Abstract
With the availability of fast computing machines, as well as the advancement of machine learning techniques and Big Data algorithms, the development of a more sophisticated total electron content (TEC) model featuring the Nighttime Winter Anomaly (NWA) and other effects is possible and is presented here. The NWA is visible in the Northern Hemisphere for the American sector and in the Southern Hemisphere for the Asian longitude sector under solar minimum conditions. During the NWA, the mean ionization level is found to be higher in the winter nights compared to the summer nights. The approach proposed here is a fully connected neural network (NN) model trained with Global Ionosphere Maps (GIMs) data from the last two solar cycles. The day of year, universal time, geographic longitude, geomagnetic latitude, solar zenith angle, and solar activity proxy, F10.7, were used as the input parameters for the model. The model was tested with independent TEC datasets from the years 2015 and 2020, representing high solar activity (HSA) and low solar activity (LSA) conditions. Our investigation shows that the root mean squared (RMS) deviations are in the order of 6 and 2.5 TEC units during HSA and LSA period, respectively. Additionally, NN model results were compared with another model, the Neustrelitz TEC Model (NTCM). We found that the neural network model outperformed the NTCM by approximately 1 TEC unit. More importantly, the NN model can reproduce the evolution of the NWA effect during low solar activity, whereas the NTCM model cannot reproduce such effect in the TEC variation.
Highlights
The Earth’s ionosphere is a medium that contains electrically charged particles
We found that the mean residuals were approximately equal for both models for the high solar activity (HSA) in 2015, whereas the standard deviation (STD) residual was about 1.2 TEC Units (TECU) less for the neural network (NN) model
2020, both mean and STD values were about 1 TECU less for the NN model compared to the Neustrelitz TEC Model (NTCM)
Summary
The ionosphere is ionized by solar radiation, and its state is constantly varying due to everchanging space weather conditions. In the ionosphere there exists enough ionization to affect the propagation of radio waves [1] Depending on the state of the ionosphere, radio waves experience a delay in their signal. The propagation delay is directly proportional to the ionospheric TEC, which increases with an increase in electron density. The ionosphere is a dispersive medium, meaning that the delay depends on the frequency of the radio waves. Utilizing this property, ionospheric delay can be corrected by combining two or more GNSS signals. Single frequency users need external information or ionospheric models to correct this propagation effect
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