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.

Geomagnetic Field Measurement over Akure Using Ground Based Magnetometer Observatory

The geomagnetic field over Akure, southern Nigeria （7°15＇ N; 5°12＇ E） was investigated from direct observation for a period of two years （2005-2006）. Geomagnetic field over Akure was monitored and measured using a locally produced magnetometer. The geomagnetic data generated were evaluated at every local time hour. Diurnal, monthly and Seasonal effects were investigated. The results of the analyses from the magnetic data generated provide a comprehensive understanding of the geomagnetic variation over the region. This research validates the instrument and presents a direct measurement opportunity to capture the influence of local sources.

A regional GNSS-VTEC model over Nigeria using neural networks： A novel approach

A neural network model of the Global Navigation Satellite System - vertical total electron content （GNSS-VTEC） over Nigeria is developed. A new approach that has been utilized in this work is the consideration of the International Reference Ionosphere＇s （IRI＇s） critical plasma frequency （foF2） parameter as an additional neuron for the network＇s input layer. The work also explores the effects of using various other input layer neurons like distur- bance storm time （DST） and sunspot number. All available GNSS data from the Nigerian Permanent GNSS Network （NIGNET） were used, and these cover the period from 2011 to 2015, for 14 stations. Asides increasing the learning accuracy of the networks, the inclusion of the IRI＇s foF2 parameter as an input neuron is ideal for making the networks to learn long-term solar cycle variations. This is important especially for regions, like in this work, where the GNSS data is available for less than the period of a solar cycle. The neural network model developed in this work has been tested for time-varying and spatial per- formances. The latest 10% of the GNSS observations from each of the stations were used to test the forecasting ability of the networks, while data from 2 of the stations were entirely used for spatial performance testing. The results show that root-mean-squared-errors were generally less than 8.5 TEC units for all modes of testing performed using the optimal network. When compared to other models, the model developed in this work was observed to reduce the prediction errors to about half those of the NeQuick and the IRI model.

The Asymmetrical Behaviour of the SqH Current System during the Prenoon-Postnoon Epochs at African Longitudes

This study examines the prenoon-postnoon asymmetrical behaviour and latitudinal dependence of Sq （solar quiet） current system using data of quiet-time daily variations of the geomagnetic field intensity from twelve geomagnetic observatories along the African Meridian. The dataset of each month during 2009 （noted for empirically low solar activity with average sunspot number Rz = 3.1） was treated for non-cyclic correction. From a blend of spatial contour maps and graphical analyses, our results show that Sq current system exhibits in the daytime unstable tendency. A consistent diurnal variation of solar quiet variation in the horizontal component of earth magnetic field （SqH） was observed which exhibits synoptic pre-noon and post-noon mean values of 59 nT and 33 nT with ranges of 33 nT and 24 nT, respectively. The centre of circulation of overhead electric current is observed to exhibit both pre-noon and post-noon epoch＇s asymmetric variations. This is noted to indicate the dynamic heterogeneous genesis of the mechanism responsible for the observation. The spatial contour mapping result depicts SqH behaviour switch twice a year around March and September with similar spatial distribution in January up to March and then October up to December. A similar distribution was noted for the months of April to September. Prenoon values of SqH have higher magnitudes across the latitudes in comparison with the post noon values just as is the case at noontime.