Estimation of Precipitable Water Vapour in Nigeria Using NIGNET GNSS/GPS, NCEP-DOE Reanalysis II and Surface Meteorological Data

Abimbola, O. J.; Falaiye, O A; Omojola, Joseph

doi:10.21315/jps2017.28.2.2

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…In Nigeria, the study of Abimbola et al, (2017) found a weak correlation between GNSS PWV of some NIGNET stations operated and maintained by the Office of the Surveyor General of the Federation (OSGOF), and the NCEP/ DOE Reanalysis II -the time series of each of the PWV sources exhibited an identical pattern. Lack of meteorological sensors attached to NIGNET and the poor resolution of NCEP/DOE was attributed to the weak correlation.…”

Section: Introductionmentioning

confidence: 99%

An Appraisal of the Ecmwf Reanalysis5 (Era5) Model in Estimating and Monitoring Atmospheric Water Vapour Variability Over Nigeria

Bawa

Isioye

Moses

et al. 2022

Geodesy and Cartography

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This study research the performance of the ERA5 reanalysis model in estimating and monitoring the variability of atmospheric water vapour content over Nigeria. The ERA5 is a fifth-generation reanalysis model recently released by the European Centre for Medium-Range Weather Forecasts (ECMWF). The ERA5 model comes with excitingly high spatial and temporal resolution when compared to earlier models like the ERA-Interim and ERA-40. However, like the previous models, the ERA5 comes with numerous modelling uncertainties arising from data fusion methods and observation schemes, which often affects its performance at the different regions of the Earth. In this study, ERA5 precipitable water vapour (PWV) was validated with GNSS PWV from permanent GNSS stations in Nigeria NIGNET for the period of 2012–2013. The performance of ERA5 was investigated at sub-daily, diurnal, and seasonal scales in relation to KöppenGeiger climate classification using standard statistical metrics (namely, coefficient of correlation (r), Root mean square error (RMSE), Reliability index (RI), Mean absolute errors (MAE) and mean bias). The r, RI, RMSE, MAE and mean bias values at sub-daily, diurnal and seasonal scales were computed as, (0.8670, 0.882, 0.979), (3.697 mm, 3.400 mm, 7.014 mm), (1.015, 1.019, 1.008), (2.769 mm, 2.706 mm, 1.939 mm) and (0.826 mm, 2.033 mm, 1.739 mm), respectively. The results indicate the strongest performance of ERA5 at seasonal scale with more than 95% agreement. The pattern of variability of ERA5 within the different climate zones of Nigeria showed good consistency with GNSS PWV and Köppen-Geiger climate classification. The study recommended the use of ERA5 in the retrieval of historic PWV records and near real-time GNSS applications.

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Section: Introductionmentioning

confidence: 99%

An Appraisal of the Ecmwf Reanalysis5 (Era5) Model in Estimating and Monitoring Atmospheric Water Vapour Variability Over Nigeria

Bawa

Isioye

Moses

et al. 2022

Geodesy and Cartography

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“…Bevis et al [1] used 8700 radiosonde profiles from 13 stations in the United States of America (USA) to obtain a statistical model of the form T m = aT s + b. The Bevis et al [1] model was adopted in many studies (e.g., Raju et al [2]; Fernandez et al [3]; Musa et al [4]; Abimbola et al [5]) across the globe despite the fact that it was obtained for the USA region. Mendes et al [6] and Solbrig [8], using radiosonde data, obtained similar linear relation for Germany, Raju et al [2] for Indian sub-continent, Shoji [9] for Japan and Isioye et al [10] for West Africa.…”

Section: Introductionmentioning

confidence: 99%

“…T m = 0.65T s + 86.9 (5) the aim of this work to find a suitable statistical model for the evaluation of atmospheric mean temperature T m in West African region.…”

Section: Introductionmentioning

confidence: 99%

Statistical modelling of atmospheric mean temperature in sub-Sahel West Africa

2020

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Atmospheric mean temperature T m , is a vital parameter in the evaluation of precipitable water vapor (PWV) through the analysis of GPS signal, it is therefore important to have a good way of evaluation of Tm for the eventual accurate evaluation of PWV using GPS. Simple statistical models exist for various regions of the world for the evaluation of T m using surface temperature T s , in the form T m = aT s + b where a and b are constants. For West Africa, where atmospheric data is usually very scarce, there is a minimal attempt at finding a statistical model for T m , as a function of T s . In this work attempt has been made to find such a model using data from the Climate Monitoring Satellite Facilities (CM-SAF) of the European Meteorological Satellites (EUMETSAT). The model derived was found to compare well with that obtained using radiosonde data with root-mean-square error of 1.189 and mean-biased error of 0.0952 between the two models.

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Accuracy assessment of reanalysis datasets for GPS-PWV estimation using Indian IGS stations observations

Srivastava

2022

Geocarto International

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Estimation of Precipitable Water Vapour in Nigeria Using NIGNET GNSS/GPS, NCEP-DOE Reanalysis II and Surface Meteorological Data

Cited by 5 publications

References 11 publications

An Appraisal of the Ecmwf Reanalysis5 (Era5) Model in Estimating and Monitoring Atmospheric Water Vapour Variability Over Nigeria

An Appraisal of the Ecmwf Reanalysis5 (Era5) Model in Estimating and Monitoring Atmospheric Water Vapour Variability Over Nigeria

Statistical modelling of atmospheric mean temperature in sub-Sahel West Africa

Accuracy assessment of reanalysis datasets for GPS-PWV estimation using Indian IGS stations observations

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