Understanding the Present-Day Spatiotemporal Variability of Precipitable Water Vapor over Ethiopia: A Comparative Study between ERA5 and GPS

Abstract

Atmospheric water vapor plays a crucial role in atmospheric, climate change, meteorological, and hydrological processes. In a country like Ethiopia, with its complex topography and synoptic-scale spatiotemporal circulation patterns, the analysis of the spatiotemporal variability of precipitable water vapor (PWV) is very challenging, and is hampered by the lack of long observational datasets. In this study, we process the PWV over eight Ethiopian global positioning system (GPS) sites and one close to the Ethiopian eastern border, for the available common period 2013-2020, and compare with the PWV retrieved from the state-of-the-art ERA5 reanalysis. Both PWV datasets agree very well at our sample, with correlation coefficients between 0.96 and 0.99, GPS-PWV show a moderate wet bias compared to ERA5-PWV for the majority of the sites, and an overall root mean square error of 3.4 mm. Seasonal and diurnal cycles are also well captured by these datasets. The seasonal variations of PWV and precipitation at the sites agree very well. Maximum diurnal PWV amplitudes are observed for stations near water bodies or dense vegetation, such as Arbaminch (ARMI) and Bahir Dar (BDMT). At those stations, the PWV behavior at heavy rainfall events has been investigated and an average 25% increase (resp. decrease) from 12 h before (resp. 12 h after) the start of the rainfall event, when the PWV peaks, has been observed.

Precipitable water vapor in regional climate models over Ethiopia: model evaluation and climate projections

“…Figure 12a shows that PWV over Ethiopia increases by 7.7% for each degree warming consistent with the Clausius-Clapeyron scaling. This result is therefore in line with several works based on large-scale (Mears et al 2007, Mieruch et al 2014, Chen et al 2016, Schröder et al, 2016, Wang et al, 2016, Parracho et al, 2018, Allan et al 2022) and more local assessments (Zhao et al, 2019, Van Malderen et al, 2022 for observations and GCMs.…”

“…Such events are typically preceded by an increase in low-level humidity followed by an increase in upper-tropospheric humidity over a wide region. Similar findings were recently confirmed using GPS data (Priego et al 2017;Sapucci et al 2018;Xiong et al 2021;Koji et al 2022).…”

“…The GPS data were processed using the GAMIT GPS software v10.71 (Herring et al 2018) in ionosphere-free double-difference mode to estimate the Zenith tropospheric Total Delay (ZTD), from which the PWV is derived. Details on this analysis can be found in Koji et al (2022). The GPS-PWV observation data are used to validate the annual cycle of the CORDEX evaluation run model.…”

“…ERA5 (Hersbach et al 2020), is the most recent reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) and its predecessor being ERA-Interim (Dee et al 2011). Koji et al (2022) compared the current-day variations of ERA5 PWV over Ethiopia with PWV data estimated from GPS measurements yielding correlation coefficients between 96% and 99%, a moderately wet bias and an overall Root Mean Squared Error (RMSE) of 3.4 mm. Given this high degree of agreement, and given its large spatiotemporal availability, ERA5 is taken here as the reference dataset to validate the PWV CORDEX simulations over Ethiopia.…”

Editorial for the Special Issue ″Climate Modelling and Monitoring Using GNSS″

“…Those papers can be arranged in major groups. Several papers [1][2][3] discuss different aspects in dealing with the estimation of long-term GNSS-derived water vapor trends and intercomparisons with external sources and NWP models. Other papers [4] use GNSS-estimated tropospheric parameters to evaluate NWP models and use these parameters for building ZTD climatological [5][6][7] or precipitation [8,9] models.…”