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Abstract Measurements from the Ionospheric Connections Explorer satellite (ICON) form the basis of direct numerical forecast simulations of plasma convective instability in the postsunset equatorialFregion ionosphere. ICON data are selected and used to initialize and force the simulations and then to test the results one orbit later when the satellite revisits the same longitude. Data from the IVM plasma density and drifts instrument and the MIGHTI red‐line thermospheric winds instrument are used to force the simulation. Data from IVM are also used to test for irregularities (electrically polarized plasma depletions). Fourteen datasets from late March 2022, were examined. The simulations correctly predicted the occurrence or non‐occurrence of irregularities 12 times while producing one false positive and one false negative. This demonstrates that the important telltales of instability are present in the ICON state variables and that the important mechanisms for irregularity formation are captured by the simulation code. Possible refinements to the forecast strategy are discussed.
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Predicting Equatorial Ionospheric Convective Instability Using Machine Learning
Abstract The numerical forecast methods used to predict ionospheric convective plasma instabilities associated with Equatorial Spread‐F(ESF) have limited accuracy and are often computationally expensive. We test whether it is possible to bypass first‐principle numeric simulations and forecast irregularities using machine learning models. The data are obtained from the incoherent scatter radar at the Jicamarca Radio Observatory located in Lima, Peru. Our models map vertical plasma drifts, time, and solar activity to the occurrence and location of clusters of echoes telltale of ionospheric irregularities. Our results show that these models are capable of identifying the predictive power of the tested inputs, obtaining accuracies around 75%.
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- Award ID(s):
- 2213849
- PAR ID:
- 10478218
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Space Weather
- Volume:
- 21
- Issue:
- 12
- ISSN:
- 1542-7390
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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