Atmospheric bending effects in GNSS tomography

Möller, Gregor; Landskron, Daniel

doi:10.5194/amt-12-23-2019

Cited by 24 publications

(22 citation statements)

References 16 publications

(23 reference statements)

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“…A hypothesis of straight ray propagating was considered in this study for modelling the path delay from GPS stations to ground-based receivers in our tomography models. Because the bending effect is negligible over 10 • [3,63,64], the inversion problem becomes linear and can be formulated using the discrete theory. Post-fit residuals cleaned from systematic effects, which were not used in this study, could be beneficial for GPS slant observations under severe weather conditions.…”

Section: Summary Conclusion and Perspectives For Future Workmentioning

confidence: 99%

Cross-Comparison and Methodological Improvement in GPS Tomography

et al. 2019

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GPS tomography has been investigated since 2000 as an attractive tool for retrieving the 3D field of water vapour and wet refractivity. However, this observational technique still remains a challenging task that requires improvement of its methodology. This was the purpose of this study, and for this, GPS data from the Australian Continuously Operating Research Station (CORS) network during a severe weather event were used. Sensitivity tests and statistical cross-comparisons of tomography retrievals with independent observations from radiosonde and radio-occultation profiles showed improved results using the presented methodology. The initial conditions, which were associated with different time-convergence of tomography inversion, play a critical role in GPS tomography. The best strategy can reduce the normalised root mean square (RMS) of the tomography solution by more than 3 with respect to radiosonde estimates. Data stacking and pseudo-slant observations can also significantly improve tomography retrievals with respect to non-stacked solutions. A normalised RMS improvement up to 17% in the 0-8 km layer was found by using 30 min data stacking, and RMS values were divided by 5 for all the layers by using pseudo-observations. This result was due to a better geometrical distribution of mid-and low-tropospheric parts (a 30% coverage improvement). Our study of the impact of the uncertainty of GPS observations shows that there is an interest in evaluating tomography retrievals in comparison to independent external measurements and in estimating simultaneously the quality of weather forecasts. Finally, a comparison of multi-model tomography with numerical weather prediction shows the relevant use of tomography retrievals to improving the understanding of such severe weather conditions. Global Positioning System (GPS) tomography considers the use of slant-integrated estimates, wet delays, or corresponding water vapour content estimated from the data records of ground-based GPS stations to respectively retrieve the 3D field of wet refractivity or water vapour density, as introduced by [1,2]. Comparisons of tomography retrievals with other techniques (e.g., those using a water vapour radiometer, radiosonde, raman lidar, or atmospheric emitted radiance interferometer) and with numerical weather models have shown relevant results and an encouraging understanding of meteorological situations ([3-17]) The resolution and configuration/geometry of the network of GPS stations are critical parameters with which to obtain the best scenario for applying GPS tomography to retrieve water vapour density or wet refractivity. These fields can be ideally retrieved for meteorological applications with a horizontal resolution of a few kilometres, a vertical resolution of~500 m in the lower troposphere, and a vertical resolution of~2 km in the upper troposphere, with a time resolution of 15 to 5 min. However, to obtain this remarkable geometrical resolution, data from a dense homogeneously distributed network of GPS stations (e.g., a netw...

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Section: Summary Conclusion and Perspectives For Future Workmentioning

confidence: 99%

Cross-Comparison and Methodological Improvement in GPS Tomography

et al. 2019

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“…latter case, the coarse along-ray but high-vertical-resolution RO data are complementary to the finer horizontal scale, but the coarse vertical-resolution-passive MW data enables the potential tomographic reconstruction of 3-D water vapor structure [47].…”

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confidence: 99%

Benefits of a Closely-Spaced Satellite Constellation of Atmospheric Polarimetric Radio Occultation Measurements

