ERA Interim Research Articles

Evaluation of global forcing datasets for hydropower inflow simulation in Nepal

AbstractDischarge over the Narayani river catchment of Nepal was simulated using Statkraft's Hydrologic Forecasting Toolbox (Shyft) forced with observations and three global forcing datasets: (i) ERA-Interim (ERA-I), (ii) Water and Global Change (WATCH) Forcing Data ERA-I (WFDEI), and (iii) Coordinated Regional Climate Downscaling Experiment with the contributing institute Rossy Centre, Swedish Meteorological and Hydrological Institute (CORDEX-SMHI). Not only does this provide an opportunity to evaluate discharge variability and uncertainty resulting from different forcing data but also it demonstrates the capability and potential of using these global datasets in data-sparse regions. The fidelity of discharge simulation is the greatest when using observations combined with the WFDEI forcing dataset (hybrid datasets). These results demonstrate the successful application of global forcing datasets for regional catchment-scale modeling in remote regions. The results were also promising to provide insight of the interannual variability in discharge. This study showed that while large biases in precipitation can be reduced by applying a precipitation correction factor (p_corr_factor), the best result is obtained using bias-corrected forcing data as input, i.e. the WFDEI outperformed other forcing datasets. Accordingly, the WFDEI forcing dataset holds great potential for improving our understanding of the hydrology of data-sparse Himalayan regions and providing the potential for prediction. The use of CORDEX-SMHI- and ERA-I-derived data requires further validation and bias correction, particularly over the high mountain regions.

Comparative assessment of two RegCM versions in simulating Indian Summer Monsoon

In the present study, sensitivity of Indian Summer Monsoon (ISM) to the cumulus convection scheme (CCS) is assessed using Regional Climate Model (RegCM4.4.5). Seasonal scale (May–June–July–August–September) simulation of the model forced with European Centre for Medium Range Weather Forecasts reanalysis data (ERA interim) is carried out for the consecutive three monsoon years 2007, 2008 and 2009. Four major CCS (MIT, Grell, Tiedtke and Kain–Fritsch) are employed. Model simulated results are validated with various observed and reanalysis datasets. In addition, the model results are also compared with that of its earlier version (RegCM4.1). Detailed analysis reveals that the model’s ability to delineate large scale ISM features such as Heat Low, Tibetan high, Somali Jet, Tropical Easterly Jet (TEJ), Sub-tropical Westerly Jet (STWJ) are fairly well using multiple CCS options. Fairly, better model performance is identified while Grell over ocean and MIT over land (GO_ML) is used. This scheme exhibits relatively lower warm (cold) bias over entire northwest and partially central India (peninsular and other parts of India). Both lower and upper level circulation pattern including TEJ and STWJ are better simulated by GO_ML scheme. Simulated distribution of precipitation is also more realistic and closed to TRMM data using that scheme. Further comparison of results from two model versions indicates that the simulation with recent version (4.4) is more realistic than that with the earlier version (RegCM4.1). The study concludes that RegCM4.4 with GO_ML would be the optimal combination when overall performance of the model is taken into consideration.

Comparisons of Diurnal Variations of Land Surface Temperatures from Numerical Weather Prediction Analyses, Infrared Satellite Estimates and In Situ Measurements

This study evaluates the diurnal cycle of Land Surface Temperature (LST) from Numerical Weather Prediction (NWP) reanalyses (ECMWF ERA5 and ERA Interim), as well as from infrared satellite estimates (ISCCP and SEVIRI/METEOSAT), with in situ measurements. Data covering a full seasonal cycle in 2010 are studied. Careful collocations and cloud filtering are applied. We first compare the reanalysis and satellite products at continental and regional scales, and then we concentrate on comparisons with the in situ observations, under a large variety of environments. SEVIRI shows better agreement with the in situ measurements than the other products, with bias often less than ±2K and correlation of 0.99. Over snow or arid surface, ISCCP tends to have more systematic errors than the other products. ERA5 agrees better to the in situ over barren land than ERA Interim, particularly at night time, thanks to the new surface model. However, over vegetated surfaces, both reanalyses tend to have higher/lower temperature at night/day time than the in situ measurements, probably related to the surface processes and its interactions with atmosphere in the NWP model.

