Abstract
We characterize the spatial–temporal variability of integrated water vapor (IWV) in Ethiopia from a network of global positioning system (GPS) stations and the European Center for Medium range Weather Forecasting (ECMWF) model. The IWV computed from the ECMWF model is integrated from the height of the GPS stations on 60 pressure levels to take both the actual earth surface and the model orography discrepancies into account. First, we compare the IWV estimated from GPS and from the model. The bias varies from site to site, and the correlation coefficients between the two datasets exceed 0.85 at different time scales. The results of this study show that the general ECMWF IWV trend is underestimation over highlands and overestimation over lowlands for wet periods, and overestimation over highlands and underestimation over lowlands for dry periods with very few exceptional stations. Second, we observe the spatial variation of the IWV. High values are obtained in those stations that are located in the north-eastern (Afar depression) sites and the south-western part of the country. This distribution is related to the spatial variability of the climate in Ethiopia. Finally, we study the seasonal cycle and inter-annual variability of IWV for all stations over Ethiopia. The main result is the strong inter-annual variability observed for the dry seasons.
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Acknowledgments
We thank all the participants of the measurement campaigns and the teams involved in making the data available. We are particularly grateful to our colleagues from the Institute of Geophysics, Space Sciences and Astronomy from the Addis Ababa University and from the Centre d’Etudes et de Recherches Djiboutiennes and the Observatoire Géophysique d’Arta (www.oga.dj). Thanks in particular to Patrice Ulrich. This work was supported by the CNES-TOSCA and by the Agence Nationale pours la Recherche, in the framework of the Project DoRA ANR-09-JCJC-0051-01.
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Abraha, K.E., Lewi, E., Masson, F. et al. Spatial–temporal variations of water vapor content over Ethiopia: a study using GPS observations and the ECMWF model. GPS Solut 21, 89–99 (2017). https://doi.org/10.1007/s10291-015-0508-7
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DOI: https://doi.org/10.1007/s10291-015-0508-7