The Development of Spatial Attention U-Net for The Recovery of Ionospheric Measurements and The Extraction of Ionospheric Parameters
This work addresses the extraction of ionospheric parameters from noisy data for atmospheric science applications, representing an incremental improvement.
The authors tackled the problem of recovering ionospheric signals from noisy radar data using a Spatial Attention U-Net model, achieving identification of F2 and E layer modes with a peak difference in critical frequencies of 0.63 MHz and an uncertainty of 0.18 MHz.
We train a deep learning artificial neural network model, Spatial Attention U-Net to recover useful ionospheric signals from noisy ionogram data measured by Hualien's Vertical Incidence Pulsed Ionospheric Radar. Our results show that the model can well identify F2 layer ordinary and extraordinary modes (F2o, F2x) and the combined signals of the E layer (ordinary and extraordinary modes and sporadic Es). The model is also capable of identifying some signals that were not labeled. The performance of the model can be significantly degraded by insufficient number of samples in the data set. From the recovered signals, we determine the critical frequencies of F2o and F2x and the intersection frequency between the two signals. The difference between the two critical frequencies is peaking at 0.63 MHz, with the uncertainty being 0.18 MHz.