A quantitative comparison of high latitude electric field models during a large geomagnetic storm

Orr, L.; Grocott, A.; Walach, M.‐T.; Chisham, G. ORCID:; Freeman, M.P. ORCID:; Lam, M.M. ORCID:; Shore, R.M. ORCID: 2023 A quantitative comparison of high latitude electric field models during a large geomagnetic storm. Space Weather, 21 (1), e2022SW003301. 24, pp.

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Models of the high-latitude ionospheric electric field are commonly used to specify the magnetospheric forcing in thermosphere or whole atmosphere models. The use of decades-old models based on spacecraft data is still widespread. Currently the Heelis (Heelis et al., 1982) and Weimer (Weimer, 2005b) climatology models are most commonly used but it is possible a more recent electric field model could improve forecasting functionality. Modern electric field models, derived from radar data, have been developed to incorporate advances in data availability (Thomas & Shepherd, 2018; Walach et al., 2022; Bristow et al., 2022). It is expected that climatologies based on this larger and up-to-date dataset will better represent the high latitude ionosphere and improve forecasting abilities. An example of two such models, which have been developed using line-of-sight velocity measurements from the Super Dual Auroral Radar Network (SuperDARN) are the Thomas and Shepherd model (TS18) (Thomas & Shepherd, 2018), and Walach and Grocott geomagnetic Storm model (WGS21) (Walach et al., 2021). Here we compare the outputs of these electric field models during the September 2017 storm, covering a range of solar wind and interplanetary magnetic field (IMF) conditions. We explore the relationships between the IMF conditions and the model output parameters such as transpolar voltage, the polar cap size and the lower latitude boundary of convection. We find that the electric potential and field parameters from the spacecraft-based models have a significantly higher magnitude than the SuperDARN-based models. We discuss the similarities and differences in topology and magnitude for each model.

Item Type: Publication - Article
Digital Object Identifier (DOI):
ISSN: 1542-7390
Date made live: 22 Dec 2022 15:38 +0 (UTC)

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