Evaluating image quality measures to assess the impact of lossy data compression applied to climate simulation data
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data compression, scientific visualization, climat↗
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Web-based Visual Analytics for Extreme Scale Climate Science
In this paper, we introduce a Web-based visual analytics framework for democratizing advanced visualization and analysis capabilities pertinent to large-scale earth system simulations. We address significant limitations of present climate data analysis tools such as tightly coupled dependencies, ineffi- cient data movements, complex user interfaces, and static visualizations. Our Web-based visual analytics framework removes critical barriers to the widespread accessibility and adoption of advanced scientific techniques. Using distributed connections to back-end diagnostics, we minimize data movements and leverage HPC platforms. We also mitigate system dependency issues by employing a RESTful interface. Our framework embraces the visual analytics paradigm via new visual navigation techniques for hierarchical parameter spaces, multi-scale representations, and interactive spatio-temporal data mining methods that retain details. Although generalizable to other science domains, the current work focuses on improving exploratory analysis of large-scale Community Land Model (CLM) and Community Atmosphere Model (CAM) simulations.
climate science↗
Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT)
For the past three years, a large analysis and visualization effort—funded by the Department of Energy’s Office of Biological and Environmental Research (BER), the National Aeronautics and Space Administration (NASA), and the National Oceanic and Atmospheric Administration (NOAA)—has brought together a wide variety of industry-standard scientific computing libraries and applications to create Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT) to serve the global climate simulation and observational research communities. To support interactive analysis and visualization, all components connect through a provenance application–programming interface to capture meaningful history and workflow. Components can be loosely coupled into the framework for fast integration or tightly coupled for greater system functionality and communication with other components. The overarching goal of UV-CDAT is to provide a new paradigm for access to and analysis of massive, distributed scientific data collections by leveraging distributed data architectures located throughout the world. The UV-CDAT framework addresses challenges in analysis and visualization and incorporates new opportunities, including parallelism for better efficiency, higher speed, and more accurate scientific inferences. Today, it provides more than 600 users access to more analysis and visualization products than any other single source.
54 ENVIRONMENTAL SCIENCES↗
Experts’ Familiarity Versus Optimality of Visualization Design: How Familiarity Affects Perceived and Objective Task Performance
Experts, cutting across domains like biology, climate science, cyber, energy, etc., frequently use visualizations as the principal medium for making analytical judgments or for presenting the results of their analysis. However, scientists are often skeptical about adopting new visualization methods over familiar ones, although the latter might be perceptually sub-optimal. This is due to the use of the familiarity heuristic, where the perceived cognitive ease in processing familiar representations of information leads scientists to undermine the effect of visualization best practices. Recent studies have shown that this often results in a discrepancy between scientists’ perceived and actual performance quality. It has also been shown that in some cases, participatory design sessions and qualitative and quantitative user studies are able to mitigate the effects of such bias. In this paper, we discuss the potential causes and effects of familiarity related biases with examples from recent studies and reflect on the related research questions.
visualization, climate science, cognitive bias, fa↗
Empirical Analysis of the Subjective Impressions and Objective Measures of Domain Scientists’ Visual Analytic Judgments
Scientists often use specific data analysis and presentation methods familiar within their domain. But does high familiarity drive better analytical judgment? This question is especially relevant when familiar methods themselves can have shortcomings: many visualizations used conventionally for scientific data analysis and presentation do not follow established best practices. This necessitates new methods that might be unfamiliar yet prove to be more effective. But there is little empirical understanding of the relationships between scientists’ subjective impressions about familiar and unfamiliar visualizations and objective measures of their visual analytic judgments. To address this gap and to study these factors, we focus on visualizations used for comparison of climate model performance. We report on a comprehensive survey-based user study with 47 climate scientists and present an analysis of : i) relationships among scientists’ familiarity, their perceived lev- els of comfort, confidence, accuracy, and objective measures of accuracy, and ii) relationships among domain experience, visualization familiarity, and post-study preference.
