
NSF Org: |
OAC Office of Advanced Cyberinfrastructure (OAC) |
Recipient: |
|
Initial Amendment Date: | April 6, 2021 |
Latest Amendment Date: | May 21, 2021 |
Award Number: | 2104004 |
Award Instrument: | Standard Grant |
Program Manager: |
Varun Chandola
vchandol@nsf.gov (703)292-2656 OAC Office of Advanced Cyberinfrastructure (OAC) CSE Directorate for Computer and Information Science and Engineering |
Start Date: | July 1, 2021 |
End Date: | June 30, 2026 (Estimated) |
Total Intended Award Amount: | $599,879.00 |
Total Awarded Amount to Date: | $599,879.00 |
Funds Obligated to Date: |
|
History of Investigator: |
|
Recipient Sponsored Research Office: |
58 EDGEWOOD AVE NE ATLANTA GA US 30303-2921 (404)413-3570 |
Sponsor Congressional District: |
|
Primary Place of Performance: |
25 Park Pl NE Suite 700 Atlanta GA US 30303-2921 |
Primary Place of
Performance Congressional District: |
|
Unique Entity Identifier (UEI): |
|
Parent UEI: |
|
NSF Program(s): |
SOLAR-TERRESTRIAL, Software Institutes, EarthCube |
Primary Program Source: |
|
Program Reference Code(s): |
|
Program Element Code(s): |
|
Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
Extreme space weather events such as solar flares, coronal mass ejections, energetic proton events and geomagnetic storms can cause massive disruptions in many technologically complex systems, including radio communications, telecommunication and navigation satellites, electrical power systems, or space and even commercial airline flights. This project builds a detection and analysis cyberinfrastructure, and investigates one of the most distinctive features in the solar atmosphere ? magnetic polarity inversion lines, which are hotspots of the most intense eruptive activity. Analyzing these features enables solar physicists to advance understanding of extreme space weather events and provide needed predictive capabilities for space weather forecasters.
This project creates an innovative and sustainable software infrastructure to detect, characterize and analyze polarity inversion lines. The first step toward that objective is the identification of polarity inversion lines, and quantitative characterization of these multi-faceted features through image descriptors. In subsequent stages, this project analyzes the time series of these features and descriptors using advanced machine learning and data mining techniques, specifically for improving space weather forecasting capabilities. This includes analyzing the spatiotemporal patterns of emergence and disappearance for polarity inversion lines, selecting and understanding important shape characteristics of these lines pertinent to solar eruptive activity, and creating a prototype eruption forecasting system with discovered precursors. Automatically identifying and analyzing polarity inversion lines has several direct benefits: physically understanding solar magnetic shear layers and the transition from typical non-eruptive active region states to intense, eruptive ones; making contributions to forecasting of solar eruptions; and generating descriptors and measures that can be useful to the study of shear layers in nature.
This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Solar Terrestrial Physics Program and the Division of Integrative and Collaborative Education and Research within the NSF Directorate for Geosciences.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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.
Please report errors in award information by writing to: awardsearch@nsf.gov.