Browsing by Author "Fosu, Collins"

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    Comparative Evaluation and Analysis of Different Tropospheric Delay Models in Ghana
    (South African Journal of Geomatics, 2021-08) Osah, Samuel; Acheampong, Akwasi Afrifa; Dadzie, Isaac; Fosu, Collins; 0000-0003-1640-6307
    Tropospheric delay prediction models have become increasingly important in Global Navigation Satellite System (GNSS) as they play a critical role in GNSS positioning applications. Due to the different atmospheric conditions over the earth regions, tropospheric effect on GNSS signals also differs, influencing the performance of these prediction models. Thus, the choice of a particular prediction model can significantly degrade the positioning accuracy especially when the model does not suit the user’s environs. Therefore, a performance assessment of existing prediction models in various regions for a suitable one is very imperative. This paper evaluates and analyses seven commonly used tropospheric delay models in Ghana in terms of performances in Zenith Tropospheric Delay (ZTD) estimation and baseline positional accuracies using data from six selected Continuously Operating Reference Stations (CORS). The 1˚x1˚ gridded Vienna Mapping Functions 3 (VMF3) ZTD product and coordinates solutions from the CSRS-PPP positioning service were respectively used as references. The results show that the Black model performed better in estimating the ZTD, followed by Askne and Nordius model. The Saastamoinen, Marini and Murray, Niell, Goads and Goodman and Hopfield models respectively performed poorly. However, the result of the baseline solutions did not show much variation in the coordinate difference provided by the use of the prediction models, nonetheless, the Black and Askne and Nordius models continue to dominate the other models. Of all the models evaluated, either Black or Askne and Nordius model is recommended for use to mitigate the ZTD in the study area, however, the choice of the Black model will be more desirable.
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    Deep learning model for predicting daily IGS zenith tropospheric delays in West Africa using TensorFlow and Keras
    (Advances in Space Research, 2021-08) Osah, Samuel; Acheampong, Akwasi Afrifa; Fosu, Collins; Dadzie, Isaac; 0000-0003-1640-6307
    Accessibility and precise modelling of tropospheric delay play a significant role in the precise Global Navigation satellite system (GNSS) positioning applications as well as meteorological studies and weather forecasting. However, if in the event that a GNSS Continuously Operating Reference Station (CORS) is inaccessible due to power outages, poor internet connectivity, equipment failure, and firmware issues, gaps are created in the data archive, and the quality of the tropospheric delay estimation is degraded. Over the years, several modelling approaches and methodologies have been proposed towards the precise estimation of tropospheric delay, owing to the spatiotemporal variability of water vapour content in the atmosphere. This study employs Deep learning (DL) approach with TensorFlow and Keras to develop a predictive model (DLztd) for predicting daily IGS final ZTDs over four selected IGS stations in West Africa. Daily surface meteorological parameters (Pressure (P), Temperature (T), and Water vapour partial pressure (e)), as well as daily ZTD and stations’ coordinates (latitude, and ellipsoidal height) obtained from the site-wise VMF3-ZTD products for the period 2015–2018, were used as input variables to train and test the model, while data from 2019 were used to evaluate the predictive performance of the developed model. Statistical performance indicators such as Mean Bias (MB), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), coefficient of determination (R2), Nash-Sutcliffe coefficient of Efficiency (NSE), and the fraction of prediction within a Factor of Two (FAC2) were employed to determine the degree of agreement between the DLztd model predictions and IGS final ZTD data. The results from the various analyses indicate exceptionally good prediction capability of the DLztd model with average MB, RMSE, MAPE, R2, NSE and FAC2 of 3.25 mm, 9.62 mm, 0.30%, 0.959, 0.947, and 1.00 respectively. This demonstrates that the DLztd model provides a remarkable alternative for improving the availability of the ZTD data over the IGS stations under study should the stations' data be inaccessible or unavailable.
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    Evaluation of Zenith Tropospheric Delay Derived from Ray-Traced VMF3 Product over the West African Region Using GNSS Observations
    (Advances in Meteorology, 2021) Osah, Samuel; Acheampong, Akwasi Afrifa; Fosu, Collins; Dadzie, Isaac; 0000-0003-1640-6307
    The growing demand for Global Navigation Satellite System (GNSS) technology has necessitated the establishment of a vast and ever-growing network of International GNSS Service (IGS) tracking stations worldwide. &e IGS provides highly accurate and highly reliable daily time-series Zenith Tropospheric Delay (ZTD) products using data from the member sites towards the use of GNSS for precise geodetic, climatological, and meteorological applications. However, if for reasons like poor internet connectivity, equipment failure, and power outages, the IGS station is inaccessible or malfunctioning, and gaps are created in the data archive resulting in degrading the quality of the ZTD and precipitable water vapour (PWV) estimation. To address this challenge as a means of providing an alternative data source to improve the continuous availability of ZTD data and as a backup data in the event that the IGS site data are missing or unavailable in West Africa, this paper compares the sitewise operational Vienna Mapping Functions 3 (VMF3) ZTD product with the IGS final ZTD product over five IGS stations in West Africa. Eight different statistical evaluation metrics, such as the mean bias (MB), mean absolute error (MAE), root mean squared error (RMSE), Pearson correlation coefficient (r), coefficient of determination (r 2 ), refined index of agreement (IAr), Nash–Sutcliffe coefficient of efficiency (NSE), and the fraction of prediction within a factor of two (FAC2), are employed to determine the degree of agreement between the VMF3 and IGS tropospheric products. &e results show that the VMF3-ZTD product performed excellently and matches very well with the IGS final ZTD product with an average MB, MAE, RMSE, r, r 2 , NSE, IAr, and FAC2 of 0.38 cm, 0.87 cm, 1.11 cm, 0.988, 0.976, 0.967, 0.992, and 1.00 (100%), respectively. &is result is an indication that the VMF3-ZTD product is accurate enough to be used as an alternative source of ZTD data to augment the IGS final ZTD product for positioning and meteorological applications in West Africa.
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    Regression models for predicting daily IGS zenith tropospheric delays in West Africa: Implication for GNSS meteorology and positioning applications
    (Meteorological Applications, 2021) Osah, Samuel; Acheampong, Akwasi Afrifa; Fosu, Collins; Dadzie, Isaac; 0000-0003-1640-6307
    The ability to precisely and accurately model and predict tropospheric delay is essential for precise global navigation satellite system (GNSS) and meteorological applications. The International GNSS Service (IGS) provides highly accurate and highly reliable daily time series zenith tropospheric delay (ZTD) products for all its member sites using data from each IGS site. Nevertheless, if for reasons such as poor internet connectivity, equipment failure, and power outages the IGS station is inaccessible, gaps are created in the data archive, resulting in degrading the quality of the ZTD estimation, as well as inhibits the quality of precipitable water vapour (PWV) estimation, needed for precise positioning applications, meteorological studies, and weather forecasting. To address this challenge, five regression models are proposed in this study to model and predict daily ZTDs using daily datasets from four IGS stations in West Africa over a period of 5 years (2015–2019). The site-specific Vienna Mapping Functions 3 (VMF3) products (ZTD, pressure, temperature, water vapour partial pressure) and stations' coordinates (latitudes and longitudes) are used as the predictors, while the IGS final ZTD product as the response variable in fitting the models. Several performance measures are calculated to compare the predictive performance of the models. The results show that the five regression models performed outstandingly and agree very well with the IGS-ZTD data, and hence provide a useful alternative for ZTD predictions and also in the event the West African IGS stations' ZTD data are unavailable. Nonetheless, the support vector regression model outperformed the remaining four models.