Prediction of the thermospheric and ionospheric responses to the 21 June 2020 annular solar eclipse

On 21 June 2020,an annular solar eclipse will traverse the low latitudes from Africa to Southeast Asia.The highest latitude of the maximum eclipse obscuration is approximately 30°.This low-latitude solar eclipse provides a unique and unprecedented opportunity to explore the impact of the eclipse on the low-latitude ionosphere–thermosphere(I–T)system,especially in the equatorial ionization anomaly region.In this study,we describe a quantitative prediction of the impact of this upcoming solar eclipse on the I–T system by using Thermosphere–Ionosphere–Electrodynamics General Circulation Model simulations.A prominent total electron content(TEC)enhancement of around 2 TEC units occurs in the equatorial ionization anomaly region even when this region is still in the shadow of the eclipse.This TEC enhancement lasts for nearly 4.5 hours,long after the solar eclipse has ended.Further model control simulations indicate that the TEC increase is mainly caused by the eclipse-induced transequatorial plasma transport associated with northward neutral wind perturbations,which result from eclipse-induced pressure gradient changes.The results illustrate that the effect of the solar eclipse on the I–T system is not transient and linear but should be considered a dynamically and energetically coupled system.

A long-range forecasting model for the thermosphere based on the intelligent optimized particle filtering

The uncertainties associated with the variations in the thermosphere are responsible for the inaccurate prediction of the orbit decay of low Earth orbiting space objects due to the drag force.Accurate forecasting of the thermosphere is urgently required to avoid satellite collisions,which is a potential threat to the rapid growth of spacecraft applications.However,owing to the imperfections in the physics-based forecast model,the long-range forecast of the thermosphere is still primitive even if the accurate prediction of the external forcing is achieved.In this study,we constructed a novel methodology to forecast the thermosphere for tens of days by specifying the uncertain parameters in a physics-based model using an intelligent optimized particle filtering algorithm.A comparison of the results suggested that this method has the capability of providing a more reliable forecast with more than 30-days leading time for the thermospheric mass density than the existing ones under both weak and severe disturbed conditions,if solar and geomagnetic forcing is known.Moreover,the accurate estimation of the state of thermosphere based on this technique would further contribute to the understanding of the temporal and spatial evolution of the upper atmosphere.