Performance of ERA5 data in retrieving Precipitable Water Vapour over East African tropical region
Title | Performance of ERA5 data in retrieving Precipitable Water Vapour over East African tropical region |
Publication Type | Journal Article |
Year of Publication | 2020 |
Authors | Ssenyunzi, RCliffe, Oruru, B, D’ujanga, F¬enceMutonyi, Realini, E, Barindelli, S, Tagliaferro, G, von Engeln, A, de Giesen, N |
Journal | Advances in Space Research |
Volume | 65 |
Issue | 8 |
Pagination | 1877-1893 |
Date Published | 15 April 2020 |
Keywords | ECMWFNWPGNSSERA5PWV |
Abstract | The accuracy of surface meteorological measurements is vital to derive accurate Global Positioning System (GPS) Precipitable Water Vapour (PWV) data. However, in absence of surface meteorological data, data from Numerical Weather Prediction Models (NWP) are used. The accuracy of these models varies depending on the model, region, season and other climatic conditions. In this study, GPS data for derivation of PWV is collected from 13 geodetic permanent stations for the years 2013 to 2016. Five out of 13 GPS stations are equipped with meteorological sensors. The interpolated European Centre for Medium-Range Weather Forecasts (ECMWF) 5th Re-Analysis (ERA5) dataset at these locations is first validated using the meteorological data from these sensors. The assessment shows that the average root mean square errors (RMSE) of surface pressure and temperature values are about 0.72 hPa and 1.04 K respectively. PWV determined also requires information on the weighted mean temperature (<msub is="true"><mrow is="true"><mi is="true">T</mi></mrow><mrow is="true"><mi is="true">m</mi></mrow></msub></mrow></math>" role="presentation" style="box-sizing: border-box; margin: 0px; padding: 0px; display: inline-block; line-height: normal; font-size: 16.2px; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; position: relative;" tabindex="0"> |
URL | https://www.sciencedirect.com/science/article/pii/S027311772030079X |
DOI | 10.1016/j.asr.2020.02.003 |