Displays

The visual representation of data is at the heart of science. One of the choices faced by the scientist in representing data is the decision regarding colours. However, due to historical usage and default colour palettes on visualisation software, colour maps that distort data through uneven colour gradients are still commonly used today. In fact, the most-used colour map in presentations at the EGU General Assembly in 2018 - including Geodynamics sessions - was the one colour map that is most widely known to distort the data and misguide readers (see https://betterfigures.org/2018/04/16/how-many-rainbows-at-egu-2018/).

Here, we present the work that has been accomplished, the readily available solution, and present a how-to guide to ‚Scientific Colour Maps’ (Crameri 2018, Zenodo; Crameri et al. (In Review)), a methodology that prevents data distortion, offers intuitive colouring, and is accessible for people with colour-vision deficiencies.

Crameri, F. (2018). Scientific colour-maps. Zenodo. http://doi.org/10.5281/zenodo.1243862
Crameri, F., Shephard, G.E. Heron, P.J. Advantage, availability, and application of Scientific Colour Maps. (In Review with Nature Communications)

How to cite: Shephard, G. E., Crameri, F., and Heron, P. J.: How to appreciate, use, and choose Scientific Colour Maps, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11833, https://doi.org/10.5194/egusphere-egu2020-11833, 2020.

To communicate the results of a research or scientific facts and theories concerning spatial characteristics, every fields of geoscience use thematic cartography to represent spatial information. Because of this, journals, books and other publications about earth sciences has always needed accurate, reliable and clear methods of map visualization. A map should thematically fit in the body of the publication and should enrich the content. It is an extra task for scientist to take basic cartographic and visual rules into consideration, but with correct methods their publications can earn multiple benefits, such as increased readership and wider dissemination.

When using cartographic methods, we need to find the balance between the triad of i) precision, ii) quality of visual representation and iii) quantity of thematic data. The primary aim is to give an overall ‘image’ of the concerning spatial phenomena that effectively complements the written text of an article. However, these representations sometimes lack some important marks that help the reader to understand the information. Our study focuses on the quality of cartographic visualization in geosciences measuring these marks with the help of an objective system of criteria. These included image quality, cartographic elements that help to locate the studied area (e.g. coordinates), topographic content and copyright rules. By the use of this system we could give grades for each map for each criterion. We have assessed more than 300 maps per field of geoscience (geology, geography, geophysics, meteorology, cartography) in international and Hungarian journals and conference posters.

By summarizing the grades, multiple conclusions can be drawn. We can analyse the map usage habits of each science field: what type of map do they usually use (e.g. thematic or topographic), do they use maps to present results or just give an overview about a studied area and what are the common mistakes that may confuse the reader when interpreting a map. These statistics also hold the opportunity to give advices for each branch to develop their map communication skills. It was also possible to inspect the cartographic practices of each countries. We have found that there is a large spatial variability in the map use habits of different cultures. This can either mean specific but correct ways of visualization or solutions that make the map hard to understand and should not be followed. By examining this spatial factor, proposals concerning objective map element usage can be given to countries or even to whole regions to improve their cartographic communication skills.

How to cite: Pál, M. and Albert, G.: How Earth Scientists Communicate With Maps?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-321, https://doi.org/10.5194/egusphere-egu2020-321, 2020.

In this presentation, we discuss visualization strategies for optimal time and energy trajectory planning problems for Autonomous Underwater Vehicles (AUVs) in transient 3D ocean currents. Realistic forecasts using an Ocean General Circulation Model (OGCM) are used to define time and energy optimal AUV trajectory problems in 2D and 3D. The visualization goal is to explore and explain the trajectory the AUV follows, especially how it exploits both the vertical structure of the current field as well as its unsteadiness to minimize travel time and energy consumption. We present our choice of visualization tools for this purpose and discuss shortcomings and possible improvements, especially for challenging scenarios involving 3D time-dependent flow and realistic bathymetry.

How to cite: Theussl, T., Albarakati, S., Lima, R., Hoteit, I., and Knio, O.: Visualization Strategies for Optimal 3D Time-Energy Trajectory Planning for AUVs using Ocean General Circulation Models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5166, https://doi.org/10.5194/egusphere-egu2020-5166, 2020.