Influence of Enso to Variability of Rain Chain at Tropical Cyclon in Southern East Nusa Tenggara

The Region of East Nusa Tenggara province which is geographically situated around Equator is an area immediately adjacent to place where the growth of tropical cyclones. This research to determine the variability of tropical cyclones that have happened, how the influence of ENSO on the variability of tropical cyclones, and how the impact of tropical cyclones on rainfall in East Nusa Tenggara. In this study, the rainfall data used is represented by 8 stations observations in the region of NTT, namely Kupang, Rote, Sabu, Waingapu, Ruteng, Maumere, Larantuka, and Alor, with period of 19 years ie the assessment year 1996-2014. The data was obtained from Era Interim ECMWF. Tropical cyclone data was obtained from JTWC-Japan and TCWC-Australia, covering a maximum sustained wind data, position, and lifetime cyclone. Rainfall data obtained from Climatological Station of Lasiana-Kupang. Results of the analysis showed that during the study period the total incidence of tropical cyclones in the south of East Nusa Tenggara many as 113 cases, with an average of 6 events cyclones per year and were distributed mainly in the winter between November and April. The results also show that ENSO influence on tropical cyclone variability indirectly to the parameter of tropical cyclones.

Impact Analysis of Univariate and Multivariate Bias Correction on Rice Irrigation Water Needs in Jiangxi Province, China

Regional climate models (RCMs) provide an improved representation of climate information as compared to global climate models (GCMs). However, in climate-agricultural impact studies, accurate and interdependent local-scale climate variables are preferable, but both RCMs and GCMs are still subjected to bias. This study compares univariate bias correction (UBC) and multivariate bias correction (MBC) method to simulate rice irrigation water needs (IWNs) in Jiangxi Province, China. This research uses the daily output of Hadley Centre Global Environmental Model version 3 regional climate model (HadGEM3-RA) forced with ERAINT (ECMWF ERA Interim) data and 13 Jiangxi ground-based observations, and the observation data are reference data with 1989–2005 defined as a calibration period and 2006–2007 as a validation period. The result shows that UBC and MBC methods favorably bias-corrected all climate variables during the calibration period, but still no significant difference is noted between the two methods. However, the UBC ignores the relationship between climate variables, while MBC preserves the climate variables’ interdependence which affect subsequent analyses. In rice IWNs simulation analysis, MBC has better skill at correcting bias compare to UBC in ETo (evapotranspiration) and Peff (effective rainfall) components. Nonetheless, both methods have a low ability to correct extreme values bias. Overall, both techniques successfully reduce bias, even though they are still less effective for precipitation compared to maximum and minimum temperature, relative humidity and windspeed.

Assessing the variations in the chemical composition of rainwater and air masses using the zonal and meridional index

Abstract The chemical composition of rainwater was studied from January 2009 to December 2017 in the northern group of Eastern Carpathians, Romania, taking under consideration air mass trajectories, the zonal index and zonal circulations, to show the imprints of long-range transported pollutants on the precipitation chemistry. Rainwater samples were analyzed for pH, major anions and cations. The relative dominance of ions among sampling sites was quite similar, Ca2+ and SO42− being the most abundant. Neutralization was primarily brought by Ca2+, followed by NH4+, proven by the ionic ratios, neutralization factors, neutralizing and acidic potential, and the significant correlation coefficients between the sum of alkaline species (Ca2+ + Mg2+ + NH4+) and the sum of acidic species (NO3− + SO42−). Source apportionment showed the relative contributions of marine, crustal and anthropogenic sources to the total chemical composition of rainwater, indicating the significant influence of anthropogenic activities (mining, coal combustion) but the imprints of soil derived ions and sea salts can also be observed. The cluster analysis of air mass back trajectories showed that the studied region's climate and the atmospheric chemistry may have been influenced by long-range transported pollutants originated from NW and SW part of the continent. Zonal circulations and the daily zonal index using ERA Interim were constructed, demonstrating the greater influence of air masses coming from NW and SW, highlighting the polluting sources outside the European Union.

