| NSF Org: |
OPP Office of Polar Programs (OPP) |
| Recipient: |
|
| Initial Amendment Date: | May 13, 2015 |
| Latest Amendment Date: | May 13, 2015 |
| Award Number: | 1504432 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Gregory Anderson
greander@nsf.gov (703)292-4693 OPP Office of Polar Programs (OPP) GEO Directorate for Geosciences |
| Start Date: | July 1, 2015 |
| End Date: | June 30, 2020 (Estimated) |
| Total Intended Award Amount: | $449,974.00 |
| Total Awarded Amount to Date: | $449,974.00 |
| Funds Obligated to Date: |
|
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
660 S MILL AVENUE STE 204 TEMPE AZ US 85281-3670 (480)965-5479 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
975 S Forest Mall, COOR 5644, PO Tempe AZ US 85287-5302 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): | Polar Cyberinfrastructure |
| Primary Program Source: |
|
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.078 |
ABSTRACT
The project aims at developing and implementing techniques for multivariate, multi-dimensional, and multi-faceted visualizations to support polar climate studies. Specifically, the project will focus on the following objectives. Objective 1: develop a "PolarGlobe" virtual earth and map engine with programming interface aided by cutting-edge visualization techniques that will serve as an Arctic research platform. The platform will support virtual scene simulation, volumetric graphics rendering, true terrain rendering, and dynamic scene alternation. Objective 2: A location-aware, cyber-enabled multivariate visualization technique aimed at supporting cross-sectional and longitudinal climate studies. Objective 3: A 2.5D visualization module that realizes seamless service-oriented integration of climate data with true-terrain scenes supported by a highly efficient, high spatial-resolution terrain server. Objective 4: A multi-faceted visualization module for examining complex climate patterns from different perspectives. Objective 5: A general visualization platform to support interactive, intuitive and responsive visualization of polar climate data. The development of this new polar CI portal visualization technique will serve as an intuitive, interactive and collaborative decision-making platform to uncover the unknown spatiotemporal dynamics in the polar climate and to advance the understanding of regional to global climate complexity. The cyberinfrastructure platform here developed will also serve as a very effective teaching tool for students in climate studies, polar sciences and geographic information science.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
Note:
When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external
site maintained by the publisher. Some full text articles may not yet be available without a
charge during the embargo (administrative interval).
Some links on this page may take you to non-federal websites. Their policies may differ from
this site.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Over the five years’ project course, we have conducted in-depth research for visualizing scientific big data to support and accelerate polar climate research and beyond. In particular, a novel M-cube technique is developed, that is to enable Multi-dimensional, Multi-variate, and Multi-faceted visualization of space-time big data. A cyberinfrastructure tool called PolarGlobe (http://cici.lab.asu.edu/polarglobe) has also been developed to allow anyone, from climate researchers to everyday weather watchers, to access and visualize climate data. Data in both raster and vector formats and data that are produced in real-time can all be easily and efficiently integrated and visualized in the PolarGlobe platform.
Utilizing cutting-edge streaming and volume rendering technology, PolarGlobe is capable of demonstrating the change in the atmosphere vividly and in real-time. Fundamental challenges in visualization of climate big data, including big data retrieval (Wang, Li et al. 2017), organization (Song, Li et al. 2016), and transfer (Shao and Li 2019), as well as high-performance dynamic rendering (Wang and Li 2019) that hinder the successful integration of visualization into a cyberinfrastructure environment were well addressed. The M-Cube visualization technique has empowered PolarGlobe to support climate studies in both polar regions and at a global scale. It also provides an avenue for widespread sharing and dissemination of scientific results and the generation of new hypotheses and predictions of various climate phenomena and extreme events. PolarGlobe renovates traditional climate visualization tools by removing the dependency for installing dedicated software and putting climate analysis into geographical context by enabling data visualization on a virtual globe.
PolarGlobe serves as an intuitive, interactive and collaborative decision-making platform for uncovering the unknown spatiotemporal dynamics in the polar and global climate. It implements former Vice President Al Gore’s vision of a digital earth that enables scientists and citizens across the world to interactively study our planet. It also responds to Climate Action plan by providing a powerful tool that helps uncover the driving factors of extreme weather and climate disasters in the polar regions. This cyberinfrastructure platform also serves as a very effective teaching tool for students in climate studies, polar sciences and geographic information science.
PolarGlobe has been used to reveal Arctic warming; it has also been used to study the retreating of Greenland’s icecap and atmosphere-land interactions by fusing multi-source earth observation data. It was leveraged to track in real-time the development, intensification, and movement of Hurricane Florence which was a Category 4 tropical storm that caused catastrophic damage in the Carolinas in 2018. This research received national-level media attention and was reported by Physics.org, infodocket, and the Bulletin of the American Meteorological Society.
Videos introducing PolarGlobe functions can be found here: https://asunow.asu.edu/20181001-creativity-illustrating-dance-earth. Publications of PolarGlobe related research are listed below:
1. H. Shao, W. Li, and W. Kang, S.J. Rey, 2020. When spatial analytics meets cyberinfrastructure: a replicable cyberinfrastructure for online spatial-statistical-visual analytics. Journal of Geovisualization and Spatial Analysis, 4(2020), 17.
2. S. Wang and W. Li, 2019. Capturing the dance of the Earth: PolarGlobe: real-time scientific visualization of vector field data to support climate science. Computers, Environment and Urban Systems, 77(2019), 101352.
3. H. Shao and W. Li, 2019. A comprehensive optimization strategy for real-time spatial feature sharing and visual analytics in cyberinfrastructure. International Journal of Digital Earth. https://doi.org/10.1080/17538947.2017.1421719.
4. S. Wang, W. Li, and F. Wang, 2017. Web-scale multidimensional visualization of geospatial big data to support climate sciences. Informatics, 4(3), 17.
5. W. Li and S. Wang, 2017. PolarGlobe: A Web-wide virtual globe system for visualizing multidimensional, time-varying, big climate data. International Journal of Geographical Information Science, doi: 10.1080/13658816.2017.1306863.
6. F. Wang, W. Li, and S. Wang, 2016. Polar cyclone identification from 4D climate data in a knowledge-driven visualization system. Climate, 4(3), 43; doi:10.3390/cli4030043.
7. W. Li, S. Wu, M. Song and X. Zhou. 2016. A scalable cyberinfrastructure solution to support big data management and multivariate visualization of time-series sensor observation data. Earth Science Informatics, DOI: 10.1007/s12145-016-0267-1.
Last Modified: 08/18/2020
Modified by: Wenwen Li
Please report errors in award information by writing to: awardsearch@nsf.gov.
