Tomographic reconstruction of neutral atmospheres using Slant and Horizontal Wet Delays achievable through the processing of signals observed from small GPS networks

Notarpietro, Riccardo; Gabella, Marco; Perona, Giovanni Emilio

doi:10.5721/itjrs20084026

Cited by 7 publications

(11 citation statements)

References 7 publications

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“…Since a suitable vertical receiver distribution is difficult to implement, the availability of quasi-horizontal observations is necessary. Then, limb sounding Radio Occultation observations are necessary in order to guarantee good observations coming also from low elevation angles (this aspect has already been demonstrated by Kirchengast, 2001 andNotarpietro et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

“…Such new approach is based on an algorithm previously developed, on which we shown, from a simulative point of view only, the possibility to infer wet refractivity fields without using first guess atmospheric models and without adopting any a priori informations (Notarpietro et al, 2008). Such an algorithm has been applied to real measurements collected by a local network of GPS receivers.…”

Section: State Of the Artmentioning

confidence: 99%

“…Notarpietro et al (2008), shown the possibility to infer Wet Refractivity fields without using first guess atmospheric models. The algorithm accomplishes the reconstruction in two consecutive steps.…”

Section: Theoretical Basis Retrieval Technique Observables and Valimentioning

confidence: 99%

See 2 more Smart Citations

GNSS Signals: A Powerful Source for Atmosphere and Earth’s Surface Monitoring

Notarpietro¹,

Cucca²,

Bonafoni³

2012

Remote Sensing of Planet Earth

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Section: Discussionmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

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GNSS Signals: A Powerful Source for Atmosphere and Earth’s Surface Monitoring

Notarpietro¹,

Cucca²,

Bonafoni³

2012

Remote Sensing of Planet Earth

Self Cite

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“…When investigating the standard deviation of the differences between estimated refractivities and WRF refractivities for the CS 10 case, considering an increased number of synthetic GNSS sites only, while keeping a constant number of five signal directions 11 per site, is not advantageous. However, as of 15 different signal directions per site, a clear improvement in standard deviation 12 is visible when increasing the number of sites in the tomographic setting solved by means of CS. Independently of the num-13 ber of sites, for realistic GPS-like observing geometry settings with five to ten signal directions per site, the LSQ refractivity 14 estimates are more precise than the CS refractivity estimates.…”

mentioning

confidence: 98%

“…10 regularized by means of t = 3 regularization terms, namely by horizontal and vertical smoothing constraints as well as by prior 11 knowledge from surface meteorology. As described in Heublein et al (2018), the horizontal smoothing constraints assuming 12 the refractivity of a voxel (a, b, k) to equal the weighted mean refractivity of the surrounding voxels (p, q, k) with voxel indices 13 p = a and q = b within the same height layer k are defined by:…”

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confidence: 99%

Observing geometry effects on a GNSS based water vapor tomography solved by Least Squares and by Compressive Sensing

Heublein¹,

Bradley²,

Hinz³

2019

Preprint

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Abstract. In this work, the effect of the observing geometry on the tomographic reconstruction quality of both a regularized Least Squares (LSQ) and a Compressive Sensing (CS) approach for neutrospheric water vapor tomography is compared based on synthetic Global Navigation Satellite System (GNSS) Slant Wet Delay (SWD) estimates. In this context, the term observing geometry mainly refers to the number of GNSS sites situated within a specific study area subdivided into a certain number of volumetric pixels (voxels) and to the number of signal directions available at each GNSS site. The novelties of this research are 1) the comparison of the observing geometry’s effects on the tomographic reconstruction accuracy when using LSQ resp. CS for the solution of the tomographic system and 2) the investigation of the effect of the signal directions’ variability on the tomographic reconstruction. The tomographic reconstruction is performed based on synthetic SWD data sets generated, for many samples of various observing geometry settings, based on wet refractivity information from the Weather Research and Forecasting (WRF) model. The validation of the achieved results focuses on a comparison of the refractivity estimates with the input WRF refractivities. The results show that the recommendation of Champollion et al. (2004) to discretize the analyzed study area into voxels with horizontal sizes comparable to the mean GNSS inter site distance represents a good rule of thumb for both LSQ and CS based tomography solutions. In addition, this research shows that CS needs a variety of at least 15 signal directions per site in order to estimate the refractivity field more accurately and more precisely than LSQ. Therefore, the use of CS is particularly recommended for water vapor tomography applications for which a high number of multi-GNSS SWD estimates are available.

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Compressive sensing reconstruction of 3D wet refractivity based on GNSS and InSAR observations

Heublein

Alshawaf

Erdnüß

et al. 2018

J Geod

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In this work, the reconstruction quality of an approach for neutrospheric water vapor tomography based on Slant Wet Delays (SW Ds) obtained from Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) is investigated. The novelties of this approach are (1) the use of both absolute GNSS and absolute InSAR SW Ds for tomography and (2) the solution of the tomographic system by means of compressive sensing (CS). The tomographic reconstruction is performed based on (i) a synthetic SW D dataset generated using wet refractivity information from the Weather Research and Forecasting (WRF) model and (ii) a real dataset using GNSS and InSAR SW Ds. Thus, the validation of the achieved results focuses (i) on a comparison of the refractivity estimates with the input WRF refractivities and (ii) on radiosonde profiles. In case of the synthetic dataset, the results show that the CS approach yields a more accurate and more precise solution than least squares (LSQ). In addition, the benefit of adding synthetic InSAR SW Ds into the tomographic system is analyzed. When applying CS, adding synthetic InSAR SW Ds into the tomographic system improves the solution both in magnitude and in scattering. When solving the tomographic system by means of LSQ, no clear behavior is observed. In case of the real dataset, the estimated refractivities of both methodologies show a consistent behavior although the LSQ and CS solution strategies differ.

show abstract

Tomographic reconstruction of neutral atmospheres using Slant and Horizontal Wet Delays achievable through the processing of signals observed from small GPS networks

Cited by 7 publications

References 7 publications

GNSS Signals: A Powerful Source for Atmosphere and Earth’s Surface Monitoring

GNSS Signals: A Powerful Source for Atmosphere and Earth’s Surface Monitoring

Observing geometry effects on a GNSS based water vapor tomography solved by Least Squares and by Compressive Sensing

Compressive sensing reconstruction of 3D wet refractivity based on GNSS and InSAR observations

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