Assessment of the performance of the TOPGNSS and ANN-MB antennas for ionospheric measurements using low-cost u-blox GNSS receivers

Low-cost GNSS receivers have recently been gaining reliability as good candidates for ionospheric studies. In line with these gains are genuine concerns about improving the performance of these receivers. In this work, we present a comprehensive investigation of the performances of two antennas(the u-blox ANN-MB and the TOPGNSS TOP-106) used on a low-cost GNSS receiver known as the u-blox ZED-F9P. The two antennas were installed on two identical and co-located u-blox receivers. Data used from both receivers cover the period from January to June 2022. Results from the study indicate that the signal strengths are dominantly greater for the receiver with the TOPGNSS antenna than for the receiver with the ANN-MB antenna, implying that the TOPGNSS antenna is better than the ANN-MB antenna in terms of providing greater signal strengths. Summarily, the TOPGNSS antenna also performed better in minimizing the occurrence of cycle slips on phase TEC measurements. There are no conspicuous differences between the variances(computed as 5-min standard deviations) of phase TEC measurements for the two antennas, except for a period around May-June when the TOPGNSS gave a better performance in terms of minimizing the variances in phase TEC. Remarkably, the ANN-MB antenna gave a better performance than the TOPGNSS antenna in terms of minimizing the variances in pseudorange TEC for some satellite observations. For precise horizontal(North and East) positioning, the receiver with the TOPGNSS antenna gave better results, while the receiver with the ANN-MB antenna gave better vertical(Up) positioning. The errors for the receivers of both antennas are typically within about 5 m(the monthly mean was usually smaller than 1 m) in the horizontal direction and within about 10 m(the monthly mean was usually smaller than 4 m) in the vertical direction.

An application of artificial intelligence for investigating the effect of COVID-19 lockdown on three-dimensional temperature variation in equatorial Africa

We present interesting application of artificial intelligence for investigating effect of the COVID-19 lockdown on 3-dimensional temperature variation across Nigeria(2°-15°E,4°-14°N),in equatorial Africa.Artificial neural networks were trained to learn time-series temperature variation patterns using radio occultation measurements of atmospheric temperature from the Constellation Observing System for Meteorology,Ionosphere,and Climate(COSMIC).Data used for training,validation and testing of the neural networks covered period prior to the lockdown.There was also an investigation into the viability of solar activity indicator(represented by the sunspot number)as an input for the process.The results indicated that including the sunspot number as an input for the training did not improve the network prediction accuracy.The trained network was then used to predict values for the lockdown period.Since the network was trained using pre-lockdown dataset,predictions from the network are regarded as expected temperatures,should there have been no lockdown.By comparing with the actual COSMIC measurements during the lockdown period,effects of the lockdown on atmospheric temperatures were deduced.In overall,the mean altitudinal temperatures rose by about 1.1℃ above expected values during the lockdown.An altitudinal breakdown,at 1 km resolution,reveals that the values were typically below0.5℃ at most of the altitudes,but exceeded 1℃ at 28 and 29 km altitudes.The temperatures were also observed to drop below expected values at altitudes of 0-2 km,and 17-20 km.