Abstract We introduce a new framework called Machine Learning (ML) based Auroral Ionospheric electrodynamics Model (ML‐AIM). ML‐AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents of Kunduri et al. (2020,https://doi.org/10.1029/2020JA027908), the FAC‐derived auroral conductance model of Robinson et al. (2020,https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen and Brekke (1993,https://doi.org/10.1029/92gl02109). The ML‐AIM inputs are 60‐min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pedersen/Hall currents, and Joule Heating. We conduct two ML‐AIM simulations for a weak geomagnetic activity interval on 14 May 2013 and a geomagnetic storm on 7–8 September 2017. ML‐AIM produces physically accurate ionospheric potential patterns such as the two‐cell convection pattern and the enhancement of electric potentials during active times. The cross polar cap potentials (ΦPC) from ML‐AIM, the Weimer (2005,https://doi.org/10.1029/2004ja010884) model, and the Super Dual Auroral Radar Network (SuperDARN) data‐assimilated potentials, are compared to the ones from 3204 polar crossings of the Defense Meteorological Satellite Program F17 satellite, showing better performance of ML‐AIM than others. ML‐AIM is unique and innovative because it predicts ionospheric responses to the time‐varying solar wind and geomagnetic conditions, while the other traditional empirical models like Weimer (2005,https://doi.org/10.1029/2004ja010884) designed to provide a quasi‐static ionospheric condition under quasi‐steady solar wind/IMF conditions. Plans are underway to improve ML‐AIM performance by including a fully ML network of models of aurora precipitation and ionospheric conductance, targeting its characterization of geomagnetically active times.
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A Fast Analytical Model for the Complete Radial Structure of Tropical Cyclone Low‐Level Wind Field
Abstract A tropical cyclone (TC) can generally be divided into three regions: inner core with vigorous convection, intermediate region with intermittent convection, and far outer region with less convective activity. The different physics in these three regions suggest correspondingly different wind structure models. In this study, we combine the inner‐core wind model from Tao et al. (2023,https://doi.org/10.1029/2023gl104583, T23), the outer wind model from Emanuel (2004,https://texmex.mit.edu/pub/emanuel/PAPERS/Energetics_Structure.pdf, E04), and a transition model of a modified Rankine vortex to create a new fast and analytical model for the complete radial structure of the TC wind field. The T23 model captures inner‐core wind variation with small errors, while the E04 model reproduces the broad outer wind structure at large radii well. The new wind model combines the strengths from both T23 and E04 models without the need for statistical fitting, showing great potential in reproducing the full range of simulated and observed TC winds.
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- PAR ID:
- 10660077
- Publisher / Repository:
- American Geophysical Union
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 53
- Issue:
- 1
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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