Award Abstract # 2149747
Collaborative Research: ANSWERS: Prediction of Geoeffective Solar Eruptions, Geomagnetic Indices, and Thermospheric Density Using Machine Learning Methods

NSF Org: AGS
Division of Atmospheric and Geospace Sciences
Recipient: RUTGERS, THE STATE UNIVERSITY
Initial Amendment Date: April 22, 2022
Latest Amendment Date: December 13, 2023
Award Number: 2149747
Award Instrument: Standard Grant
Program Manager: Mangala Sharma
msharma@nsf.gov
 (703)292-4773
AGS
 Division of Atmospheric and Geospace Sciences
GEO
 Directorate for Geosciences
Start Date: May 1, 2022
End Date: April 30, 2026 (Estimated)
Total Intended Award Amount: $539,988.00
Total Awarded Amount to Date: $596,791.00
Funds Obligated to Date: FY 2022 = $539,988.00
FY 2024 = $56,803.00
History of Investigator:
  • Xiaoli Bai (Principal Investigator)
    xiaoli.bai@rutgers.edu
Recipient Sponsored Research Office: Rutgers University New Brunswick
3 RUTGERS PLZ
NEW BRUNSWICK
NJ  US  08901-8559
(848)932-0150
Sponsor Congressional District: 12
Primary Place of Performance: Rutgers University New Brunswick
33 Knightsbridge Road
Piscataway
NJ  US  08854-3925
Primary Place of Performance
Congressional District:
06
Unique Entity Identifier (UEI): M1LVPE5GLSD9
Parent UEI:
NSF Program(s): MAGNETOSPHERIC PHYSICS,
AERONOMY,
Space Weather Research
Primary Program Source: 01002223DB NSF RESEARCH & RELATED ACTIVIT
01002425DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 8092, 123Z, 4444
Program Element Code(s): 575000, 152100, 808900
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.050

ABSTRACT

Understanding and predicting eruptions on the Sun and their terrestrial impacts are a research as well as strategic national priority, as such space weather affects our electronic communication, electric power supply, satellite infrastructure, national defense, and more. This project is a collaboration among Rutgers University, New Jersey Institute of Technology, West Virginia University, and Montclair State University that will improve our ability to predict several linked space weather components: geoeffective solar eruptions, the global magnetic response of Earth to these eruptions, as well as variation of neutral density in the Earth?s thermosphere and its effect on satellite drag. The work covers many aspects of geospace science, solar physics, and data science including machine learning. The innovative machine learning tools developed from the project will be applicable for analyzing disparate data sets in astronomy and other areas of science. Faculty members, early career researchers including a postdoctoral fellow and graduate students will collaborate on the project, creating a multidisciplinary training program for future generations of scientists. The project will emphasize diversity and the participation of underrepresented minorities through both the research efforts and education activities such as K-12 teacher workshops.

The two key science questions are: What are the physical mechanisms for the onset of geoeffective solar eruptions? And what are the effects of solar eruptions on neutral density in the thermosphere? Specifically, the project will create synthetic vector magnetograms using ground- and space-based data for solar cycles 23 and 24; develop machine learning (ML) tools to predict solar flares and associated geoeffective coronal mass ejections (CMEs) based on magnetogram parameters; predict geomagnetic indices from derived magnetic properties of solar active regions and CMEs, solar wind parameters and solar images; and predict neutral density in the thermosphere using ML approaches that integrate satellite data, observed and predicted geomagnetic indices, and empirical neutral density models. Most of the funding will be used to support three graduate students (one at WVU and two at NJIT) and a postdoc at Rutgers. K-12 teacher workshops will be organized by Montclair State University. ANSWERS projects advance the nation?s STEM expertise and societal resilience to space weather hazards by filling key knowledge gaps regarding the coupled Sun-Earth system.

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

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Wang, Yiran and Bai, Xiaoli "Thermospheric density predictions during quiet time and geomagnetic storm using a deep evidential model-based framework" Acta Astronautica , v.211 , 2023 https://doi.org/10.1016/j.actaastro.2023.06.023 Citation Details
Yiran Wang, Xiaoli Bai "Comparison of Gaussian processes and Neural Networks for thermospheric density predictions during quiet time and geomagnetic storms" 2022 AAS/AIAA Astrodynamics Specialist Conference , 2022 Citation Details
Yiran Wang, Xiaoli Bai "Global Thermospheric Density Prediction Model Based on Deep Evidential Framework" , 2023 Citation Details
Wang, Yiran and Bai, Xiaoli "A Global Thermospheric Density Prediction Framework Based on a Deep Evidential Method" Space Weather , v.22 , 2024 https://doi.org/10.1029/2024SW004070 Citation Details
Yiran Wang, Xiaoli Bai "Comparison of Gaussian processes and Neural Networks for thermospheric density predictions during quiet time and geomagnetic storms" 2022 AAS/AIAA Astrodynamics Specialist Conference , 2022 Citation Details
Daniell, Joshua D. and Mehta, Piyush M. "Probabilistic Solar Proxy Forecasting With Neural Network Ensembles" Space Weather , v.21 , 2023 https://doi.org/10.1029/2023SW003675 Citation Details
Joshua Daniell, Piyush M. "Sources of uncertainty in drag modeling and its effect on predicted stated covariance" , 2023 Citation Details
Licata, Richard J. and Mehta, Piyush M. and Weimer, Daniel R. and Drob, Douglas P. and Tobiska, W. Kent and Yoshii, Jean "Science Through Machine Learning: Quantification of PostStorm Thermospheric Cooling" Space Weather , v.20 , 2022 https://doi.org/10.1029/2022SW003189 Citation Details
Licata, Richard J. and Mehta, Piyush M. and Weimer, Daniel R. and Tobiska, W. Kent and Yoshii, Jean "MSISUQ: Calibrated and Enhanced NRLMSIS 2.0 Model With Uncertainty Quantification" Space Weather , v.20 , 2022 https://doi.org/10.1029/2022SW003267 Citation Details
Mehta, Piyush_M and Licata, Richard_J "TIEGCM ROPE Dimensionality Reduction: Part I" Space Weather , v.23 , 2025 https://doi.org/10.1029/2024SW004185 Citation Details

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