Work Description

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| README.txt |
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Project Information
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Date: 17 April, 2022

Dataset Title: Data Pertaining to Initial Simulations Using the MAGNetosphere-Ionosphere-Thermosphere (MAGNIT) Auroral Precipitation Model

Dataset Creator: A. Mukhopadhyay

Dataset Contact: Agnit Mukhopadhyay agnitm@umich.edu

Primary Funding: 80NSSC17K0015 (NASA) & 0NSSC18K1120 (NASA Earth and Space Science Fellowship, NESSF), Grant 1663770 (NSF)

Key Points:
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- A semi-physical global modeling approach is used to estimate diffuse & discrete sources of auroral precipitation during the Galaxy15 event.

- Diffuse sources contribute 74% of the total auroral power. Discrete sources are strongly driven by activity and can contribute up to 61%.

- Broadband precipitation contributes 31% of the auroral Pedersen conductance playing a significant role in ionospheric electrodynamics.

Research Abstract:
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The accurate determination of auroral precipitation in global models has remained a daunting and rather inexplicable obstacle. Understanding the calculation and balance of multiple sources that constitute the aurora, and their eventual conversion into ionospheric electrical conductance, is critical for improved prediction of space weather events. In this study, we present a semi-physical global modeling approach that characterizes contributions by four types of precipitation - monoenergetic, broadband, electron and ion diffuse - to ionospheric electrodynamics. The model uses a combination of adiabatic kinetic theory and loss parameters derived from historical energy flux patterns to estimate auroral precipitation from magnetohydrodynamic (MHD) quantities. It then converts them into ionospheric conductance that is used to compute the ionospheric feedback to the magnetosphere. The model has been employed to simulate the April 5 - 7, 2010 "Galaxy15" space weather event. Comparison of auroral fluxes show good agreement with observational datasets like NOAA-DMSP and OVATION Prime. The study shows a dominant contribution by electron diffuse precipitation, accounting for ~74% of the auroral energy flux. However, contributions by monoenergetic and broadband sources dominate during times of active upstream conditions, providing for up to 61% of the total hemispheric power. The study also indicates a dominant role played by broadband precipitation in ionospheric electrodynamics which accounts for ~31% of the Pedersen conductance.

Methodology:
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The data are simulation setup and results from space weather simulations conducted using the Space Weather Modeling Framework (SWMF) driven with multiple ionospheric conductance models. The dataset is primarily intended to support publication(s) conducted using the novel MAGNetosphere-Ionosphere-Thermosphere (MAGNIT) auroral precipitation model.

Files contained here:
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All simulation files from SWMF-MAGNIT simulations, along with simulations driven using empirical models RLM and CMEE are contained in this deposit.

Gun-zipped files ending with '_IE.gz' contain simulation files pertaining to the ionospheric electrodynamics of the model (zipped IDL files). Each gun-zipped file (MAGNIT_Gal15_IE.gz, RLM_Gal15_IE.gz and CMEE_Gal15_IE.gz) contain numerous 2-D ionospheric shell files (MLT, MLat, plasma parameters) that present the state of the ionosphere during a given time instance. Each folder is designated for each conductance model used, e.g. MAGNIT_Gal15_IE.gz contains ionospheric files for simulations driven using MAGNIT.

The file LCF_Runs.gz contain values from simulation runs conducted for deducing flux multipliers for each source in MAGNIT. Please refer to the README.txt file therein directory for more information.

Lastly, gun-zipped file 'GM_Vars.gz' contains log files and geo-index files forall SWMF simulations. The file is sub-divided by the three conductance models - MAGNIT, CMEE and RLM. Standard SWMF output (ASCII format) is stored in each directory. Please refer to SWMF User Manual and/or SpacePy data visualization manual to visualize any given file.

HOW TO READ THE SIMULATION OUTPUT:
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Standard tools to read SWMF output are already publicly available using IDL and Python (see SpacePy/PyBats - https://spacepy.github.io/pybats.html -- 'rim.Iono(*filename)'). For information about post-processed data and further comparisons to observations, please contact the authors for details.

Related Publications:
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Main Publication: Mukhopadhyay, A., et al. (2022). Global Driving of Auroral Precipitation - 1. Balance of Sources, Journal of Geophsyical Research - Space Physics. DOI Forthcoming.

For information about the Space Weather Modeling Framework, please refer to the following publications and weblinks:
1. Center for Space Environment Modeling (CSEM) - http://csem.engin.umich.edu/
2. Toth et al. (2005). Space Weather Modeling Framework: A new tool for the space science community. J. Geophys. Res., 110, A12226, doi:10.1029/2005JA011126.
3. Toth et al. (2012). Adaptive numerical algorithms in space weather modeling, Journal of Computational Physics, Volume 231, Issue 3, https://doi.org/10.1016/j.jcp.2011.02.006.

For information about the Ridley Legacy Model (RLM), please refer to the following publication:
1. Ridley, A. J., Gombosi, T. I., and DeZeeuw, D. L.: Ionospheric control of the magnetosphere: conductance, Ann. Geophys., 22, 567–584, https://doi.org/10.5194/angeo-22-567-2004, 2004.

For information about the Conductance Model for Extreme Events (CMEE) and Oval Adjustments, please refer to the following publication:
1. Mukhopadhyay, A., et al. (2020). Conductance Model for Extreme Events : Impact of Auroral Conductance on Space Weather Forecasts. .

Use and Access:
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This data set is made available under an Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0).

To Cite this Data:
Agnit Mukhopadhyay. Data Pertaining to Initial Simulations Using the MAGNetosphere-Ionosphere-Thermosphere (MAGNIT) Auroral Precipitation Model [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/rtmh-x457