Web enabled collaborative climate visualization in the Earth System Grid

Kendall, Wesley; Glatter, M.; Huang, Jian; Hoffman, Forrest M.; Bernholdt, David E.

doi:10.1109/cts.2008.4543934

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…Additional work has been done by Kendall et al [15] in the area of web-enabled, collaborative visualization of ESG data. Again, through the organization of data and use of common components such as web browsers, Lee et al [19] have constructed a system for faster, interactive analysis of molecular simulations by automating portions of the workflow.…”

Section: Related Workmentioning

confidence: 96%

Experiences using smaash to manage data-intensive simulations

Hudson

Norris

Reid

et al. 2011

Proceedings of the 20th International Symposium on High Performance Distributed Computing

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High performance scientific computer simulations created with such systems as the University of Chicago's FLASH code generate enormous amounts of data that must be captured, cataloged, and analyzed. Unless this is formally done, monitoring such simulations, tracking and reproducing old ones, and analyzing and archiving their output, can be haphazard and idiosyncratic. Smaash, a simulation management and analysis system that has been developed at the University of Chicago and Argonne National Laboratory, seeks to solve some of these problems by offering what approaches a single point of control and analysis, a metadatabase, and a set of tools that automate some of what scientists have been doing by hand.Smaash was designed to be independent of the particular simulation code, and is accessible from many computing platforms. It is automatic and standardized, and was built using open source software tools. Data security is considered throughout the process, yet users are insulated from onerous verification procedures. Because the system was developed with feedback from scientific users, its user interface reflects how scientists work in their daily life. We describe our system and a typical simulation it was designed to support. We illustrate its utility with several examples describing our experience of freeing scientists from the data manipulation phase to focus on the computational results and the analysis of high performance computing.

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Section: Related Workmentioning

confidence: 96%

Experiences using smaash to manage data-intensive simulations

Hudson

Norris

Reid

et al. 2011

Proceedings of the 20th International Symposium on High Performance Distributed Computing

View full text Add to dashboard Cite

show abstract

“…Therefore, providing accessible and cost-effective solutions for scalability, such as cloud-based platforms, is crucial to enable a broader range of users to harness the benefits of scaling in Earth system data analysis. 6 Visual interaction with Data Cubes Data and process visualisation are critical for communicating Earth system science because big data are often hard to understand intuitively based on metadata alone, especially for non-expert audiences [133][134][135]. The gap between analytic capability and the means to effectively visualise results slows our progress in understanding complex Earth system phenomena.…”

Section: Scalability Obstaclesmentioning

confidence: 99%

Data Cubes for Earth System Research: Challenges Ahead

Montero Loaiza,

Kraemer,

Anghelea

et al. 2023

Preprint

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Progress in Earth system science is accelerating rapidly, due to the increasing availability of multivariate datasets, often global, with moderate to high spatio-temporal resolutions. Turning these data into knowledge presents interoperability, technical, analytical, and other challenges. Earth System Data Cubes (ESDCs) have surfaced as essential tools, offering analysis-ready, cloud-optimised multivariate solutions. Coupled with advancements in Artificial Intelligence (AI), these solutions have the potential to release a wealth of information from the vast amounts of data that they contain. The application of AI methods to ESDCs promises to unpick the complexities of the Earth system, learning the underlying non-linearities to forecast its spatio-temporal behaviour. However, naive applications of such methods might lead to wrong conclusions and predictions. In this perspective paper, we discuss the methodological and conceptual challenges that AI applications of ESDCs bring. Particular risks are naive applications that ignore intrinsic properties of the Earth system, such as spatio-temporal auto-correlation issues that may deliver highly accurate but flawed predictions. Other applications may ignore known causal structures of Earth system dynamics. We also face technical challenges, such as adequate sampling strategies in ESDCs. Furthermore, documenting data cube provenance is essential to ensure end-to-end reproducible workflows. Effective visualisation tools are required to enable users to quickly navigate terabytes of data and develop an intuition for spatio-temporal dynamics encoded in these cubes. Given this, we aim to synthesise the main challenges and derive an agenda for advancing data science on data cubes to better understand global Earth system processes.

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“…The system is independent of grid type, inherently parallel and scalable, and distilled to be state-free in the sense that it operates like web servers (allowing as many concurrent clients as desired [1]). The system not only operates with near-optimal load balance on centralized computing clusters, but also efficiently works in geographically distributed settings [6]. Figure 2 shows our parallel visualization infrastructure supporting 27 concurrent interactive visualization clients on a Powerwall system to study the well-known Terascale Supernova Initiative (TSI) dataset.…”

Section: Activities and Findingsmentioning

confidence: 99%

“…Supported by the DOE ECPI award, we have five papers [1-5] (four already published and one under revision) in top visualization journals and two conference papers [6][7].…”

Section: Publications and Productsmentioning

confidence: 99%

Transfer Function Design for Scientific Discovery

Huang¹

2008

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Web enabled collaborative climate visualization in the Earth System Grid

Abstract: ABSTRACT

Cited by 6 publications

References 12 publications

Experiences using smaash to manage data-intensive simulations

Experiences using smaash to manage data-intensive simulations

Data Cubes for Earth System Research: Challenges Ahead

Transfer Function Design for Scientific Discovery

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