The new generation of global storm-resolving climate models yields model output at unprecedented resolution, going way beyond what can be displayed on a state-of-the-art computer screen. This data can be visualized in photo-realistic renderings that cannot be easily distinguished from satellite data (e.g. Stevens et al, 2019). The EU-funded Centre of Excellence in Simulation of Weather and Climate in Europe (ESiWACE) enables this kind of simulations through improvements of model performance, data storage and processing. It is closely related with the DYAMOND model intercomparison project. The Max-Planck-Institute for Meteorology (MPI-M) will contribute to the second phase of the DYAMOND intercomparison with coupled global 5 km-resolving atmosphere-ocean climate simulations, internally called DYAMOND++.

Because of the great level of detail, these simulations are especially appealing for scientific outreach. In this PICO presentation we will illustrate how we turn the output of a DYAMOND++ test simulation into a movie clip for dome theaters, as used in the WISDOME contest of the IEEE EUROVIS conference and in planetaria and science centers. Our presentation outlines the main steps of this process from data generation via pre-processing to the methods employed in the rendering of the scenes.

Stevens, B., Satoh, M., Auger, L. et al.: DYAMOND: the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains. Prog Earth Planet Sci (2019) 6: 61. https://doi.org/10.1186/s40645-019-0304-z

How to cite: Ziemen, F., Röber, N., Spickermann, D., and Böttinger, M.: Visualization of high-resolution climate model output in a Visualization dome, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7078, https://doi.org/10.5194/egusphere-egu2020-7078, 2020.

Color is an integral element in many visualizations in (geo-)sciences, specifically in maps but also bar plots, scatter plots, or time series displays. Well-chosen colors can make graphics more appealing and, more importantly, help to clearly communicate the underlying information. Conversely, poorly-chosen colors can obscure information or confuse the readers. One example for the latter gained prominence in the controversy over Hurricane Dorian: Using an official weather forecast map, U.S. President Donald Trump repeatedly claimed that early forecasts showed a high probability of Alabama being hit. We demonstrate that a potentially confusing rainbow color map may have attributed to an overestimation of the risk (among other factors that stirred the discussion).

To avoid such problems, we introduce general strategies for selecting robust color maps that are intuitive for many audiences, including readers with color vision deficiencies. The construction of sequential, diverging, or qualitative palettes is based on on appropriate light-dark "luminance" contrasts while suitably controlling the "hue" and the colorfulness ("chroma"). The strategies are also easy to put into practice using computations based on the so-called Hue-Chroma-Luminance (HCL) color model, e.g., as provided in our "colorspace" software package (http://hclwizard.org), available for both the R and Python programming languages. In addition to the HCL-based color maps the package provides interactive apps for exploring and modifying palettes along with further tools for manipulation and customization, demonstration plots, and emulation of visual constraints.

How to cite: Stauffer, R. and Zeileis, A.: Robust Color Maps That Work for Most Audiences (Including the U.S. President), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7173, https://doi.org/10.5194/egusphere-egu2020-7173, 2020.

Volcano ground deformations needs hardware and software tools of high complexity related to the processing of raw GNSS data, filtering of outliers and spikes and clear visualization of displacements occurring in real time. In this project we developed a web application for high rate real time signals visualization from permanent GNSS  remote stations managed by INGV OE (Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo). Currently the new software tool is able to import GNSS data processed by some of the most important high rate real time software like GeoRTD® (owned by Geodetics), GNSS Spider® (Owned by Leica Geosystems) and RTKlib. The tool is based on the Grafana open source platform and InfluxDB open source database. Various dashboards have been configured to display time series of the North-East-Up coordinates to monitor single stations, to compare signals coming from different data sources and to display the displacement vectors on the map. We also applied a simple alghoritm for the detection of abnormal variations due to impending volcanic activity.This web interface is applied to different active Italian volcanoes as Etna (Sicily), Stromboli (Aeolian Islands) and Phlegrean Fields (Naples). We tested the performance of this software using as a case study the 24th December 2018 dike intrusion on the Etna volcano.

How to cite: Pandolfo, F., Mattia, M., Rossi, M., and Bruno, V.: RTView 1.0: a new software for High Rate GNSS data analysis and visualization in Real Time, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9931, https://doi.org/10.5194/egusphere-egu2020-9931, 2020.

