Conductance Model for Extreme Events: Impact of Auroral Conductance on Space Weather Forecasts

• Published in: Space Weather
• Main Authors: Mukhopadhyay, Agnit, Welling, Daniel T., Liemohn, Michael W., Ridley, Aaron J., Chakraborty, Shibaji, Anderson, Brian J.
• Other Authors: Electrical and Computer Engineering
• Format: Text
• Language: English
• Published: 2020
• Subjects: ionospheric conductance; Canada; Morley; Pedersen; Ridley; Toth; Yellowknife
• Online Access: http://hdl.handle.net/10919/102370; https://doi.org/10.1029/2020SW002551

Ionospheric conductance is a crucial factor in regulating the closure of magnetospheric field-aligned currents through the ionosphere as Hall and Pedersen currents. Despite its importance in predictive investigations of the magnetosphere-ionosphere coupling, the estimation of ionospheric conductance in the auroral region is precarious in most global first-principles-based models. This impreciseness in estimating the auroral conductance impedes both our understanding and predictive capabilities of the magnetosphere-ionosphere system during extreme space weather events. In this article, we address this concern, with the development of an advanced Conductance Model for Extreme Events (CMEE) that estimates the auroral conductance from field-aligned current values. CMEE has been developed using nonlinear regression over a year's worth of 1-min resolution output from assimilative maps, specifically including times of extreme driving of the solar wind-magnetosphere-ionosphere system. The model also includes provisions to enhance the conductance in the aurora using additional adjustments to refine the auroral oval. CMEE has been incorporated within the Ridley Ionosphere Model (RIM) of the Space Weather Modeling Framework (SWMF) for usage in space weather simulations. This paper compares performance of CMEE against the existing conductance model in RIM, through a validation process for six space weather events. The performance analysis indicates overall improvement in the ionospheric feedback to ground-based space weather forecasts. Specifically, the model is able to improve the prediction of ionospheric currents, which impact the simulated dB/dt and Delta B, resulting in substantial improvements in dB/dt predictive skill. NASANational Aeronautics & Space Administration (NASA) [NNX17AB87G, 80NSSC18K1120, 80NSSC17K0015]; NSFNational Science Foundation (NSF) [1663770]; National Science FoundationNational Science Foundation (NSF) Support for this work has been provided by NASA Grants NNX17AB87G, 80NSSC18K1120, and 80NSSC17K0015, and NSF Grant 1663770. We would like to acknowledge high-performance computing support from Pleaides (allocation 1815) provided by NASA's High-End Computing Capability Programme, and Cheyenne (allocation UUSL0016) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. The authors thank NASA Community Coordinated Modeling Center (CCMC) Staff and INTERMAGNET (https://intermagnet.github.io/) for providing the magnetometer measurements. The authors would also like to thank the Geomagnetism Unit of the Geological Survey of Canada (GSC) and the U.S. Geological Survey (USGS); Jeffrey J. Love for maintaining magnetometer measurements at Yellowknife and Newport respectively for public usage. The authors would like to thank Dr. Meghan Burleigh for reading a draft manuscript. We thank Dr. Shasha Zou, Dr. Robert Robinson, Dr. Steven Morley, and Dr. Gabor Toth for sharing their expertise in the course of this study. A. M. would like to thank Dr. Dogacan su Ozturk, Dr. Zhenguang Huang, Dr. Natalia Ganjushkina, Ms. Abigail Azari, Mr. Alexander Shane, Mr. Brian Swiger, and Mr. Christopher Bert for sharing their expertise during the development of modeling, curve-fitting, and validation tools used in this study.