Atmospheric precursors for intense summer rainfall over the United Kingdom

AbstractIntense sub‐daily summer rainfall is linked to flooding impacts in the United Kingdom. Characterizing the atmospheric conditions prior to the rainfall event can improve understanding of the large‐scale mechanisms involved. The most intense sub‐daily rainfall intensity data generated from rain gauge records across the United Kingdom over the period 1979–2014 are combined with fields from the ERA Interim reanalysis to characterize atmospheric conditions prior to heavy rainfall events. The 200 most intense 3‐hourly events for six UK regions are associated with negative anomalies in sea level pressure (<−2 hPa) and 200 hPa geopotential height (<−60 m) to the west or south west of the United Kingdom 1 day earlier, with above average moisture, evaporation and dew point temperature over North West Europe. Atmospheric precursors are more intense but less coherent between regions for composites formed of the 25 heaviest rainfall events but all display substantial moisture transport from the south or south east prior to their occurrence. Composites for the heaviest events are characterized by a tripole geopotential anomaly pattern across the North Atlantic. Above average geopotential height and dew point temperature over Newfoundland and below average geopotential height but elevated evaporation in the North Atlantic are found to be weakly associated with an increased chance of the most intense sub‐daily rainfall events 5–9 days later.

Global Distribution of Surface Water Vapour Density Using in Situ and Reanalysis Data

Global spatial and annual distribution of surface water vapour density were estimated using 2005 -2016 monthly air temperature and relative humidity at 1° ×1° resolution obtained from Era interim and NCEP/NCAR database products. Obtained results from reanalysis were statistically tested using in situ data from Tropospheric Data Acquisition Network (TRODAN) of The Center for Atmospheric Research (CAR). Four seasonal variations of surface water vapour density (winter (DJF), spring (MAM), summer (JJA) and autumn (SON)) was examined. Observed result from the two reanalysis follow similar trends with value from Era interim leading. High values ranges between 50 g/m2 and 68 g/m2 were observed in tropical regions and humid sub-tropical regions. Low values ranges between 8 g/m2 and 38 g/m2 were observed in Ice cap, Tundra and arid regions. High warming may be experienced in tropical and sub-tropical regions, similarly, climate change with alarming rate may be experienced in locations with low values. The annual cycle of surface water vapor density is clearly established from two reanalysis across world classified into twelve regions. The statistical test for the reanalysis present good result with a mean bias error, MBE, root mean square error, RMSE and R square of 20.56, 18.29, 0.87 and 5.87, 0.98, 0.93 for Era interim and NCEP/NCAR respectively.

The Performance of Different Mapping Functions and Gradient Models in the Determination of Slant Tropospheric Delay

Global navigation satellite systems (GNSSs) have become an important tool for remotely sensing water vapor in the atmosphere. In GNSS data processing, mapping functions and gradient models are needed to map the zenith tropospheric delay (ZTD) to the slant total tropospheric delay (STD) along a signal path. Therefore, it is essential to investigate the spatial–temporal performance of various mapping functions and gradient models in the determination of STD. In this study, the STDs at nine elevations were first calculated by applying the ray-tracing method to the atmospheric European Reanalysis-Interim (ERA—Interim) dataset. These STDs were then used as the reference to study the accuracy of the STDs that determined the ZTD together with mapping functions and gradient models. The performance of three mapping functions (i.e., Niell mapping function (NMF), global mapping function (GMF), and Vienna mapping function (VMF1)) and three gradient models (i.e., Chen, MacMillan, and Meindl) in six regions (the temperate zone, Qinghai–Tibet Plateau, Equator, Sahara Desert, Amazon Rainforest, and North Pole) in determining slant tropospheric delay was investigated in this study. The results indicate that the three mapping functions have relatively similar performance above a 15° elevation, but below a 15° elevation, VMF1 clearly performed better than the GMF and NMF. The results also show that, if no gradient model is included, the root-mean-square (RMS) of the STD is smaller than 2 mm above the 30° elevation and smaller than 9 mm above the 15° elevation but shows a significant increase below the 15° elevation. For example, in the temperate zone, the RMS increases from approximately 35 mm at the 10° elevation to approximately 160 mm at the 3° elevation. The inclusion of gradient models can significantly improve the accuracy of STDs by 50%. All three gradient models performed similarly at all elevations and in all regions. The bending effect was also investigated, and the results indicate that the tropospheric delay caused by the bending effect is normally below 13 mm above a 15° elevation, but this delay increases dramatically from approximately 40 mm at a 10° elevation to approximately 200 mm at a 5° elevation, and even reaches 500–700 mm at a 3° elevation in most studied regions.

