Comparative Study of Empirical Tropospheric Models for the Hong Kong Region

Li, Z. W.; Ding, Xiaoli; Chen, Wu; Liu, G. X.; Shea, Yu Kai Geoffrey; Emerson, N.

doi:10.1179/003962608x325394

Cited by 11 publications

(17 citation statements)

References 17 publications

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“…The difference in performance is mainly due to the modelling of the wet component of the tropospheric delay in the models particularly the assumption made about the distribution of water vapour within the troposphere (Hofmann-Wellenhof et al, 2008;Li et al, 2008). For instance, the Black model assumes regional empirical constants that vary according to season and climate.…”

Section: Models Assessment Based On Estimated Ztdmentioning

confidence: 99%

“…Besides, α and λ are assigned constant values in the Saastamoinen model while variable in the Askne and Nordius model to meet the prevailing environs. Therefore, models, where α and λ can be varied to suit the prevailing environs, produces better accuracy (Askne and Nordius, 1987;Li et al, 2008). Moreover, the numerical values for the refractivity constants k1, k2, k3 used by the Saastamoinen model differ slightly from those used by the Askne and Nordius model.…”

Section: Models Assessment Based On Estimated Ztdmentioning

confidence: 99%

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Comparative evaluation and analysis of different tropospheric delay models in Ghana

Osah¹,

Acheampong²,

Dadzie³

et al. 2022

SA J of Geomatics

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Tropospheric delay prediction models have become increasingly important in Global Navigation Satellite System (GNSS) as they play a critical role in GNSS positioning applications. Due to the different atmospheric conditions over the earth regions, tropospheric effect on GNSS signals also differs, influencing the performance of these prediction models. Thus, the choice of a particular prediction model can significantly degrade the positioning accuracy especially when the model does not suit the user’s environs. Therefore, a performance assessment of existing prediction models in various regions for a suitable one is very imperative. This paper evaluates and analyses seven commonly used tropospheric delay models in Ghana in terms of performances in Zenith Tropospheric Delay (ZTD) estimation and baseline positional accuracies using data from six selected Continuously Operating Reference Stations (CORS). The 1˚x1˚ gridded Vienna Mapping Functions 3 (VMF3) ZTD product and coordinates solutions from the CSRS-PPP positioning service were respectively used as references. The results show that the Black model performed better in estimating the ZTD, followed by Askne and Nordius model. The Saastamoinen, Marini and Murray, Niell, Goads and Goodman and Hopfield models respectively performed poorly. However, the result of the baseline solutions did not show much variation in the coordinate difference provided by the use of the prediction models, nonetheless, the Black and Askne and Nordius models continue to dominate the other models. Of all the models evaluated, either Black or Askne and Nordius model is recommended for use to mitigate the ZTD in the study area, however, the choice of the Black model will be more desirable.

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Section: Models Assessment Based On Estimated Ztdmentioning

confidence: 99%

Section: Models Assessment Based On Estimated Ztdmentioning

confidence: 99%

Comparative evaluation and analysis of different tropospheric delay models in Ghana

Osah¹,

Acheampong²,

Dadzie³

et al. 2022

SA J of Geomatics

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“…In the third elevation‐dependent interpolator (called SKlm+Baby hereinafter), the Baby semi‐empirical model (Baby et al 1988) is used to model the elevation‐dependent component of the water vapour delay. As the Baby semi‐empirical model is only reference to one meteorological station and the model itself has an uncertainty of up to several centimetres (Li et al 2008a), the elevation‐dependent component of water vapour delay is difficult to model accurately. This degrades the accuracy of the SKlm+Baby interpolator.…”

Section: Elevation‐dependent Interpolation Model Of Wet Delaymentioning

confidence: 99%

Correcting atmospheric effects on InSAR with MERIS water vapour data and elevation-dependent interpolation model

Feng

et al. 2012

Geophysical Journal International

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SUMMARY The propagation delay when radar signals travel from the troposphere has been one of the major limitations for the applications of high precision repeat‐pass Interferometric Synthetic Aperture Radar (InSAR). In this paper, we first present an elevation‐dependent atmospheric correction model for Advanced Synthetic Aperture Radar (ASAR—the instrument aboard the ENVISAT satellite) interferograms with Medium Resolution Imaging Spectrometer (MERIS) integrated water vapour (IWV) data. Then, using four ASAR interferometric pairs over Southern California as examples, we conduct the atmospheric correction experiments with cloud‐free MERIS IWV data. The results show that after the correction the rms differences between InSAR and GPS have reduced by 69.6 per cent, 29 per cent, 31.8 per cent and 23.3 per cent, respectively for the four selected interferograms, with an average improvement of 38.4 per cent. Most importantly, after the correction, six distinct deformation areas have been identified, that is, Long Beach–Santa Ana Basin, Pomona–Ontario, San Bernardino and Elsinore basin, with the deformation velocities along the radar line‐of‐sight (LOS) direction ranging from −20 mm yr−1 to −30 mm yr−1 and on average around −25 mm yr−1, and Santa Fe Springs and Wilmington, with a slightly low deformation rate of about −10 mm yr−1 along LOS. Finally, through the method of stacking, we generate a mean deformation velocity map of Los Angeles over a period of 5 yr. The deformation is quite consistent with the historical deformation of the area. Thus, using the cloud‐free MERIS IWV data correcting synchronized ASAR interferograms can significantly reduce the atmospheric effects in the interferograms and further better capture the ground deformation and other geophysical signals.

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“…Using ground meteorological data to calibrate tropospheric delays in radio ranging has been well documented [48-52]. Hanssen and Feijt [53] and Zebker et al [20] proposed the use of the Saastamoinen model to assess the potential effects of the troposphere on InSAR measurements.…”

Section: Mitigation Of Atmospheric Effects On Repeat-pass Insarmentioning

confidence: 99%

Atmospheric Effects on InSAR Measurements and Their Mitigation

Ding

Zhu

et al. 2008

Sensors

165

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Interferometric Synthetic Aperture Radar (InSAR) is a powerful technology for observing the Earth surface, especially for mapping the Earth's topography and deformations. InSAR measurements are however often significantly affected by the atmosphere as the radar signals propagate through the atmosphere whose state varies both in space and in time. Great efforts have been made in recent years to better understand the properties of the atmospheric effects and to develop methods for mitigating the effects. This paper provides a systematic review of the work carried out in this area. The basic principles of atmospheric effects on repeat-pass InSAR are first introduced. The studies on the properties of the atmospheric effects, including the magnitudes of the effects determined in the various parts of the world, the spectra of the atmospheric effects, the isotropic properties and the statistical distributions of the effects, are then discussed. The various methods developed for mitigating the atmospheric effects are then reviewed, including the methods that are based on PSInSAR processing, the methods that are based on interferogram modeling, and those that are based on external data such as GPS observations, ground meteorological data, and satellite data including those from the MODIS and MERIS. Two examples that use MODIS and MERIS data respectively to calibrate atmospheric effects on InSAR are also given.

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Comparative Study of Empirical Tropospheric Models for the Hong Kong Region

Cited by 11 publications

References 17 publications

Comparative evaluation and analysis of different tropospheric delay models in Ghana

Comparative evaluation and analysis of different tropospheric delay models in Ghana

Correcting atmospheric effects on InSAR with MERIS water vapour data and elevation-dependent interpolation model

Atmospheric Effects on InSAR Measurements and Their Mitigation

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