An enhanced integrated water vapour dataset from more than 10 000 global ground-based GPS stations in 2020
dc.contributor.author | Yuan, P. | |
dc.contributor.author | Blewitt, G. | |
dc.contributor.author | Kreemer, C. | |
dc.contributor.author | Hammond, W.C. | |
dc.contributor.author | Argus, D. | |
dc.contributor.author | Yin, X. | |
dc.contributor.author | Van Malderen, R. | |
dc.contributor.author | Mayer, M. | |
dc.contributor.author | Jiang, W. | |
dc.contributor.author | Awange, Joseph | |
dc.contributor.author | Kutterer, H. | |
dc.date.accessioned | 2023-05-05T06:56:10Z | |
dc.date.available | 2023-05-05T06:56:10Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Yuan, P. and Blewitt, G. and Kreemer, C. and Hammond, W.C. and Argus, D. and Yin, X. and Van Malderen, R. et al. 2023. An enhanced integrated water vapour dataset from more than 10 000 global ground-based GPS stations in 2020. Earth System Science Data. 15 (2): pp. 723-743. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/91901 | |
dc.identifier.doi | 10.5194/essd-15-723-2023 | |
dc.description.abstract |
We developed a high-quality global integrated water vapour (IWV) dataset from 12 552 ground-based global positioning system (GPS) stations in 2020. It consists of 5 min GPS IWV estimates with a total number of 1 093 591 492 data points. The completeness rates of the IWV estimates are higher than 95 % at 7253 (58 %) stations. The dataset is an enhanced version of the existing operational GPS IWV dataset provided by the Nevada Geodetic Laboratory (NGL). The enhancement is reached by employing accurate meteorological information from the fifth generation of European ReAnalysis (ERA5) for the GPS IWV retrieval with a significantly higher spatiotemporal resolution. A dedicated data screening algorithm is also implemented. The GPS IWV dataset has a good agreement with in situ radiosonde observations at 182 collocated stations worldwide. The IWV biases are within ±3.0 kg m-2 with a mean absolute bias (MAB) value of 0.69 kg m-2. The standard deviations (SD) of IWV differences are no larger than 3.4 kg m-2. In addition, the enhanced IWV product shows substantial improvements compared to NGL's operational version, and it is thus recommended for high-accuracy applications, such as research of extreme weather events and diurnal variations of IWV and intercomparisons with other IWV retrieval techniques. Taking the radiosonde-derived IWV as reference, the MAB and SD of IWV differences are reduced by 19.5 % and 6.2 % on average, respectively. The number of unrealistic negative GPS IWV estimates is also substantially reduced by 92.4 % owing to the accurate zenith hydrostatic delay (ZHD) derived by ERA5. The dataset is available at 10.5281/zenodo.6973528 (Yuan et al., 2022). | |
dc.language | English | |
dc.publisher | COPERNICUS GESELLSCHAFT MBH | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.subject | Science & Technology | |
dc.subject | Physical Sciences | |
dc.subject | Geosciences, Multidisciplinary | |
dc.subject | Meteorology & Atmospheric Sciences | |
dc.subject | Geology | |
dc.subject | RADIOSONDE | |
dc.subject | MODEL | |
dc.subject | METEOROLOGY | |
dc.subject | ERRORS | |
dc.subject | SET | |
dc.title | An enhanced integrated water vapour dataset from more than 10 000 global ground-based GPS stations in 2020 | |
dc.type | Journal Article | |
dcterms.source.volume | 15 | |
dcterms.source.number | 2 | |
dcterms.source.startPage | 723 | |
dcterms.source.endPage | 743 | |
dcterms.source.issn | 1866-3508 | |
dcterms.source.title | Earth System Science Data | |
dc.date.updated | 2023-05-05T06:56:04Z | |
curtin.department | School of Earth and Planetary Sciences (EPS) | |
curtin.accessStatus | Open access | |
curtin.faculty | Faculty of Science and Engineering | |
curtin.contributor.orcid | Awange, Joseph [0000-0003-3533-613X] | |
curtin.contributor.researcherid | Awange, Joseph [A-3998-2008] | |
dcterms.source.eissn | 1866-3516 | |
curtin.contributor.scopusauthorid | Awange, Joseph [6603092635] | |
curtin.repositoryagreement | V3 |