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

Abstract

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.

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

“…This is as against stations RECT, FUTA and UNEC which depict varying peaks from mid-May to October. Stations CLBR, RUST and ULAG located relatively very close to the coast of Nigeria exhibited very high PWV between 42-62 kg/m 2 (or mm, because 1 kg of water corresponds to 1 dm 3 of water) from February to December -peaks at these stations are mostly observed around the month of May at 0-9 hour (UTC)this corroborates the study of Ojigi and Opaluwa (2019).…”

“…Several numerical weather models (NWMs) of different generations from the European Centre for Medium-Range Weather Forecasts (ECMWF) and National Centre for Environmental Prediction (NCEP)/National Centre for Atmospheric Research (NCAR) have evolved over the years. The NWMs are based on prediction models and the integration of data from different sources (Ojigi & Opaluwa, 2019;Zhang et al, 2019). This, as a result, makes such models erroneous due to variation of the models, fusion method and observation schemes (Ssenyunzi et al, 2020;Yang et al, 2019).…”

“…This, as a result, makes such models erroneous due to variation of the models, fusion method and observation schemes (Ssenyunzi et al, 2020;Yang et al, 2019). The accuracy of the models vary from one region to another, thus, there is a need to validate their performance over the different region (Ansari et al, 2018;Isioye et al, 2017;Ojigi & Opaluwa, 2019;Xiaoming et al, 2010;Yang et al, 2019). Although, GNSS-based PWV is rarely used in NWM reanalysis data models, but most often used in validations.…”

“…Out of the 15 NIG-NET stations across Nigeria, Isioye et al (2017) used five and recorded a strong interrelation among GNSS, AIRS and ERA-Interim based models. In another study by Ojigi and Opaluwa (2019) NCEP/DOE and ERAI were compared against each other and more than 90% correlation was recorded.…”