The 2016-2018 California Methane Survey used the airborne imaging spectrometer AVIRIS-NG to survey approximately 59,000 km² and 272,000 individual facilities and infrastructure components. Over 500 strong methane point sources spanning the waste management, agriculture, and energy sectors were detected, geolocated, and quantified. In order to facilitate communication of results with scientists, stakeholder agencies in California, private sector companies, and the public, we developed the Methane Source Finder web-based data portal. This state of the art Earth science data visualization tool allows users to discover, analyze, and download data across a range of spatial scales derived from remote-sensing, surface monitoring, and bottom-up infrastructure information. In this presentation, we will highlight our overall science findings from the California Methane Survey and provide a number of examples where observed methane plumes were used to directly guide leak detection and repair efforts. Future plans include expanding the data portal beyond California and incorporating regional scale flux inversions derived from satellite observations. Methane Source Finder supports methane research (e.g., multi-scale synthesis), enables facility-scale mitigation, and improves public awareness of greenhouse gas emissions.

How to cite: Thorpe, A., Duren, R., Tapella, R., Bue, B., Foster, K., Yadav, V., Rafiq, T., Hopkins, F., Gill, K., Rodriguez, J., Plave, A., Cusworth, D., and Miller, C.: Visualizing anthropogenic methane plumes from the California Methane Survey, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9945, https://doi.org/10.5194/egusphere-egu2020-9945, 2020.

This contributions deals with an extensive apparatus of sensing and modelling the Earth, producing numerous fragmented Counter-Earths - the digital models and data visualizations of the planetary ecosystem. We center our analysis around this increasingly non-human visual culture, in order to seek possible theoretical framings of global climate sensing and modelling. After a historical and theoretical introduction to emergence and composition of this infrastructure - drawing from the works of Jennifer Gabrys and Paul N. Edwards -, we elaborate a framework in which we can see machine production of images of the planet as continuous algorithmic process of transformation of planetary circumstances. Contesting interpretation of the imagery that facilitates this process as representations of the planet, we categorize climate models and satellite visual outputs as operational images, following insights by Vilem Flusser and Harun Farocki. While fully acknowledging its historical and theoretical importance, this terminology is in this contribution further assessed as still too human-centric, and for this reason, we proceed with Dietmar Offenhuber’s concept of autographic visualization that endows non-human assemblages with capacity of self-presentation and self-diagrammatization. Consequently, we conclude with several examples of autographic visualization of climate change on a planetary scale, discovering Earth’s tendency to be externalized, geological memory of modernity, that can be read through machine sensing systems uncovering these hidden traces of the past.

How to cite: Heinicker, P. and Likavčan, L.: Counter-Earths – Planetary models beyond operational images, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11205, https://doi.org/10.5194/egusphere-egu2020-11205, 2020.

With the accumulation of 4-component borehole strainmeter data and the improvement of observation reliability, it is a primary of current research to improve the efficiency of processing, analyzing and visualizing of these data. The visualization of borehole strain observation data is a key means to convey the information behind the data, display the data research results and extract the shallow surface stress state revealed by borehole strain.

Borehole strainmeter data are of great significance for earthquake prediction research due to its’ high resolution in short-medium term time scale of earthquake prediction. With the progress of observation technology, many four-component borehole strain gauges in China had experienced the data stabilization period, the early years of establishing the instrument, and the borehole strain station began to obtain a batch of high-quality observation data.

By using the normal stress petal diagram to show the change of the ground stress, it can not only qualitatively analyze the change of the relative ground stress of the station, but also quantitatively read  the observed normal stress in any direction at a certain time. In this paper, the method of normal stress petals diagram is combined with map visualization technology to process and analyze the strain observation data of four-component borehole across the country. The main works are as follows: first of all, The construction of the stress petal visualization platform can display the dynamic stress effectively in all directions of 30 stations across the country; secondly, Variable sliding window length and sliding spacing added according to specific needs can not only directly display the change of the stress petal over the years, but also show the stress petal map of the solid tide strain all over the country; thirdly, The platform can display the co-seismic stress petal variation image observed at the national borehole strain stations and visually show the stress changes observed by the local borehole strain gauge during the seismic wave propagation. Finally, The borehole strainmeter data can monitor the relative geostress state of the fault near the borehole. Then the magnitude and direction of the maximum principal stress at the borehole strain station reflected by the stress petal can further calculate the corresponding changes of dynamic coulomb stress and static coulomb stress which can help to analyze seismic dynamic triggering problems.

