Improving the estimation of zenith dry tropospheric delays using regional surface meteorological data
Global Navigation Satellite Systems (GNSS) are emerging as possible tools for remote sensing high-resolution atmospheric water vapour that improves weather forecasting through numerical weather prediction models. Nowadays, the GNSS-derived tropospheric zenith total delay (ZTD), comprising zenith dry...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Pergamon
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/34092 |
| _version_ | 1848754128730193920 |
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| author | Luo, X. Heck, B. Awange, Joseph |
| author_facet | Luo, X. Heck, B. Awange, Joseph |
| author_sort | Luo, X. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Global Navigation Satellite Systems (GNSS) are emerging as possible tools for remote sensing high-resolution atmospheric water vapour that improves weather forecasting through numerical weather prediction models. Nowadays, the GNSS-derived tropospheric zenith total delay (ZTD), comprising zenith dry delay (ZDD) and zenith wet delay (ZWD), is achievable with sub-centimetre accuracy. However, if no representative near-site meteorological information is available, the quality of the ZDD derived from tropospheric models is degraded, leading to inaccurate estimation of the water vapour component ZWD as difference between ZTD and ZDD. On the basis of freely accessible regional surface meteorological data, this paper proposes a height-dependent linear correction model for a priori ZDD. By applying the ordinary least-squares estimation (OLSE), bootstrapping (BOOT), and leave-one-out cross-validation (CROS) methods, the model parameters are estimated and analysed with respect to outlier detection. The model validation is carried out using GNSS stations with near-site meteorological measurements. The results verify the efficiency of the proposed ZDD correction model, showing a significant reduction in the mean bias from several centimetres to about 5 mm. The OLSE method enables a fast computation, while the CROS procedure allows for outlier detection. All the three methods produce consistent results after outlier elimination, which improves the regression quality by about 20% and the model accuracy by up to 30%. |
| first_indexed | 2025-11-14T08:35:29Z |
| format | Journal Article |
| id | curtin-20.500.11937-34092 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:35:29Z |
| publishDate | 2013 |
| publisher | Pergamon |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-340922019-02-19T04:27:57Z Improving the estimation of zenith dry tropospheric delays using regional surface meteorological data Luo, X. Heck, B. Awange, Joseph Linear regression Regional surface meteorological data Outlier detection GNSS meteorology Zenith tropospheric delay Global Navigation Satellite Systems (GNSS) are emerging as possible tools for remote sensing high-resolution atmospheric water vapour that improves weather forecasting through numerical weather prediction models. Nowadays, the GNSS-derived tropospheric zenith total delay (ZTD), comprising zenith dry delay (ZDD) and zenith wet delay (ZWD), is achievable with sub-centimetre accuracy. However, if no representative near-site meteorological information is available, the quality of the ZDD derived from tropospheric models is degraded, leading to inaccurate estimation of the water vapour component ZWD as difference between ZTD and ZDD. On the basis of freely accessible regional surface meteorological data, this paper proposes a height-dependent linear correction model for a priori ZDD. By applying the ordinary least-squares estimation (OLSE), bootstrapping (BOOT), and leave-one-out cross-validation (CROS) methods, the model parameters are estimated and analysed with respect to outlier detection. The model validation is carried out using GNSS stations with near-site meteorological measurements. The results verify the efficiency of the proposed ZDD correction model, showing a significant reduction in the mean bias from several centimetres to about 5 mm. The OLSE method enables a fast computation, while the CROS procedure allows for outlier detection. All the three methods produce consistent results after outlier elimination, which improves the regression quality by about 20% and the model accuracy by up to 30%. 2013 Journal Article http://hdl.handle.net/20.500.11937/34092 10.1016/j.asr.2013.09.005 Pergamon fulltext |
| spellingShingle | Linear regression Regional surface meteorological data Outlier detection GNSS meteorology Zenith tropospheric delay Luo, X. Heck, B. Awange, Joseph Improving the estimation of zenith dry tropospheric delays using regional surface meteorological data |
| title | Improving the estimation of zenith dry tropospheric delays using regional surface meteorological data |
| title_full | Improving the estimation of zenith dry tropospheric delays using regional surface meteorological data |
| title_fullStr | Improving the estimation of zenith dry tropospheric delays using regional surface meteorological data |
| title_full_unstemmed | Improving the estimation of zenith dry tropospheric delays using regional surface meteorological data |
| title_short | Improving the estimation of zenith dry tropospheric delays using regional surface meteorological data |
| title_sort | improving the estimation of zenith dry tropospheric delays using regional surface meteorological data |
| topic | Linear regression Regional surface meteorological data Outlier detection GNSS meteorology Zenith tropospheric delay |
| url | http://hdl.handle.net/20.500.11937/34092 |