Advancing Ionospheric Irregularity Reconstruction With ICON/MIGHTI Wind-Driven Insights

Abstract

In the mesosphere-lower thermosphere region, atmospheric plasma components exhibit short-term enhancements, forming sporadic E (Es) layers that impact communication systems. The prevailing theory posits that neutral wind shear is the primary driver of mid-latitude Es layer. Here, we present neutral wind field data from the ICON/Michelson Interferometer for Global High-resolution Thermospheric Imaging mission during 2019–2022, revealing a clear relationship between wind shear and Es layer formation in the Northern Hemisphere. Notably, the vertical ion divergence/convergence significantly impact mid-latitude Es production. Inspired by deep learning techniques, we developed a deep learning model based on wind shear and neutral wind data, reconstructing the small-scale morphology of Es layers. Vertical ion convergence information derived from the wind shear physical equations was found to be a key factor in enhancing model performance. Our results demonstrate that incorporating physical data from vertical ion drift improves the predictive capabilities of ionospheric irregularities artificial intelligence models, increasing the accuracy from 71.6% to 87.9%.