How to cite: Li, F., Ren, T., Zhang, H., and Shi, Y.: Visualization of 4-component borehole strainmeter data in China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4798, https://doi.org/10.5194/egusphere-egu2020-4798, 2020.

Visualization is an important and ubiquitous tool in the daily work of atmospheric researchers and weather forecasters to analyse data from simulations and observations. Visualization research has made much progress in recent years, in particular with respect to techniques for ensemble data, interactivity, 3D depiction, and feature-detection. Transfer of new techniques into the atmospheric sciences, however, is slow.

Met.3D (https://met3d.wavestoweather.de) is an open-source research software aiming at making novel interactive 3D and ensemble visualization techniques accessible to the atmospheric community. Since its first public release in 2015, Met.3D has been used in multiple visualization research projects targeted at atmospheric science applications, and also has evolved into a feature-rich visual analysis tool facilitating rapid exploration of atmospheric simulation data. The software is based on the concept of “building a bridge” between “traditional” 2D visual analysis techniques and interactive 3D techniques and allows users to analyse their data using combinations of 2D maps and cross-sections, meteorological diagrams and 3D techniques including direct volume rendering, isosurfaces and trajectories, all combined in an interactive 3D context.

This PICO will provide an overview of the Met.3D project and highlight recent additions and improvements to the software. We will show several examples of how the combination of 2D and 3D visualization elements in an interactive context can be used to explore atmospheric simulation data, including the analysis of forecast errors, analysis of synoptic-scale features including jet-streams and fronts, and analysis of forecast uncertainty in ensemble forecasts.

How to cite: Rautenhaus, M.: Met.3D: Interactive 3D ensemble visualization for rapid exploration of atmospheric simulation data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15469, https://doi.org/10.5194/egusphere-egu2020-15469, 2020.

With earth system models growing ever more complex, a comprehensive, transparent and easily communicable analysis of the interacting model components is becoming increasingly difficult. Global Sensitivity Analysis (GSA) provides a structured analytical approach to tackle this problem by quantifying the relative importance of various model inputs and components on the variability of the model outputs. However, there are a number of critical choices needed to set up a GSA, such as selecting the appropriate GSA method for the intended purpose and defining the inputs to be tested and their variability space. In this work, we test the use of interactive visualization to analyze the impacts of such critical choices on GSA results and hence achieve a more robust and comprehensive understanding of the model behavior. To this end, we combine the Python version of the Sensitivity Analysis For Everybody (SAFE) toolbox, which is currently used by more than 2000 researchers worldwide, with the literate programming platform Jupyter Notebooks, and interactive visualizations. Unlike traditional static visualization, interacting visualizations allow the user to interrogate in real time the impact of user choices on the analysis result.  Due to computational constraints of most earth system models, not all impacts can be visualized in real time (e.g. only those not requiring the model to be re-run). In those cases where a model needs to be re-run (e.g. to test the impact of the definition of the inputs space of variability), interactive visualizations still offer a useful tool, which allows to highlight only specific features of interest (e.g. behavior of the input/output samples for extreme values of the inputs or outputs). Jupyter Notebooks, by combining text, code and figures, enhance the transparency, transferability and reproducibility of GSA results. Interactive visualizations strengthen the understanding of the impacts of the choices made to run GSA and the robustness of GSA result (e.g. by being able to easily assess the impact of varying output metrics or the GSA method on the analysis results). In general, this work offers an example of how the use of notebooks and interactive visualizations can increase the transparency and communication of complex modelling concepts.

How to cite: Noacco, V., Peñuela-Fernandez, A., Pianosi, F., and Wagener, T.: Interactive Jupyter Notebooks for the visual analysis of critical choices in Global Sensitivity Analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18831, https://doi.org/10.5194/egusphere-egu2020-18831, 2020.

Identifying the atmospheric processes which lead to extreme events requires careful generalisation of the meteorological conditions surrounding such events such as sea-level pressure and air temperature. Through the case study of clustering the processes behind extreme rainfall events (annual maximum 1-day rainfall totals) in Great Britain, this presentation shows how visualising the iterative processes used by clustering algorithms can aid in algorithm selection and optimisation. Here two big data datasets, namely the CEH-GEAR (gridded observed rainfall) and NCEP/NCAR Reanalysis datasets are synthesised and clustered using different methods such as k-means, linkage methods and self-organising maps. The performances of these methods are compared and contrasted through analysis of the clusters created at each iteration, highlighting the importance of algorithm selection and understanding. The key findings of this clustering process result in three large-scale meteorological condition types which lead to extreme rainfall in Great Britain as well as a novel approach to comparing clustering mechanisms when using meteorological data.

