Observations of precipitable water vapour over complex topography of Ethiopia from ground-based GPS, FTIR, radiosonde and ERA-Interim reanalysis

Abstract

Abstract. Water vapour is one of the most important greenhouse gases. Long-term changes in the amount of water vapour in the atmosphere need to be monitored not only for its direct role as a greenhouse gas but also because of its role in amplifying other feedbacks such as clouds and albedo. In recent decades, monitoring of water vapour on a regular and continuous basis has become possible as a result of the steady increase in the number of deployed global positioning satellite (GPS) ground-based receivers. However, the Horn of Africa remained a data-void region in this regard until recently, when some GPS ground-receiver stations were deployed to monitor tectonic movements in the Great Rift Valley. This study seizes this opportunity and the installation of a Fourier transform infrared spectrometer (FTIR) at Addis Ababa to assess the quality and comparability of precipitable water vapour (PWV) from GPS, FTIR, radiosonde and interim ECMWF Re-Analysis (ERA-Interim) over Ethiopia. The PWV from the three instruments and the reanalysis show good correlation, with correlation coefficients in the range from 0.83 to 0.92. On average, GPS shows the highest PWV followed by FTIR and radiosonde observations. ERAInterim is higher than all measurements with a bias of 4.6 mm compared to GPS. The intercomparison between GPS and ERA-Interim was extended to seven other GPS stations in the country. Only four out of eight GPS stations included simultaneous surface pressure observations. Uncertainty in the model surface pressure of 1 hPa can cause up to 0.35 mm error in GPS PWV. The gain obtained from using observed surface pressure in terms of reducing bias and strengthening correlation is significant but shows some variations among the GPS sites. The comparison between GPS and ERA-Interim PWV over the seven other GPS stations shows differences in the magnitude and sign of bias of ERA-Interim with respect to GPS PWV from station to station. This feature is also prevalent in diurnal and seasonal variabilities. The spatial variation in the relationship between the two data sets is partly linked to variation in the skill of the European Centre for Medium-Range Weather Forecasts (ECMWF) model over different regions and seasons. This weakness in the model is related to poor observational constraints from this part of the globe and sensitivity of its convection scheme to orography and land surface features. This is consistent with observed wet bias over some highland stations and dry bias over few lowland stations. The skill of ECMWF in reproducing realistic PWV varies with time of the day and season, showing large positive bias during warm and wet summer at most of the GPS sites.

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

“…The descriptive revealed good GPSPWV-MODISPWV agreement at surface level (R = 0.96; RMSE = 3.81). This MODISPWV validation is within the range of acceptable results, when compared to similar research findings on PWV validation (Tsidu et al, 2015;Gui et al, 2017b) but slightly lower than Ningombam et al, (2016). From Table 2, we can also conclude that the computed alpha reliability, correlation coefficient, root mean square error and bias, were within the range of PWV estimation with GPS positioning.…”

Addition of a channel for XCO observations to a portable FTIR spectrometer for greenhouse gas measurements

“…PROFFIT is in wide use in the NDACC (e.g. Sepúlveda et al, 2014;Virolainen et al, 2014;Mengistu Tsidu et al, 2015), is in very good agreement with the official TCCON analysis (Dohe, 2013), and is also successfully applied for the analysis of spectra recorded with the EM27/SUN (Gisi et al, 2012;Frey et al, 2015). For the analysis of the low-resolution spectra, the methane, water vapour, HDO, and CO a priori profiles were adopted from the TC-CON processor.…”

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

“…This is corroborated by Mengistu et al, who stated that the surface pressure of 1 hPa can cause up to 0.35 mm error in GPS PWV. 15 The spatial variation of PWV as modelled by NCEP Reanalysis 2 is shown in Figure 6 for four given days within the year 2012. The 16 January figure indicates that most of the entire country is dry as the tropical continental air mass (cT) blowing in from the Sahara Desert predominates at this time.…”