Atmospheric water vapour, a major component in weather systems serves as the main source for precipitation, provides latent heat which helps maintain the earth’s energy balance and a major parameter in Numerical Weather Prediction (NWP) models. An observational technique based on the Global Navigation Satellite System (GNSS) has made it possible to easily retrieve Precipitable Water (PW) at station’s antenna position with very high spatial and temporal variabilities. GNSS techniques are superior to ground-based and balloons sensors in terms of accuracy, ease of use, wider coverage and easier assimilation into NWP models. This study sought to use prediction models using daily observational data from Four (4) International GNSS Service stations in West Africa. The best prediction model can be used in cases of station outages and to predict PW over data poor regions using computed Zenith Tropospheric Delays (ZTD). gLAB software was used to process the stations’ data in Precise Point Positioning mode and PW were retrieved using station’s temperature and pressure values. Computed PW were compared against Total Column Water Vapour from ERA-Interim Reanalysis data in 2016. Correlation coefficient (R²) values ranging from 0.947 — 0.995 were obtained for the four stations. With computed PW’s, three regression models were tested to find the best-fit with PW as the dependent variable and ZTD being the independent variable. The quadratic model gave the highest R² and lowest RMSE values as against the linear and exponential models. Time series forecasts models such as moving average, autoregressive, exponential smoothing and autoregressive integrated moving average were also employed. The forecasts results were compared against ZTD with autoregressive model reporting the highest R² and lowest RMSE amongst the forecast models developed.

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. The 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 (r²), refined index of agreement (IA_r), NashSutcliffe 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. The 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, r², NSE, IA_r, and FAC2 of 0.38cm, 0.87cm, 1.11cm, 0.988, 0.976, 0.967, 0.992, and 1.00 (100), respectively. This 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.

Over the years, the Weather Research and Forecasting Model (WRF) has been gaining popularity as a low-cost alternative source of data for wind resource assessments. This paper investigates the impact of selected time control, and nudging options on wind simulations in WRF. We conducted 15 numerical experiments, combining 5 simulation run-times and 3 options for disabling nudging in the Planetary Boundary Layer (PBL) in WRF. Hourly wind speed and direction predictions were compared with actual measurements at 40 m, 50 m and 60 m a.g.l. From our results, we recommend that, for optimum performance, the method of disabling nudging in the PBL should be chosen with simulation run times in mind. For wind simulations in our study area, up to 2 days run-times with nudging disabled below 1600 m in model configurations gives the best wind speed predictions. However, disabling nudging below the model-calculated PBL height offers more consistent results and produces relatively less prediction error with longer run times.

Signals from Global Navigational Satellite Systems (GNSS) when integrated with surface meteorological parameters can be used to sense atmospheric water vapour. Using gLAB software and employing precise point positioning techniques, zenith troposphere delays (ZTD) for a GPS base station at KNUST, Kumasi have been computed and used to retrieve Precipitable Water (PW). The PW values obtained were compared with products from ERA-Interim and NCEP reanalysis data. The correlation coefficients, r, determined from these comparisons were 0.839 and 0.729 for ERA-interim and NCEP respectively. This study has demonstrated that water vapour can be retrieved with high precision from GNSS signal. Furthermore, a location map have been produced to serve as a guide in adopting and installing GNSS base stations in Ghana to achieve a country wide coverage of GNSS based water vapour monitoring.

Precipitable water comparisons over Ghana using PPP Techniques and reanalysis data

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.