How to cite: Barnes, A., Kjeldsen, T., and McCullen, N.: Visual approach to clustering large-scale meteorological datasets, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19018, https://doi.org/10.5194/egusphere-egu2020-19018, 2020.

NASA’s spaceborne laser altimeter, ICESat-2, sends 10,000 laser pulses per second towards Earth, in 6 separate beams, and records individual photons reflected back to its telescope. From these photon elevations, specialized ICESat-2 data products for land ice, sea ice, sea surface, land surface, vegetation and inland water are generated. Altogether these products total nearly 1 TB per day, which poses data management/visualization challenges for potential users. OpenAltimetry, a browser-based interactive visualization tool, was built to provide intuitive access to data from ICESat-2 and its predecessor mission (ICESat). It emphasizes ease of use and rapid access for expert and non-expert audiences alike. The initial design choices and subsequent user-informed development have led to a tool that has been enthusiastically received by the ICESat-2 Science Team, researchers from various disciplines, and the general public. This presentation will highlight the elements that led to OpenAltimetry’s success.

How to cite: Khalsa, S. J., Borsa, A., Nandigam, V., and Phan, M.: OpenAltimetry: Key Elements of Success in Visualizing NASA's Spaceborne LiDAR Data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20005, https://doi.org/10.5194/egusphere-egu2020-20005, 2020.

In this digital age, communication has become increasingly visual. Like never before, visual information is increasing exposure and widening outreach to new audiences. With growing demands from Journals (Table of Content arts, visual abstracts, scientific figures), conferences (posters, presentation) and competitive grants submissions, the science world is not spared, and figures represent a tremendous opportunity to communicate findings more effectively. It is therefore important to get figures and images right for the intended audience, even more so when visualizing scientific data and conveying complex concepts. 

The Intergovernmental Panel on Climate Change (IPCC), whose primary role is to inform policy makers on the state of knowledge on climate change, showcases how complex science can be visually communicated to a non-expert audience. Since its fifth assessment report, published in 2014, the IPCC has acknowledged the importance of communicating its assessments in an understandable, accessible, actionable and relevant way to all its stakeholders without compromising on the scientific robustness and accuracy. 

Currently in its sixth assessment cycle, the IPCC features a new approach to figure design in its three recently published Special Reports. This approach consists in an unprecedented collaboration between design, information and cognitive specialists and the IPCC authors. This co-design process, along with a continuous guidance to authors on visualization and cognitive concepts, was conducted in a user-centered way to best serve the audience needs and their respective background. The challenge of visually representing multi-disciplinary results, testing, evaluating, and refining the figures improved the clarity of the key messages. The entire co-design method has proven to be a successful process during the preparation of the special reports and the preparation of the sixth assessment report is building on this experience. Despite a lack of available analytics, the IPCC communication department has observed an unprecedented media coverage and a certain amount of derivative products based on the special reports figures created by third parties.

How to cite: Melissa, G., Sophie, B., Robin, M., Sarah, C., Ozge, Y., Jordan, H., Angela, M., and Tom Gabriel, J.: Data visualisation and information design at the science-policy interface: drawing from the IPCC experience. , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20574, https://doi.org/10.5194/egusphere-egu2020-20574, 2020.

Blender is an open-source 3D creation suite with a wide range of applications and users. Even though it is not a tool specifically designed for scientific visualization, it proved to be a very valuable tool to produce stunning visual results. We will show how in our workflow we go from model’s output written in netCDF to a finished visual product just relying on open-source software. The kind of visualization formats that can be produced ranges from static images to 2D/3D/360/Virtual Reality videos, enabling a wide span of potential outcomes. This kind of products are highly suitable for dissemination and scientific outreach.

How to cite: paronuzzi ticco, S., tinto primis, O., and Arsouze, T.: Using Blender for Earth Science’s visualization, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21494, https://doi.org/10.5194/egusphere-egu2020-21494, 2020.