Work Description

Title: Improving Forecasting Ability of GITM using Data-driven Model Refinement Open Access Deposited

h
Attribute Value
Methodology
  • These data were generated by running the Global Ionosphere-Thermosphere Model at various time periods (shown below) and extracting the modeled thermospheric mass densities along the those corresponding to the orbit of the CHAMP or GRACE satellite and the subsolar point from the empirical model MSIS. The data is then post-processed and visualized in a combination of Python and MATLAB scripts.
Description
  • This research was completed to statistically validate that a data-model refinement technique could integrate real measurements to remove bias from physics-based models via changing the forcing parameters such as the thermal conductivity coefficients.
Creator
Depositor
  • bponder@umich.edu
Contact information
Discipline
Funding agency
  • National Science Foundation (NSF)
  • Other Funding Agency
  • National Aeronautics and Space Administration (NASA)
Other Funding agency
  • Department of Defense

  • United States Air Force
Keyword
Date coverage
  • 2002-09-16 to 2006-09-26
Citations to related material
  • Ponder, B. M., Ridley, A. J., Goel, A., & Bernstein, D. S. (2023). Improving forecasting ability of GITM using data-driven model refinement. Space Weather, 21, e2022SW003290. https://doi.org/10.1029/2022SW003290
Resource type
Curation notes
  • Citation to Related Materials was updated on September 14, 2023 to reflect the published article's full citation.
Last modified
  • 09/14/2023
Published
  • 02/17/2023
Language
DOI
  • https://doi.org/10.7302/9r1a-c979
License
To Cite this Work:
Ponder, B. M., Ridley, A. J., Goel, A., Bernstein, D. S. (2023). Improving Forecasting Ability of GITM using Data-driven Model Refinement [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/9r1a-c979

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Files (Count: 2; Size: 31.6 MB)

Thumbnailthumbnail-column Title Original Upload Last Modified File Size Access Actions
README.txt 2023-02-16 2023-02-16 2.34 KB Open Access
DeepBlue.zip 2023-02-16 2023-02-16 31.6 MB Open Access

Date: November 10, 2022

Dataset Title: Improving Forecasting Ability of GITM using Data-driven Model Refinement

Dataset Creators: B. M. Ponder & A. J. Ridley & A. Goel & D. S. Bernstein

Dataset Contact: Brandon Ponder bponder@umich.edu

Funding: 2028125 (NSF), 80NSSC20K1581 (NASA), FA9550-16-1-0071 (USAF)

Key Points:
- Inaccurate approximations to physics terms and incorrect drivers within GITM can be corrected for using data-driven model refinement.

- Dynamic adjustments to the parameterized thermal conductivity coefficients can compensate for errors in model predicted mass densities.

- Comparative statistics were computed when GITM was configured in a biased version, an out-of-the-box version and the refined version.

Research Overview:
Physics-based models have a difficult time accurately estimating the upper atmosphere density. These densities are needed to compute satellite orbit trajectories to monitor for potential collisions. Inaccurate density estimation can be due to variety of factors and so methods of correcting the model-predicted density are needed. We are presenting a method to correct the densities using available satellite measurements from the CHAMP and GRACE satellites and the commonly used empirical model NRLMSISE-00. Upon reducing the model error, we show the improved ability of a physics-based model to capture a geomagnetic storm.

Methodology:
These data were generated by running the Global Ionosphere-Thermosphere Model at various time periods (shown below) and extracting the modeled thermospheric mass densities along the those corresponding to the orbit of the CHAMP or GRACE satellite and the subsolar point from the empirical model MSIS. The data is then post-processed and visualized in a combination of Python and MATLAB scripts.

Files contained:
Please refer to specific README.txt's in each Fig/ directory.

Related publication(s):
Ponder, Brandon & Ridley, Aaron & Goel, Ankit & Bernstein, Dennis. (2022). Improving Forecasting Ability of GITM using Data-driven Model Refinement. 10.1002/essoar.10512265.1. In Press.

Use and Access:
This data set is made available under a Creative Commons Public Domain license (CC0 1.0).

To Cite Data:
Ponder, Brandon & Ridley, Aaron & Goel, Ankit & Bernstein, Dennis. (2022). Improving Forecasting Ability of GITM using Data-driven Model Refinement. 10.1002/essoar.10512265.1. In Press.

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