Dasgupta, Aritra↗
CrossVis: A visual analytics system for exploring heterogeneous multivariate data with applications to materials and climate sciences
Not Available
97 MATHEMATICS AND COMPUTING↗
Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT) Final Report
A partnership across government, academic, and private sectors has created a novel system that enables climate researchers to solve current and emerging data analysis and visualization challenges. The Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT) software project utilizes the Python application programming interface (API) combined with C/C++/Fortran implementations for performance-critical software that offers the best compromise between "scalability" and “ease-of-use.” The UV-CDAT system is highly extensible and customizable for high-performance interactive and batch visualization and analysis for climate science and other disciplines of geosciences. For complex, climate data-intensive computing, UV-CDAT’s inclusive framework supports Message Passing Interface (MPI) parallelism as well as taskfarming and other forms of parallelism. More specifically, the UV-CDAT framework supports the execution of Python scripts running in parallel using the MPI executable commands and leverages Department of Energy (DOE)-funded general-purpose, scalable parallel visualization tools such as ParaView and VisIt. This is the first system to be successfully designed in this way and with these features. The climate community leverages these tools and others, in support of a parallel client-server paradigm, allowing extreme-scale, server-side computing for maximum possible speed-up.
54 ENVIRONMENTAL SCIENCES↗
2014 Earth System Grid Federation and Ultrascale Visualization Climate Data Analysis Tools Conference Report
The climate and weather data science community met December 9–11, 2014, in Livermore, California, for the fourth annual Earth System Grid Federation (ESGF) and Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT) Face-to-Face (F2F) Conference, hosted by the Department of Energy, National Aeronautics and Space Administration, National Oceanic and Atmospheric Administration, the European Infrastructure for the European Network of Earth System Modelling, and the Australian Department of Education. Both ESGF and UVCDATremain global collaborations committed to developing a new generation of open-source software infrastructure that provides distributed access and analysis to simulated and observed data from the climate and weather communities. The tools and infrastructure created under these international multi-agency collaborations are critical to understanding extreme weather conditions and long-term climate change. In addition, the F2F conference fosters a stronger climate and weather data science community and facilitates a stronger federated software infrastructure. The 2014 F2F conference detailed the progress of ESGF, UV-CDAT, and other community efforts over the year and sets new priorities and requirements for existing and impending national and international community projects, such as the Coupled Model Intercomparison Project Phase Six. Specifically discussed at the conference were project capabilities and enhancements needs for data distribution, analysis, visualization, hardware and network infrastructure, standards, and resources.
54 ENVIRONMENTAL SCIENCES↗
Global variance decomposition of downscaled and bias-corrected CMIP6 climate projections
This dataset provides the results of a global variance decomposition of downscaled and bias-corrected CMIP6 climate projections. The total projection variance for a set of climate metrics is partitioned into contributions from: scenario uncertainty, model/GCM uncertainty, downscaling and bias-correction uncertainty, and interannual variability. The contribution from each source is expressed as a percentage of the total variance. Seven climate metrics are analyzed: Annual average temperature (avg_tas.nc) Annual total precipitation (tot_pr.nc) Annual maximum of daily maximum temperature (max_tasmax.nc) Annual maximum 1-day precipitation (max_pr.nc) Annual number of extremely hot days (hot_days.nc) Annual number of extremely wet days (wet_days.nc) Annual number of dry days (dry_days.nc) Extremely hot/wet days are defined to occur when temperature/precipitation exceeds the local 99th percentile defined over 1980-2014. Dry days are defined to occur when daily precipitation is less than 1mm. all_metrics_timesliced.nc gives the results for all metrics averaged over three 20-year periods: 2020-2039, 2050-2069, 2080-2099. For more details on the methods, see: Lafferty & Sriver, Downscaling and bias-correction contribute considerable uncertainty to local climate projections in CMIP6, npj Climate & Atmospheric Science (2023) An interactive visualization of this data can be found at: https://lafferty-sriver-2023-downscaling-uncertainty.msdlive.org
Lafferty, David↗
