Retrieval and analysis of precipitable water vapour based on GNSS, AIRS, and reanalysis models over Nigeria

Isioye, Olalekan Adekunle; Combrinck, Ludwig; Botai, Joel Ongego

doi:10.1080/01431161.2017.1346401

Cited by 10 publications

(11 citation statements)

References 67 publications

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“…According to [47,49], the climate of Nigeria is largely influenced by three major atmospheric phenomena, namely: the maritime tropical (mT) air mass, the continental tropical (cT) air mass and the equatorial easterlies. The hot, dry continental air masses originating from the high-pressure system above the Sahara Desert give rise to dusty Harmattan winds over the Sahel and Savannah Region of West Africa from November to February, while in summer, moist equatorial air masses originating over the Atlantic Ocean bring annual monsoon rains [43].…”

Section: The Climate Of Nigeriamentioning

confidence: 99%

Monitoring atmospheric water vapour variability over Nigeria from ERA-Interim and NCEP reanalysis data

Ojigi

Opaluwa

2019

SN Appl. Sci.

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The spatial and temporal variability of water vapour in the atmosphere influences the earth weather, climate system, quality of spatial positioning and radio waves propagation of communications signals amongst others. It is therefore imperative to periodically monitor and map the water vapour phenomenon over specific areas of interest across the globe. This study therefore investigates the time-series variability of the atmospheric water vapour contents (AWVC) over Nigeria from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim (ERA-I) and National Centre for Environmental Prediction/National Centre for Atmospheric Research (NCEP/NCAR) reanalysis data. The 2007-2017 daily/monthly mean data sets of ERA-I and NCEP/NCAR were visualised and extracted with Panoply version 4.8.6 and MATLAB 2018b for the 37 states capital in Nigeria. The months of minimum and maximum AWVC for all sampled locations were determined and compared, and the best fit trend equations for six cities (one city from each of the six geopolitical zones) were developed. The monthly means of AWVC over the study area showed spatial heterogeneity trend. The latitudinal variations in both ERA-I and NCEP/NCAR data sets showed that AWVC over Nigeria increases as latitude decreases towards the equator, and vice versa, irrespective of the month or time of the year. The study showed that May-September and November-February of 2007-2017 represent the periods with highest and lowest values of AWVC over Nigeria, respectively, which are the expected wet and dry seasons in the study area, and with peak months of August and January, respectively. The linear regression of the ERA-I and NCEP/NCAR data sets gave a coefficient of correlation of about 96.37%, coefficient of determination (R 2) of about 92.9% and a coefficient of efficiency of 87.83%, which indicate that ERA-I and NCEP/NCAR data sets have close values and the relationship between them in estimating AWVC over any selected location is statistically significant and valid. The coefficient of efficiency (E) of about 87.8% shows high level of internal efficiency of the ERA-I and NCEP/NCAR data sets used in this study. The best line of fit from polynomial models showed a range of the R 2 results for the best line-of-fit determination ranging between 78.36% and 95.75% for ERA-I, and 81.24% and 94.13% for NCEP/NCAR. The models and time-series spatial maps of AWVC produced in the study are recommended for use in the empirical estimation of AWVC and validations of other independent water vapour retrieval solutions such as GNSS and aerospace radiometry over the study area.

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Section: The Climate Of Nigeriamentioning

confidence: 99%

Monitoring atmospheric water vapour variability over Nigeria from ERA-Interim and NCEP reanalysis data

Ojigi

Opaluwa

2019

SN Appl. Sci.

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“…Second, we utilized the GPS meteorology concept. This extracts the precipitable water vapour (PWV) from the GPS-ZTD estimates given that the atmospheric temperature and pressure at the ABUZ station is known (Isioye et al, 2017). Finally, we utilized literatures which indicated that water vapour takes out aerosol from the atmosphere that usually occur as a result of particulate matter (PM) floating in the air/gas or dissolved in water.…”

Section: Data Analysis Approachmentioning

confidence: 99%

“…Properties of the ABUZ station and the matching AWOS station from which meteorological parameters were interpolated to the actual station level (modified afterIsioye et al, 2017) …”

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confidence: 99%

Appraising the effects of atmospheric aerosols and ground particulates concentrations on GPS-derived PWV estimates

Aliyu

Botai

2018

Atmospheric Environment

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In 2016, three Nigerian cities were listed amongst the World's most polluted in terms of particulate matter (PM). Acknowledging Nigeria's limited resources for outdoor air pollution monitoring, this study attempts to investigate the effects on atmospheric aerosol optical depth and ground PM on GPS derived-precipitable water vapour estimates. The study utilized available GPSderived precipitable water vapour (GPSPWV), the moderate resolution imaging spectroradiometer aerosol optical depth (MODISAOD) and the ground level particulate matter of less than 10 microns (GPM) datasets for December 2015 -November 2016. All the datasets were tested for normality. To evaluate the atmospheric aerosol properties, the MODISPWV estimates were pre-validated using the GPSPWV measurements. The results revealed GPSPWV-MODISPWV agreement (R = 0.964; RMSE = 3.810 mm). The GPSPWV-MODISAOD analysis showed relationship (R ≤ -0.636; RMSE ≤ 0.563) for the atmospheric aerosol experiment, while the collocating GPSPWV-GPM seasonal analysis also revealed significant correlation (R < -0.660). The correlation of combined seasonal datasets for the GPSPWV-MODISAOD and GPSPWV-GPM relationships showed high negative correlation values of 0.79 and 0.68 respectively. The findings of this study is in agreement with similar related studies, as well as serve as future position accuracy for similar related studies.

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“…e results showed the promising potential of GNSS for meteorological applications in Algeria. Isioye et al [27] likewise investigated the performance of GNSS-derived PWV with PWV retrieved from Atmospheric Infrared Sounder (AIRS) and ERA-Interim data from Nigeria. e results showed that GNSS-PWV provides a useful source of humidity information which can be utilised to improve the water cycle in numeric weather models (NWMs) for various applications in Nigeria.…”

Section: Introductionmentioning

confidence: 99%

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

Osah

Acheampong

Fosu

et al. 2021

Advances in Meteorology

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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 (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. 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, 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. 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.

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Retrieval and analysis of precipitable water vapour based on GNSS, AIRS, and reanalysis models over Nigeria

Cited by 10 publications

References 67 publications

Monitoring atmospheric water vapour variability over Nigeria from ERA-Interim and NCEP reanalysis data

Monitoring atmospheric water vapour variability over Nigeria from ERA-Interim and NCEP reanalysis data

Appraising the effects of atmospheric aerosols and ground particulates concentrations on GPS-derived PWV estimates

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

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