Ultrascale Visualization of Climate Data

Abstract

Ultrascale Visualization of Climate DataUltrascale Visualization Climate Data Analysis Tools Project Team Collaboration across research, government, academic, and private sectors is integrating more than 70 scientific computing libraries and applications through a tailorable provenance framework, empowering scientists to exchange and examine data in novel ways. Fueled by exponential increases in the computational and storage capabilities of high-performance computing platforms, climate simulations are evolving toward higher numerical fidelity, complexity, volume, and dimensionality. These technological breakthroughs are coming at a time of exponential growth in climate data, with estimates of hundreds of exabytes by 2020. 1 To meet the challenges and exploit the opportunities that such explosive growth affords, a consortium of four national laboratories, two universities, a government agency, and two private companies formed to explore the next wave in climate science. Working in close collaboration with domain experts, the Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT) project aims to provide high-level solutions to a variety of climate data analysis and visualization problems:Dealing with big data analytics. Climate science is no different from other domains in its pursuit of solutions to process, analyze, and visualize massive datasets. Sensitivity analysis. The community must be able to push ensemble analysis, uncertainty quantification, and metrics computation to new boundaries. Heterogeneous data sources. Climate science data comes from simulations, observations, and reanalysis. Any visualization and analysis solution must unify these sources. Reproducibility. All science must support systematic data maintenance by providing provenance to ensure reliable and persistent links between workflows. Multiple disciplinary domains. Complexity stems from the need to incorporate a broad nexus of climate and other related science domains such as climate adaptation and mitigation for water, energy, and agriculture conservation. • Flexible, scalable architecture. Any unifying structure must be able to incorporate both existing and future software components with minimal or no infrastructure modification.

Enabling Reanalysis Research Using the Collaborative Reanalysis Technical Environment (CREATE)

“…Processing of the 6-hourly data was similar to the monthly data, but it was necessary to process the data into monthly chunks to allow the file size to be manageable for download. Once the data were organized, it was subjected to the Climate Model Output Rewriter (CMOR), where units, axis | labeling, and standardized metadata were modified to conform to the CMIP5 standards (Williams et al 2013). This processing is now largely automated, allowing newly processed reanalyses to be added to CREATE shortly after the data are made available by the reanalysis centers.…”

Web-Scale Multidimensional Visualization of Big Spatial Data to Support Earth Sciences—A Case Study with Visualizing Climate Simulation Data

“…Standard 2D presentation techniques such as time/bar charts, 2D maps, and scatterplots are most frequently used in analyzing climate data [17]. Popular visualization tools, such as UV-CDAT (Ultrascale Visualization Climate Data Analysis Tool) [18], provide great support for data regridding, exploratory data analysis, and parallel processing of memory-greedy operations [19]. However, these tools suffer great limitations in the context of cyberinfrastructure and big data science [20].…”

SimilarityExplorer: A Visual Inter‐Comparison Tool for Multifaceted Climate Data

“…There also exists some general visualization tools such as Paraview [Kit], Visit [Law] and VisTrails [Vis] which offer some specialized climate visualizations but almost all of them only present the data without supporting any analysis. Those specialized packages were integrated in a provenance‐enabled climate visualization tool UV‐CDAT [WBD*13]. However, like most other tools, UV‐CDAT does not support multi‐model analysis.…”