Use of computational modeling combined with advanced visualization to develop strategies for the design of crop ideotypes to address food security
Sustainable crop production is a contributing factor to current and future food security. Innovative technologies are needed to design strategies that will achieve higher crop yields on less land and with fewer resources. Computational modeling coupled with advanced scientific visualization enables researchers to explore and interact with complex agriculture, nutrition, and climate data to predict how crops will respond to untested environments. These virtual observations and predictions can direct the development of crop ideotypes designed to meet future yield and nutritional demands. This review surveys modeling strategies for the development of crop ideotypes and scientific visualization technologies that have led to discoveries in “big data” analysis. Combined modeling and visualization approaches have been used to realistically simulate crops and to guide selection that immediately enhances crop quantity and quality under challenging environmental conditions. Lastly, this survey of current and developing technologies indicates that integrative modeling and advanced scientific visualization may help overcome challenges in agriculture and nutrition data as large-scale and multidimensional data become available in these fields.
3D crop modeling↗
Bridging Theory with Practice: An Exploratory Study of Visualization Use and Design for Climate Model Comparison
Evaluation methodologies in visualization have mostly focused on how well the tools and techniques cater to the analytical needs of the user. While this is important in determining the effectiveness of the tools and advancing the state-of-the-art in visualization research, a key area that has mostly been overlooked is how well established visualization theories and principles are instantiated in practice. This is especially relevant when domain experts, and not visualization researchers, design visualizations for analysis of their data or for broader dissemination of scientific knowledge. There is very little research on exploring the synergistic capabilities of cross-domain collaboration between domain experts and visualization researchers. To fill this gap, in this paper we describe the results of an exploratory study of climate data visualizations conducted in tight collaboration with a pool of climate scientists. The study analyzes a large set of static climate data visualizations for identifying their shortcomings in terms of visualization design. The outcome of the study is a classification scheme that categorizes the design problems in the form of a descriptive taxonomy. The taxonomy is a first attempt for systematically categorizing the types, causes, and consequences of design problems in visualizations created by domain experts. We demonstrate the use of the taxonomy for a number of purposes, such as, improving the existing climate data visualizations, reflecting on the impact of the problems for enabling domain experts in designing better visualizations, and also learning about the gaps and opportunities for future visualization research. We demonstrate the applicability of our taxonomy through a number of examples and discuss the lessons learnt and implications of our findings.
54 ENVIRONMENTAL SCIENCES↗
3rd Annual Earth System Grid Federation and 3rd Annual Earth System Grid Federation and Ultrascale Visualization Climate Data Analysis Tools Face-to-Face Meeting Report December 2013
The climate and weather data science community gathered December 3–5, 2013, at Lawrence Livermore National Laboratory, in Livermore, California, for the third annual Earth System Grid Federation (ESGF) and Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT) Face-to-Face (F2F) Meeting, which was hosted by the Department of Energy, National Aeronautics and Space Administration, National Oceanic and Atmospheric Administration, the European Infrastructure for the European Network of Earth System Modelling, and the Australian Department of Education. Both ESGF and UV-CDAT are global collaborations designed to develop a new generation of open-source software infrastructure that provides distributed access and analysis to observed and simulated data from the climate and weather communities. The tools and infrastructure developed under these international multi-agency collaborations are critical to understanding extreme weather conditions and long-term climate change, while the F2F meetings help to build a stronger climate and weather data science community and stronger federated software infrastructure. The 2013 F2F meeting determined requirements for existing and impending national and international community projects; enhancements needed for data distribution, analysis, and visualization infrastructure; and standards and resources needed for better collaborations.
