Information Visualization in Climate Research

Tominski, Christian; Donges, Jonathan F.; Nocke, Thomas

doi:10.1109/iv.2011.12

Cited by 33 publications

(27 citation statements)

References 23 publications

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“…Thereby, we want to narrow the gap between state-of-the-art methods of interactive visual analysis and their application in Earth system modeling. This general gap still exists, as it was recently shown in a survey by Tominski et al [33] among 76 scientists from the closely related field of climate research. Nevertheless, the last years have shown increased efforts in bridging the gap by developing tailored visual analysis methods that consider domain-specific challenges.…”

Section: Vr1 Exploration Of Goodness Of Fit In Model Parameter Spacementioning

confidence: 97%

A Visual Analysis Concept for the Validation of Geoscientific Simulation Models

Unger¹,

Schulte²,

Klemann³

et al. 2012

IEEE Trans. Visual. Comput. Graphics

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Geoscientific modeling and simulation helps to improve our understanding of the complex Earth system. During the modeling process, validation of the geoscientific model is an essential step. In validation, it is determined whether the model output shows sufficient agreement with observation data. Measures for this agreement are called goodness of fit. In the geosciences, analyzing the goodness of fit is challenging due to its manifold dependencies: 1) The goodness of fit depends on the model parameterization, whose precise values are not known. 2) The goodness of fit varies in space and time due to the spatio-temporal dimension of geoscientific models. 3) The significance of the goodness of fit is affected by resolution and preciseness of available observational data. 4) The correlation between goodness of fit and underlying modeled and observed values is ambiguous. In this paper, we introduce a visual analysis concept that targets these challenges in the validation of geoscientific models - specifically focusing on applications where observation data is sparse, unevenly distributed in space and time, and imprecise, which hinders a rigorous analytical approach. Our concept, developed in close cooperation with Earth system modelers, addresses the four challenges by four tailored visualization components. The tight linking of these components supports a twofold interactive drill-down in model parameter space and in the set of data samples, which facilitates the exploration of the numerous dependencies of the goodness of fit. We exemplify our visualization concept for geoscientific modeling of glacial isostatic adjustments in the last 100,000 years, validated against sea levels indicators - a prominent example for sparse and imprecise observation data. An initial use case and feedback from Earth system modelers indicate that our visualization concept is a valuable complement to the range of validation methods.

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Section: Vr1 Exploration Of Goodness Of Fit In Model Parameter Spacementioning

confidence: 97%

A Visual Analysis Concept for the Validation of Geoscientific Simulation Models

Unger¹,

Schulte²,

Klemann³

et al. 2012

IEEE Trans. Visual. Comput. Graphics

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“…10(e) and 10(f) Step 2 includes time series preprocessing and the computation of similarity measures S ij for quantifying statistical interdependencies between pairs of climatological time series. In step 3, the construction of a climate network from the similarity matrix S typically involves some thresholding criterion (see Nocke et al 40 and Tominski et al 42 for details on the climate network shown here that was visualized using the software CGV 41 ). In step 4, the obtained climate network is investigated drawing on the tools of complex network theory.…”

Section: Use Case: Climate Network For Detecting Climate Transitionsmentioning

confidence: 99%

“…9 pyunicorn does not possess its own graphical interface, but where visual output is meaningful, helper methods exist for plotting with matplotlib, 39 which is especially convenient in IPython. Interfaces to tools for advanced network visualization focussing on spatial networks 40 such as CGV 41,42 are provided via pyunicorn's input-output capabilities. Commented examples for typical use cases are provided by the extensive software documentation.…”

Section: Introductionmentioning

confidence: 99%

Unified functional network and nonlinear time series analysis for complex systems science: The`pyunicorn`package

Donges

Heitzig

Beronov

et al. 2015

Chaos

Self Cite

110

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We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni-and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis (RQA), recurrence networks, visibility graphs and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.Network theory and nonlinear time series analysis provide powerful tools for the study of complex systems in various disciplines such as climatology, neuroscience, social science, infrastructure or economics. In the last years, combining both frameworks has yielded a wealth of new approaches for understanding and modeling the structure and dynamics of such systems based on the statistical analysis of network or uni-and multivariate time series. The pyunicorn software package (available at https://github.com/pik-copan/pyunicorn) facilitates the innovative synthesis of methods from network theory and nonlinear time series analysis in order to develop novel integrated methodologies. This paper provides an overview of the functionality provided by pyunicorn, introduces the theoretical concepts behind it and provides examples in the form of selected use cases mainly in the fields of climatology and paleoclimatology.

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“…Many practical visualization tools have been developed to support domain research, such as Vis5D [12], Ferret [10] and GRADS [1]. However, a gap still exists between the advanced visualizations and domain work in climate research [28].…”

Section: Related Workmentioning

confidence: 99%

A visual voting framework for weather forecast calibration

Liao

Chen

et al. 2015

2015 IEEE Scientific Visualization Conference (SciVis)

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Numerical weather predictions have been widely used for weather forecasting. Many large meteorological centers are producing highly accurate ensemble forecasts routinely to provide effective weather forecast services. However, biases frequently exist in forecast products because of various reasons, such as the imperfection of the weather forecast models. Failure to identify and neutralize the biases would result in unreliable forecast products that might mislead analysts; consequently, unreliable weather predictions are produced. The analog method has been commonly used to overcome the biases. Nevertheless, this method has some serious limitations including the difficulties in finding effective similar past forecasts, the large search space for proper parameters and the lack of support for interactive, real-time analysis. In this study, we develop a visual analytics system based on a novel voting framework to circumvent the problems. The framework adopts the idea of majority voting to combine judiciously the different variants of analog methods towards effective retrieval of the proper analogs for calibration. The system seamlessly integrates the analog methods into an interactive visualization pipeline with a set of coordinated views that characterizes the different methods. Instant visual hints are provided in the views to guide users in finding and refining analogs. We have worked closely with the domain experts in the meteorological research to develop the system. The effectiveness of the system is demonstrated using two case studies. An informal evaluation with the experts proves the usability and usefulness of the system.

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Information Visualization in Climate Research

Cited by 33 publications

References 23 publications

A Visual Analysis Concept for the Validation of Geoscientific Simulation Models

A Visual Analysis Concept for the Validation of Geoscientific Simulation Models

Unified functional network and nonlinear time series analysis for complex systems science: The`pyunicorn`package

A visual voting framework for weather forecast calibration

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Information Visualization in Climate Research

Cited by 33 publications

References 23 publications

A Visual Analysis Concept for the Validation of Geoscientific Simulation Models

A Visual Analysis Concept for the Validation of Geoscientific Simulation Models

Unified functional network and nonlinear time series analysis for complex systems science: Thepyunicornpackage

A visual voting framework for weather forecast calibration

Contact Info

Product

Resources

About

Unified functional network and nonlinear time series analysis for complex systems science: The`pyunicorn`package