Positioning, based on GNSS reference network technology, is becoming a routine operation within and outside the spatial industry. The expanding user base and diverse range of applications employing this technology can impose significant expectations on the providers of reference network services. In positioning and navigation, the requirement for high accurate coordinate estimates cannot be over-emphasized. This is ensured by the provision of accurate and reliable corrections from the zero-order GNSS reference stations. It is therefore expedient to study the diurnal coordinates of such stations to guarantee reliable information for positioning and navigation applications. In this study, observation data from the Nigerian permanent GNSS continuously operating reference stations located at different states around Nigeria was processed. The hourly and diurnal (daily) coordinate solutions obtained were analysed for the purpose of monitoring the short-term stability of the network coordinates using a two-year (2012-2013) test data. The daily precise point positioning results were processed, analysed, and presented as coordinate time series using RTKPLOT. Python programming language was used to write custom modules to visualize the time series graphs at 30 seconds epochs in order to determine points and epochs where and when the condition for stability defaulted. The stations; FPNO, GEMB, and MDGR were found to be most stable in the Easting component; GEMB and MDGR were the most stable in the Northing component while in the Up component the station GEMB was the most stable. The outcome of the study will assist in detecting stations that are non-operational, performing diurnal PPP processing to detect stations that are unstable, and reporting reference stations that experience sudden coordinate changes. The developed monitoring module can be implemented by the reference stations operators as an automated program for setting up an intelligent alert system to trigger a warning whenever there is unexpected coordinate breach.
Geospatial and multi-criteria decision-making techniques are applied to process and analyse datasets for determining suitable areas for multiple utilityscale solar photovoltaic farms in the city of Akure, Ondo State, southwestern Nigeria. Data processed include local electric power distribution system data, Shuttle Radar Topographic Mission elevation data, Landsat 8 and solar global horizontal irradiance. Multi-criteria decision-making techniques adopted are the analytical hierarchy process, reclassification, and overlay. These techniques were carried out considering criteria for siting solar photovoltaic farms. Criteria considered in this study are climate, topography, economic, environmental impact operational and technical while sub-criteria are solar global horizontal irradiance, slope, proximity and land cover. The outcome of the study shows that the study area covering a total extent of ~33,200 ha, 15%, 8%, 13% and 64% are highly suitable, suitable, moderately suitable, and unsuitable respectively for siting utility-scale solar photovoltaic farms within the study area. The study reveals the potential of multiple utility-scale solar photovoltaic farms in the study area. However, the proportions of areas suitable for solar photovoltaic farms are quite lower compared to findings from similar studies conducted in northwestern Nigeria. The study recommends solar photovoltaic sources as an alternative energy source in and around the study area.
Tropospheric delay is a major error caused by atmospheric refraction in Global Navigation Satellite System (GNSS) positioning. The study evaluates the potential of the European Centre for Medium-range Weather Forecast (ECMWF) Reanalysis 5 (ERA5) atmospheric variables in estimating the Zenith Tropospheric Delay (ZTD). Linear regression models (LRM) are applied to estimate ZTD with the ERA5 atmospheric variables. The ZTD are also estimated using standard ZTD models based on ERA5 and Global Pressure and Temperature 3 (GPT3) atmospheric variables. These ZTD estimates are evaluated using the data collected from the permanent GNSS continuously operating reference stations in the Nigerian region. The results reveal that the Zenith Hydrostatic Delay (ZHD) from the LRM and the Saastamoinien model using ERA5 surface pressure are of identical accuracy, having a Root Mean Square (RMS) error of 2.3 mm while the GPT3-ZHD has an RMS of 3.4 mm. For the Zenith Wet Delay (ZWD) component, the best estimates are derived using ERA5 Precipitable Water Vapour (PWV). These include the ZWD derived by the LRM having an average RMS of 20.9 mm and Bevis equation having RMS of 21.1 mm and 21.0 mm for global and local weighted mean temperatures, respectively. The evaluation of GPT3-ZWD estimates gives RMS of 45.8 mm. This study has provided a valuable insight into the application of ERA5 data for ZTD estimation. In line with the findings of the study, the ERA5 atmospheric variables are recommended for improving the accuracy in ZTD estimation, required for GNSS positioning.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.