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Filtering by: Creator Mukhopadhyay, Agnit Keyword Nonlinear Regression Language Python

Data Pertaining to Initial Simulations Using the Conductance Model for Extreme Events

Creator:

Mukhopadhyay, Agnit, Daniel T Welling, Michael W Liemohn, Aaron J Ridley, Shibaji Chakrabarty, and Brian J Anderson

Description:

An updated auroral conductance module is built for global models, using nonlinear regression & empirical adjustments to span extreme events., Expanded dataset raises the ceiling of conductance values, impacting the ionospheric potential dB/dt & dB predictions during extreme events., and Application of the expanded model with empirical adjustments refines the conductance pattern, and improves dB/dt predictions significantly.

Keyword:

Space Weather Forecasting, Extreme Weather, Ionosphere, Magnetosphere, MI Coupling, Ionospheric Conductance, Auroral Conductance, Aurora, SWMF, SWPC, Nonlinear Regression, and dB/dt

Citation to related publication:

Mukhopadhyay, A., Welling, D. T., Liemohn, M. W., Ridley, A. J., Chakraborty, S., & Anderson, B. J. (2020). Conductance Model for Extreme Events: Impact of Auroral Conductance on Space Weather Forecasts. Space Weather, 18(11), e2020SW002551. https://doi.org/10.1029/2020SW002551

Discipline:

Engineering and Science