Analysis of Rainfall Dynamics in Conakry, Republic of Guinea

Observed rainfall data of the National Meteorological Service of Guinea (NMS) exhibit that synoptic station usually records the largest rainfall amount in Guinea. Only few studies have been done on this rainfall peak observed in Conakry. This work better analyses the atmospheric dynamics leading to rainfall particularity. Using NMS data from 1981 to 2010, the monthly contribution and mean seasonal cycle of each station has been done. These findings of the study show that between July and August (rainfall season peak), the coastline particularly Conakry records the largest amount of rainfall. Using Era Interim data for the common period (1981-2010), we also investigate the rainfall dynamics in the lower level (1000 hPa - 850 hPa) from precipitable water, divergence, and moisture flow transport. There is a west and southwest moisture flow transport explained by a strong moisture convergence in the coastal region (Lower-Guinea). Furthermore, values of precipitable water in the same region are found, in agreement with the high moisture flow transport gradient. These incoming flow (west and south-west) undergo a return by blocking’s Kakoulima range (foehn effect) and Fouta Djallon massif to initiate convection clouds on the Guinean coast. These processes enhance a convergence of moisture associated with orographic origin convection. This has an important effect by increasing the rainfall amount in Conakry.

Synoptic characteristics of extreme heatwaves over the Korean Peninsula based on ERA Interim reanalysis data

AbstractThe characteristics of heatwaves (HWs) in South Korea are studied using data from the European Centre for Medium‐Range Weather Forecasts Reanalysis Interim (ERA Interim) dataset and from automatic surface weather stations. The synoptic conditions of three specific years (1994, 2013, and 2016) during which serious HWs affected South Korea are examined. HWs in South Korea are related to the northwestwardly extension of the western North Pacific subtropical high (WNPSH) toward the Korean Peninsula. Examination of the blocking indices revealed widespread blocking over the northern Pacific Ocean and Asia during South Korean HWs, and blocking is related to stationary weather patterns. The severe temperatures associated with HWs in this region are due to prolonged subsidence. Analysis of the moist static energy (MSE) budget indicated that the energy source of subsidence is derived from both MSE advection and the net heat flux. When compared to the synoptic situation during an HW in South Korea, the relative southward movement of the WNPSH is found before and after the HW. The blocking indices also revealed weak signals and changes in vertical motion due to MSE advection.

An evaluation of COSMO‐CLM regional climate model in simulating precipitation over Central Africa

AbstractIn this study, an analysis of present day climate simulation (1998–2008) is presented for the Central African (CA) region with the COnsortium for Small‐scale MOdelling in CLimate Mode (CCLM) regional climate model, forced by the ERA‐Interim (ERAINT) reanalysis data. The ability of the CCLM to simulate the observed precipitation with particular focus on the mean spatial pattern, low‐level circulation, seasonal cycles, and daily characteristics is evaluated. Likewise, the added value of the regional model CCLM compared to the driving ERAINT reanalysis is also investigated. It is shown that ERAINT and CCLM exhibit quite different sign of bias, which is an indication of the importance of internal variability and fine scale processes representation for the simulation of surface climate. Despite the CCLM is constantly dry over southern CA, the model succeeds to reproduce reasonably the mean spatial patterns of precipitation and low‐level circulation features, along with the associated seasonal cycles over the whole CA and majority of the five selected analysis sub‐regions. Results also show that daily precipitation indices are well represented, although the better performance greatly depends on the considered seasons. Nevertheless, CCLM substantially outperforms the ERAINT daily precipitation characteristics, thus highlighting the added value of the downscaling exercise over the region. The analysis of daily precipitation indices also reveals that the dry character of the model could probably be connected to the underestimation of the simulated less intense events, which in turn result to an overestimation of the simulated dry spell duration.

Temperature Field Data Reconstruction Using the Sparse Low-Rank Matrix Completion Method

Due to limited number of weather stations and interruption of data collection, the temperature field data may be incomplete. In the past, spatial interpolation is usually used for filling the data gap. However, the interpolation method does not work well for the case of the large-scale data loss. Matrix completion has emerged very recently and provides a global optimization for temperature field data reconstruction. A recovery method is proposed for improving the accuracy of temperature field data by using sparse low-rank matrix completion (SLR-MC). The method is tested using continuous gridded data provided by ERA Interim and the station temperature data provided by Jiangxi Meteorological Bureau. Experimental results show that the average signal-to-noise ratio can be increased by 12.5%, and the average reconstruction error is reduced by 29.3% compared with the matrix completion (MC) method.

