of variables and their representations. . . 45 3.23 The appropriate representation for line data are tubes. The
example shows a line and a tube presentation for a river on the left hand side and for streamlines on the right hand side. 46 3.24 Glyphs as representative for (from left to right): point data
(cities, observation data), 2D scalar data (heat fluxes), and 3D vector data (vertical wind profile). . . 46 3.25 Raster data (land use) mapped on a DEM using the OGS
Data Explorer. . . 47 3.26 Isolines that connect equal values are added to slices to high-
light boundary values, in this case for temperature of the atmosphere. . . 47 3.27 Slices for representing 3D scalar data: the clipping planes can
be positioned along any layer. . . 48 3.28 ParaView filter pipeline for streamlines. . . 49
3.29 Streamlines representing 3D vector data: using a large num- ber of streamlines creates a complete image of the wind in a certain area. Varying the opacity leads to a clear visualization in which areas with high density are easily recognizable. . . . 49 3.30 Combing representations: streamlines (the color represents
the temperature of the atmosphere) and glyphs (showing wind direction and the color represents the magnitude of the wind). 50 3.31 Presentation software and their correlation with applicable
interaction setups and supported visualization devices. . . 51 3.32 Using a 3D projector to present the visualization provides the
freedom of mobility. (Photo: Lars Bilke) . . . 52 3.33 Using a head mounted display (HMD) to present the visual-
ization at conferences. (Photo: Lars Bilke) . . . 52 3.34 The visualization presentation software VRED has a very
complex and unintuitive GUI that is hard to handle. . . 55 3.35 The interaction functionality provided by MEVA [52] (from
left to right): controls for objects, time, and camera. . . 55 3.36 PDF3D integrates 3D objects in a PDF file and supports
control functions for time, camera, and objects. . . 56 3.37 Discussing the visualization with visualization experts (left
hand side) and presenting it to students (right hand side). (Photos: Karsten Rink, Lars Bilke) . . . 57
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