Application of GNSS derived precipitable water vapour prediction in West Africa

Abstract

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.

Regression models for predicting daily IGS zenith tropospheric delays in West Africa: Implication for GNSS meteorology and positioning applications

“…Similar findings have also been obtained by Ahmadi and Rodehutscord (2017) and Parveen et al (2016Parveen et al ( , 2017. Furthermore, the performance of the QR model over the MLR model is also consistent with the study carried out by Acheampong and Obeng (2019). In the study, the authors estimated the amount of PWV over four selected IGS ground stations in West Africa using QR, MLR, and exponential regression (ER) models.…”

“…Moreover, if the under-studied IGS tracking stations' ZTD data are inaccessible or absent, the best prediction model can as well be utilized as an alternative model to predict the missing ZTD values as a means of improving continuous data availability at the stations. These models were selected following Clements et al (2005), Sayegh et al ( 2014)), Parveen et al (2016Parveen et al ( , 2017Parveen et al ( , 2020, Acheampong and Obeng (2019)), and Zhang et al (2019), as these models have been commonly used in other areas of study.…”

“…Regression analyses, however, have been proven useful and effective in solving many problems in several fields of studies for modelling and making predictions. For instance, In the field of health, for the prediction of lung cancer rate (Clements et al, 2005), heart disease (Polaraju & Prasad, 2017), and CO 2 emissions (Libao et al, 2017), in the field of environmental science, for the prediction of PM 10 concentration (Ul-Saufie et al, 2011;Sayegh et al, 2014), in the field of engineering, for the prediction of deformation behaviour of concrete-face rockfill dam (Wen et al, 2021) and inflow to the Ujjani dam reservoir (Rajmane & Waikar, 2020), in the field of hydrology, for the prediction of monthly streamflows (Zhang et al, 2019), development of flood damage risk map for forecasting rainfall data (Choi, 2021), in the field of meteorology (GNSS), for the prediction of weighted mean temperature (Bevis et al, 1992;Isioye et al, 2016;Schueler et al, 2001) and PWV (Acheampong & Obeng, 2019), in ionospheric studies, for the prediction of global position system (GPS) ionospheric delay and scintillation (Aon et al, 2018;Zhang et al, 2019), and many more. As noted above, regression analysis has been a widely used statistical technique for developing prediction models across various disciplines, largely due to its ability to analyse the relationship between response and predictor variables without requiring a large number of training datasets for optimal prediction compared with ANN, which requires a considerable number of training datasets.…”

Linear regression models to predict the tropospheric parameters at the Global Positioning systems’ sites over the East African region

“…The formulation of the linear regressions between the GPS PWV and the TCWV exhibited high coefficients of determination. The high coefficients of determination of the empirical relation connecting the PWV and TCWV were also reported by Acheampong and Obeng (2019).…”

Evaluation of Zenith Tropospheric Delay Derived from Ray-Traced VMF3 Product over the West African Region Using GNSS Observations

“…e study demonstrated that PWV can be obtained with high precision from GNSS signals in Ghana. Additionally, the authors in [30] studied the application of prediction models (regression and time-series) for predicting GNSS-PWV over four IGS stations in West Africa. e study reported the quadratic and autoregression models as the most appropriate regression and time-series models, respectively, for the prediction of PWV in West Africa.…”