Turk

Padullés

et al. 2019

Remote Sensing

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The climate and weather forecast predictive capability for precipitation intensity is limited by gaps in the understanding of basic cloud-convective processes. Currently, a better understanding of the cloud-convective process lacks observational constraints, due to the difficulty in obtaining accurate, vertically resolved pressure, temperature, and water vapor structure inside and near convective clouds. This manuscript describes the potential advantages of collecting sequential radio occultation (RO) observations from a constellation of closely spaced low Earth-orbiting satellites. In this configuration, the RO tangent points tend to cluster together, such that successive RO ray paths are sampling independent air mass quantities as the ray paths lie "parallel" to one another. When the RO train orbits near a region of precipitation, there is a probability that one or more of the RO ray paths will intersect the region of heavy precipitation, and one or more would lie outside. The presence of heavy precipitation can be discerned by the use of the polarimetric RO (PRO) technique recently demonstrated by the Radio Occultations through Heavy Precipitation (ROHP) receiver onboard the Spanish PAZ spacecraft. This sampling strategy provides unique, near-simultaneous observations of the water vapor profile inside and in the environment surrounding heavy precipitation, which are not possible from current RO data. temperature vertical and horizontal structures in the surrounding region. Much of the water vapor resides in the 2 km nearest the Earth, the approximate top of the boundary layer, which is the layer where air motions are directly influenced by contact with the Earth's surface. However, increasing evidence points to the control of deep convection by the relatively smaller and more variable amount of moisture above the boundary layer, known as the free troposphere. When abundant, free tropospheric moisture can strengthen convection by making the atmosphere more unstable. Horizontal transport and the mixing of nearby dry air (through a process known as entrainment) can weaken the convection, by decreasing the buoyancy [2,3]. Free-tropospheric moisture thus appears to exert significant control on the development of deep convection, producing heavy precipitation [4][5][6][7][8][9][10][11].Currently, a better understanding of the cloud-convective process lacks observational constraints, due to the difficulty in obtaining accurate, vertically resolved pressure, temperature, and water vapor (denoted by p, T, and q, respectively) structure in and near convection. Infrared sensors, such as the Atmospheric Infrared Sounder (AIRS), provide water vapor structure outside of clouds and precipitation, but cannot estimate the water vapor inside clouds. Passive microwave (MW) sounding radiometers, such as the Advanced Technology Microwave Sounder (ATMS), operate in millimeter-wave (183 GHz) water vapor bands, which under heavy precipitation, are sensitive to the cloud top ice region [12], limiting their use in studies that require ...

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“…Every radiosonde station was selected to be close to an IGS station less than 6 km apart. The 2D ray-tracing program was validated against third-party software [54], with a 1 mm accurate radio propagation delay at a 3 • elevation angle. We compared these experimental delays and mapping functions with the Saastamoinen ZHD delays and VMF1 and VMF3 mapping functions slant delays.…”

Section: Discussionmentioning

confidence: 99%

Assessment of the Accuracy of the Saastamoinen Model and VMF1/VMF3 Mapping Functions with Respect to Ray-Tracing from Radiosonde Data in the Framework of GNSS Meteorology

Feng

Yan

et al. 2020

Remote Sensing

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In this paper, we assess, in the framework of Global Navigation Satellite System (GNSS) meteorology, the accuracy of GNSS propagation delays corresponding to the Saastamoinen zenith hydrostatic delay (ZHD) model and the Vienna Mapping function VMF1/VMF3 (hydrostatic and wet), with reference to radiosonde ray-tracing delays over a three-year period on 28 globally distributed sites. The results show that the Saastamoinen ZHD estimates have a mean root mean square (RMS) error of 1.7 mm with respect to the radiosonde. We also detected some seasonal signatures in these Saastamoinen ZHD estimates. This indicates that the Saastamoinen model, based on the hydrostatic assumption and the ground pressure, is insufficient to capture the full variability of the ZHD estimates over time with the accuracy needed for GNSS meteorology. Furthermore, we found that VMF3 slant hydrostatic delay (SHD) estimates outperform the corresponding VMF1 SHD estimates (equivalent SHD RMS error of 4.8 mm for VMF3 versus 7.1 mm for VMF1 at 5° elevation angle), with respect to the radiosonde SHD estimates. Unexpectedly, the situation is opposite for the VMF3 slant wet delay (SWD) estimates compared to VMF1 SWD estimates (equivalent SWD RMS error of 11.4 mm for VMF3 versus 7.0 mm for VMF1 at 5° elevation angle). Our general conclusion is that the joint approach using ZHD models and mapping functions must be revisited, at least in the framework of GNSS meteorology.

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Atmospheric bending effects in GNSS tomography

Cited by 24 publications

References 16 publications

Cross-Comparison and Methodological Improvement in GPS Tomography

Cross-Comparison and Methodological Improvement in GPS Tomography

Benefits of a Closely-Spaced Satellite Constellation of Atmospheric Polarimetric Radio Occultation Measurements

Assessment of the Accuracy of the Saastamoinen Model and VMF1/VMF3 Mapping Functions with Respect to Ray-Tracing from Radiosonde Data in the Framework of GNSS Meteorology

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