54 ENVIRONMENTAL SCIENCES↗
Parallel analysis tools and new visualization techniques for ultra-large climate data set
ParVis was a project funded under LAB 10-05: “Earth System Modeling: Advanced Scientific Visualization of Ultra-Large Climate Data Sets”. Argonne was the lead lab with partners at PNNL, SNL, NCAR and UC-Davis. This report covers progress from January 1st, 2013 through Dec 1st, 2014. Two previous reports covered the period from Summer, 2010, through September 2011 and October 2011 through December 2012, respectively. While the project was originally planned to end on April 30, 2013, personnel and priority changes allowed many of the institutions to continue work through FY14 using existing funds. A primary focus of ParVis was introducing parallelism to climate model analysis to greatly reduce the time-to-visualization for ultra-large climate data sets. Work in the first two years was conducted on two tracks with different time horizons: one track to provide immediate help to climate scientists already struggling to apply their analysis to existing large data sets and another focused on building a new data-parallel library and tool for climate analysis and visualization that will give the field a platform for performing analysis and visualization on ultra-large datasets for the foreseeable future. In the final 2 years of the project, we focused mostly on the new data-parallel library and associated tools for climate analysis and visualization.
54 ENVIRONMENTAL SCIENCES↗
Department of Energy's Biological and Environmental Research Strategic Data Roadmap for Earth System Science
Rapid advances in experimental, sensor, and computational technologies and techniques are driving exponential growth in the volume, acquisition rate, variety, and complexity of scientific data. This wealth of scientifically meaningful data has tremendous potential to lead to scientific discovery. However, to achieve scientific breakthroughs, these data must be exploitable—they must be analyzed effectively and efficiently and the results shared and communicated easily within the wider Department of Energy’s (DOE’s) Biological and Environmental Research (BER) Climate and Environmental Sciences Division (CESD) community. The explosion in data complexity and scale makes these tasks exceedingly difficult to achieve, particularly given that an increasing number of disciplines are working across techniques, integrating simulation and experimental or observational results (see Table 5 in Appendix 2). Consequently, we need new approaches to data management, analysis, and visualization that provide research teams with easy-to-use and scalable end-to-end solutions. These solutions must facilitate (and where feasible, automate and capture) every stage in the data lifecycle (shown in Figure 1), from collection to management, annotation, sharing, discovery, analysis, and visualization. In addition, the core functionalities are the same across climate science communities, but they require customization to adapt to specific needs and fit into research and analysis workflows. To this end, the mission of CESD’s Data and Informatics Program is to integrate all existing and future distributed CESD data holdings into a seamless and unified environment for the acceleration of Earth system science.
54 ENVIRONMENTAL SCIENCES↗
Expanding Access to Science Participation: A FAIR Framework for Petascale Data Visualization and Analytics
The massive data generated by scientists daily serve as both a major catalyst for new discoveries and innovations, as well as a significant roadblock that restricts access to the data. Here, our paper introduces a new approach to removing Big Data barriers and democratizing access to petascale data for the broader scientific community. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hiding the complexities of dealing with file systems or cloud services. We enable FAIR (Findable, Accessible, Interoperable, and Reusable) access to datasets such as NASA’s petascale climate datasets. Our paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction utilizes state-of-the art progressive compression algorithms and machine-learning insights to power scalable visualization dashboards for petascale data. The result provides users with the ability to identify extreme events or trends dynamically, expanding access to scientific data and further enabling discoveries. We validate our approach by improving the ability of climate scientists to visually explore their data via three fully interactive dashboards. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minority-serving institution. These dashboards, released in simplified form to the general public, contribute significantly to a broader push to democratize the access and use of climate data.
