A different view of the ionospheric winter anomaly

Using critical frequencies, f0F2 from the Lannion, Slough, Poitiers, Garchy, Dourbes, Rome, Juliusrud, Gibilmanna, Pruhonice, Uppsala, Kaliningrad, Miedzeszyn, Sofia, Athens and Kiev ionosonde stations, the possible effects of the orientation of the Interplanetary Magnetic Field (IMF) on mid-latitude ionosphere are further investigated. This time, only the southward polarity changes in IMF Bz with seasonal effects were considered. The same method of analysis was employed to facilitate a comparison between the recent results presented here with those which appeared in the preceding papers in the series. That is, the regular diurnal, seasonal and solar cycle variations in the f0F2 data were removed by subtracting the mean of the f0F2 for the same UT on all magnetically quite days (Ap < 6) within 15 days around the IMF Bz turnings (Tulunay, 1994). This last paper also includes the seasonal effects on the ionospheric data. The results confirm that much of the day-to-day variability of the mid-latitude ionosphere may be related to the orientation of the southward IMF Bz , characterized by the ionospheric winter anomaly. Day-to-day ionospheric variability becomes more significant towards higher latitudes.

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A Neural Network-Based TEC Model Capable of Reproducing Nighttime Winter Anomaly

Remote Sensing ◽

10.3390/rs13224559 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4559

Author(s):

Marjolijn Adolfs ◽

Mohammed Mainul Hoque

Keyword(s):

Neural Network ◽

Solar Activity ◽

Geomagnetic Latitude ◽

Machine Learning Techniques ◽

High Solar Activity ◽

Electron Content ◽

Solar Cycles ◽

Total Electron ◽

The Neural Network ◽

Winter Anomaly

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.

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