Climate Extremes over the Arabian Peninsula Using RegCM4 for Present Conditions Forced by Several CMIP5 Models

This paper investigates the temperature and precipitation extremes over the Arabian Peninsula using data from the regional climate model RegCM4 forced by three Coupled Model Intercomparison Project Phase 5 (CMIP5) models and ERA–Interim reanalysis data. Indices of extremes are calculated using daily temperature and precipitation data at 27 meteorological stations located across Saudi Arabia in line with the suggested procedure from the Expert Team on Climate Change Detection and Indices (ETCCDI) for the present climate (1986–2005) using 1981–2000 as the reference period. The results show that RegCM4 accurately captures the main features of temperature extremes found in surface observations. The results also show that RegCM4 with the CLM land–surface scheme performs better in the simulation of precipitation and minimum temperature, while the BATS scheme is better than CLM in simulating maximum temperature. Among the three CMIP5 models, the two best performing models are found to accurately reproduce the observations in calculating the extreme indices, while the other is not so successful. The reason for the good performance by these two models is that they successfully capture the circulation patterns and the humidity fields, which in turn influence the temperature and precipitation patterns that determine the extremes over the study region.

Simulation of the influence of the ocean and the El-Niño – Southern Oscillation phenomenon on the structure and composition of the atmosphere

The influence of El-Niño and La-Niña on the Arctic stratosphere is studied. Sea surface temperature (SST), potential vorticity, air temperature, ozone mixing ratio, and column ozone ERA Interim data are analyzed for 1997, 1999, and 2016. It is shown that El-Niño leads to a series of sudden stratospheric warmings and, consequently, to instability of the polar vortex and to an increase in column ozone, whereas La-Niña leads to a stable polar vortex and low temperatures in the stratosphere and, therefore, to a decrease in column ozone. The influence of SST and CO2 levels on the air temperature in the troposphere and stratosphere, and ozone content from 1980 to 2015 is also studied. It is shown that there is warming of the ocean and the troposphere, as well as cooling of the stratosphere. It is also shown that the changes in air temperature in the troposphere are mainly due to changes in SST, whereas for the stratosphere the impact of CO2 variability prevails. It is also shown that ozone content varies little.

Interannual variability of wind stress curl in the Black Sea and its response to changes in prevailing wind frequency

The purpose of this study is to identify trends in the long-term variability of the wind stress curl in the Black Sea and to analyze its response to changes in the direction of the prevailing wind flow over the sea. NCEP/NCAR reanalysis (1948–2016) and ERA Interim reanalysis (1979–2016) wind data at 10-m height are used. The temporal and spatial characteristics of the wind stress curl are compared by using these reanalyses with various spatial resolutions. Interannual changes in the wind stress curl are examined with long-term time series data of NCEP/NCAR reanalysis. The wind stress curl seasonal cycle, the dependence of its magnitude on wind direction, and the frequency of wind direction are in good agreement for the two reanalyses. There are multi-year periods of predominant positive and negative anomalies of the curl according to annual mean and winter data. The cyclonic wind stress curl increases in the late 1960s and early 1970s and weakens at the end of the 1990s. There is a weak positive trend in the curl time series in summer. The long-term variations of the wind stress curl are related to changes in the wind frequency of certain directions. High values of positive correlation coefficients have been obtained between the time series of the basin-averaged wind stress curl and the frequency of northeastern, eastern winds, and negative coefficients for the frequency of southwestern, western winds.

