@conference {928,
	title = {Detecting critical points in 2D scalar field ensembles using Bayesian inference},
	booktitle = {2022 IEEE 15th Pacific Visualization Symposium (PacificVis)},
	year = {2022},
	month = {April},
	abstract = {In an era of quickly growing data set sizes, information reduction methods such as extracting or highlighting characteristic features become more and more important for data analysis. For single scalar fields, topological methods can fill this role by extracting and relating critical points.  While such methods are regularly employed to study single scalar fields, it is less well studied how they can be extended to uncertain data, as produced, e.g., by ensemble simulations.

Motivated by our previous work on visualization in climate research, we study new methods to characterize critical points in ensembles of 2D scalar fields. Previous work on this topic either assumed or required specific distributions, did not account for uncertainty introduced by approximating the underlying latent distributions by a finite number of fields, or did not allow to answer all our domain experts{\textquoteright} questions. In this work, we use Bayesian inference to estimate the probability of critical points, either of the original ensemble or its bootstrapped mean. This does not make any assumptions on the underlying distribution and allows to estimate the sensitivity of the results to finite-sample approximations of the underlying distribution. We use color mapping to depict these probabilities and the stability of their estimation. The resulting images can, e.g., be used to estimate how precise the critical points of the mean-field are. We apply our method to synthetic data to validate its theoretical properties and compare it with other methods in this regard. We also apply our method to the data from our previous work, where it provides a more accurate answer to the domain experts{\textquoteright} research questions.},
	doi = {10.1109/PacificVis53943.2022.00009},
	author = {Vietinghoff, Dominik and B{\"o}ttinger, Michael and Scheuermann, Gerik and Heine, Christian}
}
@conference {908,
	title = {An Extension of Empirical Orthogonal Functions for the Analysis of Time-Dependent 2D Scalar Field Ensembles},
	booktitle = {2021 IEEE 14th Pacific Visualization Symposium (PacificVis)},
	year = {2021},
	month = {April},
	abstract = {To assess the reliability of weather forecasts and climate simulations, common practice is to generate large ensembles of numerical simulations. Analyzing such data is challenging and requires pattern and feature detection. For single time-dependent scalar fields, empirical orthogonal functions (EOFs) are a proven means to identify the main variation. In this paper, we present an extension of that concept to time-dependent ensemble data. We applied our methods to two ensemble data sets from climate research in order to investigate the North Atlantic Oscillation (NAO) and East Atlantic (EA) pattern.},
	doi = {10.1109/PacificVis52677.2021.00014},
	author = {Vietinghoff, Dominik and Heine, Christian and B{\"o}ttinger, Michael and Scheuermann, Gerik}
}
@article {929,
	title = {The Making of Continuous Colormaps},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {27},
	year = {2021},
	month = {June},
	pages = {3048-3063},
	abstract = {Continuous colormaps are integral parts of many visualization techniques, such as heat-maps, surface plots, and flow visualization. Despite that the critiques of rainbow colormaps have been around and well-acknowledged for three decades, rainbow colormaps are still widely used today. One reason behind the resilience of rainbow colormaps is the lack of tools for users to create a continuous colormap that encodes semantics specific to the application concerned. In this paper, we present a web-based software system, CCC-Tool (short for Charting Continuous Colormaps) under the URL https://ccctool.com, for creating, editing, and analyzing such application-specific colormaps. We introduce the notion of {\textquotedblleft}colormap specification (CMS){\textquotedblright} that maintains the essential semantics required for defining a color mapping scheme. We provide users with a set of advanced utilities for constructing CMS{\textquoteright}s with various levels of complexity, examining their quality attributes using different plots, and exporting them to external application software. We present two case studies, demonstrating that the CCC-Tool can help domain scientists as well as visualization experts in designing semantically-rich colormaps.},
	issn = {1941-0506},
	doi = {10.1109/TVCG.2019.2961674},
	author = {Nardini, Pascal and Chen, Min and Samsel, Francesca and Bujack, Roxana and B{\"o}ttinger, Michael and Scheuermann, Gerik}
}
@article {934,
	title = {A Testing Environment for Continuous Colormaps},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {27},
	year = {2021},
	month = {Feb},
	pages = {1043-1053},