Computer science↗
Separating the Wheat from the Chaff: Comparative Visual Cues for Transparent Diagnostics of Competing Models
Experts in data and physical sciences have to regularly grapple with the problem of competing models. Be it analytical or physics-based models, a cross-cutting challenge for experts is to reliably diagnose which model outcomes appropriately predict or simulate real-world phenomena. Expert judgment involves reconciling information across many, and often, conflicting criteria that describe the quality of model outcomes. In this paper, through a design study with climate scientists, we develop a deeper understanding of the problem and solution space of model diagnostics, resulting in the following contributions: i) a problem and task characterization using which we map experts’ model diagnostics goals to multi-way visual comparison tasks, ii) a design space of comparative visual cues for letting experts quickly understand the degree of disagreement among competing models and gauge the degree of stability of model outputs with respect to alternative criteria, and iii) design and evaluation of MyriadCues, an interactive visualization interface for exploring alternative hypotheses and insights about good and bad models by leveraging comparative visual cues. Here, we present case studies and subjective feedback by experts, which validate how MyriadCues enables more transparent model diagnostic mechanisms, as compared to the state of the art.
54 ENVIRONMENTAL SCIENCES↗
Louisiana Immersive Technologies Enterprise (LITE): Enabling Coastal Stewardship and Reliable Energy
This project will focus on research and application development in the area of large scale data computing and visualization that are critical to the DoE BER program areas of climate change research, environmental sciences, life sciences, and medical sciences. Most commercial applications available today are unable to handle the sizes of data in these domains, usually in the order of terabytes and petabytes. This project will address this problem, by taking advantage of the advanced scientific computing infrastructure of the LITE and LONI to conduct research and develop software applications that run natively on these 64-bit supercomputing platforms. The project is consistent with the goals of Scientific Discovery through Advanced Computing (SciDAC), a DoE initiative.
54 ENVIRONMENTAL SCIENCES↗
Exascale Virtualized and Programmable Distributed Cyber Resource Control: Final Scientific Technical Report
Extreme-science drives the need for distributed exascale processing and communications that are carefully, yet flexibly, managed. Exponential growth of data for scientific simulations, experimental data, collaborative data analyses, remote visualization and GRID computing requirements of scientists in fields as diverse as high energy physics, climate change, genomics, fusion, synchrotron radiation, material science, medicine, and other scientific disciplines cannot be accommodated by simply applying existing transport protocols to faster pipes. Further, scientific challenges today demand diverse research teams, heightening the need for and increasing the complexity of collaboration. To address these issues within the network layer and physical layer, we have performed a number of research activities surrounding effective allocation and management of elastic optical network (EON) resources, particularly focusing on FlexGrid transponders. FlexGrid transponders support the opportunity to build Layer-1 connections at a wide range of bandwidths and to reconfigure them rapidly. The new flexibility supports complex new ways of using the physical layer that must be carefully managed and hidden from the scientist end-users. FlexGrid networks utilize flexible (or elastic) spectral bandwidths for each data link without using fixed wavelength grids. The flexibility in spectrum allocation brings many appealing features to network operations. Current networks are designed for the worst case impairments in transmission performance and the assigned spectrum is over-provisioned. In contrast, the FlexGrid networks can operate with the highest spectral efficiency and minimum bandwidth for the given traffic demand while meeting the minimum quality of transmission (QoT) requirement. Two primary focuses of our research are: (1) resource and spectrum allocation (RSA) for IP traffic over EONs, and (2) RSA for cross-domain optical networks. Previous work concentrates primarily on large file transfers within a single domain. Adding support for IP traffic changes the nature of the RSA problem: instead of choosing to accept or deny each request for network support, IP traffic is inherently elastic and thus lends itself to a bandwidth maximization formulation. We developed a number of algorithms that could be easily deployed within existing and new FlexGrid networks, leading to networks that better support scientific collaboration. Cross-domain RSA research is essential to support large-scale FlexGrid networks, since configuration information is generally not shared or coordinated across domains. The results presented here are in their early stages. They are technically feasible and practical, but still require coordination among organizations and equipment owners and a higher-layer framework for managing network requests.
42 ENGINEERING↗