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    Improving the estimation of zenith dry tropospheric delays using regional surface meteorological data

    195213_195213.pdf (1.642Mb)
    Access Status
    Open access
    Authors
    Luo, X.
    Heck, B.
    Awange, Joseph
    Date
    2013
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Luo, X. and Heck, B. and Awange, J.L. 2013. Improving the estimation of zenith dry tropospheric delays using regional surface meteorological data. Advances in Space Research. 52 (12): pp. 2204-2214.
    Source Title
    Advances in Space Research
    DOI
    10.1016/j.asr.2013.09.005
    ISSN
    0273-1177
    Remarks

    NOTICE: This is the author’s version of a work that was accepted for publication in Advances in Space Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Advances in Space Research, Vol. 52, Issue 12. (2013). doi: 10.1016/j.asr.2013.09.005

    URI
    http://hdl.handle.net/20.500.11937/34092
    Collection
    • Curtin Research Publications
    Abstract

    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%.

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