Atmospheric Precipitation Within the Selenga River Basin and Large-Scale Atmospheric Circulation Over Eurasia in July

Data from the Global Precipitation Climatology Centre (GPCC) were used in investigating long-term variability of anomalies of the total amount of atmospheric precipitation within the Selenga river basin in July. We identified several long periods (four years and longer) with negative precipitation anomalies: 1935–1939, 1955–1958, 1977–1981, 2007–2011, and 2013–2017. The last period is characterized by the smallest precipitation amount. Day-to-day variations in total precipitation were also analyzed for each July from 1982 to 2016. The analysis revealed a tendency to a decrease of the number of days with a large precipitation amount and to an increase of dry periods. Based on ECMWF Era Interim data, we examined the features in the atmospheric blocking Europe, Siberia and the Far East for periods with large and small precipitation amounts within the Selenga river basin. To identify the blocking in the atmosphere used the southern gradient of the geopotential. It was shown that in periods with large precipitation amounts blockings occurred most frequently over Eastern Siberia and more rarely over Europe and the Far East (especially if they were accompanied by blocking over Western Siberia). Atmospheric precipitation within the Selenga basin is associated not only with blocking but also with the position of the jet stream and with dynamic blocking formation schemes. The significance of these factors has been demonstrated in a number of specific situations of the atmospheric circulation in years with large and small precipitation amounts within the Selenga basin. Long-term transformations of these characteristics caused by climate change and the general atmospheric circulation can lead to a disturbance of the correlations between blocking and precipitation amounts within the river basin. In all likelihood, the correlation of blocking with precipitation regimes, rather than with precipitation in the region, will be more rigid. A functional dependence of the Selenga flow on climate change appears to be determined by the position of the monsoon front and by the wave properties of atmospheric flows in midlatitudes.

A Comparison Between Ray-Traced GFS/WRF/ERA and GNSS Slant Path Delays in Tropical Cyclone Meranti

Tropical cyclone (TCs) prediction has improved in the last decade, but studying their structure and dynamics remains a challenging task due to limited in situ observations. The global navigation satellite system (GNSS) technique enables retrieving the state of the troposphere with high accuracy in all weather conditions. In this article, we use GNSS slant total delays (STDs) to verify the quality of the weather models during the passage of the TC Meranti, which was the strongest TC in 2016. STDs observed from 28 GNSS stations in Taiwan are compared with the STDs reconstructed from three meteorological data sets: the weather research and forecasting (WRF) model, the global forecast system (GFS), and the ERA-Interim (ERA) using a 2-D ray-tracing technique. Furthermore, two strategies were tested—including and excluding the delays due to liquid and ice water content. The results reveal a good consistency between the GNSS and the ray-traced STDs. The best agreement was found for the WRF, with a mean difference of −0.5 mm and a standard deviation of 29.4 mm when the hydrometeor delay was included. The mean hydrometeor contribution reached up to 2.8 mm for the WRF, while for the ERA and GFS, the contribution to the total delay was negligible. The recorded absolute percent differences were greater for the northern HENC station (mostly within 0.8%) than for the southern GOLI station (on average below 0.5%) due to a closer location to the passing Meranti. The most significant biases were seen in the ERA (around 7%) and the GFS (exceeding −3%) for the PLIM station, located in the central mountain range valley.

Regional HYSPLIT simulation of atmospheric transport and deposition of the Chernobyl 137Cs releases

In this work we report the results of HYSPLIT numerical simulations of the Chernobyl 137Cs atmospheric transport, dispersion, and deposition on the regional scale (~1000 km from the source) with the main focus on the analysis of the deposition processes. In the simulations we used three different gridded datasets of input meteorology and three release scenarios previously published in the literature. The resistance method and the predefined constant value of the deposition velocity (0.005 m/s) were applied to calculate dry depositions whereas an approach based on a scavenging coefficient was used for both wet in-cloud and below-cloud removals. The results were statistically evaluated against the measurements of 137Cs total depositions on the territory of Ukraine. Our simulations show considerable dependence of the HYSPLIT-predicted accumulated deposition pattern on both the input meteorology and source parameterizations. The best performance of HYSPLIT was obtained with the ERA Interim reanalysis data and the source model of Talerko (2005) and the constant deposition velocity. This simulation reproduced fairly well the spatial structure of the 137Cs contamination on the territory of Ukraine with good evaluation statistics. However, not all significant local maxima of the contamination pattern were captured clearly. Our simulations also show that dry removal processes account for approximately 50% of the total depositions in Ukraine. Both wet in-cloud and below-cloud removal mechanisms had roughly equal influence on the total amount of 137Cs radionuclides deposited on the territory of Ukraine.

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

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 (R2) 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 R2 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.