	abstract = {Many computer science disciplines (e.g., combinatorial optimization, natural language processing, and information retrieval) use standard or established test suites for evaluating algorithms. In visualization, similar approaches have been adopted in some areas (e.g., volume visualization), while user testimonies and empirical studies have been the dominant means of evaluation in most other areas, such as designing colormaps. In this paper, we propose to establish a test suite for evaluating the design of colormaps. With such a suite, the users can observe the effects when different continuous colormaps are applied to planar scalar fields that may exhibit various characteristic features, such as jumps, local extrema, ridge or valley lines, different distributions of scalar values, different gradients, different signal frequencies, different levels of noise, and so on. The suite also includes an expansible collection of real-world data sets including the most popular data for colormap testing in the visualization literature. The test suite has been integrated into a web-based application for creating continuous colormaps (https://ccctool.com/), facilitating close inter-operation between design and evaluation processes. This new facility complements traditional evaluation methods such as user testimonies and empirical studies.},
	issn = {1941-0506},
	doi = {10.1109/TVCG.2020.3028955},
	author = {Nardini, Pascal and Chen, Min and Bujack, Roxana and B{\"o}ttinger, Michael and Scheuermann, Gerik}
}
@conference {907,
	title = {Visual Analysis of Spatio-Temporal Trends in Time-Dependent Ensemble Data Sets on the Example of the North Atlantic Oscillation},
	booktitle = {2021 IEEE 14th Pacific Visualization Symposium (PacificVis)},
	year = {2021},
	month = {April},
	abstract = {A driving factor of the winter weather in Western Europe is the North Atlantic Oscillation (NAO), manifested by fluctuations in the difference of sea level pressure between the Icelandic Low and the Azores High. Different methods have been developed that describe the strength of this oscillation, but they rely on certain assumptions, e.g., fixed positions of these two pressure systems. It is possible that climate change affects the mean location of both the Low and the High and thus the validity of these descriptive methods. This study is the first to visually analyze large ensemble climate change simulations (the MPI Grand Ensemble) to robustly assess shifts of the drivers of the NAO phenomenon using the uncertain northern hemispheric surface pressure fields. For this, we use a sliding window approach and compute empirical orthogonal functions (EOFs) for each window and ensemble member, then compare the uncertainty of local extrema in the results as well as their temporal evolution across different CO2 scenarios. We find systematic northeastward shifts in the location of the pressure systems that correlate with the simulated warming. Applying visualization techniques for this analysis was not straightforward; we reflect and give some lessons learned for the field of visualization.},
	doi = {10.1109/PacificVis52677.2021.00017},
	author = {Vietinghoff, Dominik and Heine, Christian and B{\"o}ttinger, Michael and Maher, Nicola and Jungclaus, Johann and Scheuermann, Gerik}
}
@article {932,
	title = {Fiber Surfaces for many Variables},
	journal = {Computer Graphics Forum},
	volume = {39},
	year = {2020},
	pages = {317-329},
	abstract = {Abstract Scientific visualization deals with increasingly complex data consisting of multiple fields. Typical disciplines generating multivariate data are fluid dynamics, structural mechanics, geology, bioengineering, and climate research. Quite often, scientists are interested in the relation between some of these variables. A popular visualization technique for a single scalar field is the extraction and rendering of isosurfaces. With this technique, the domain can be split into two parts, i.e. a volume with higher values and one with lower values than the selected isovalue. Fiber surfaces generalize this concept to two or three scalar variables up to now. This article extends the notion further to potentially any finite number of scalar fields. We generalize the fiber surface extraction algorithm of Raith et al. [RBN*19] from 3 to d dimensions and demonstrate the technique using two examples from geology and climate research. The first application concerns a generic model of a nuclear waste repository and the second one an atmospheric simulation over central Europe. Both require complex simulations which involve multiple physical processes. In both cases, the new extended fiber surfaces helps us finding regions of interest like the nuclear waste repository or the power supply of a storm due to their characteristic properties.},
	doi = {https://doi.org/10.1111/cgf.13983},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.13983},
	author = {Blecha, Christian and Raith, Felix and Pr{\"a}ger, Arne Jonas and Nagel, Thomas and Kolditz, Olaf and Ma{\ss}mann, Jobst and R{\"o}ber, Niklas and B{\"o}ttinger, Michael and Scheuermann, Gerik}
}
