GNSS Water Vapor Tomography

Bender, Michael A.; Dick, Galina

doi:10.1007/978-3-030-52171-4_36

Cited by 4 publications

(6 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The new version of the Miranda and Mateus (2021, 2022) algorithm, with local estimates of the regional scale height of water vapor, appears to be able to replicate the vertical profile of water vapor density with smaller errors than other methods (Bender & Dick, 2021; Zhang et al., 2021), even when those methods incorporate non‐GNSS data and in many cases are evaluated against indirect data such as reanalysis.…”

Section: Discussionmentioning

confidence: 99%

“…The new mean tomography RMSE is approaching typical RMSE values of in situ observations and compares very well with other algorithms (cf. Bender & Dick, 2021). Some extra details of the tomographic inversion are described in Text S2 of the Supporting Information S1.…”

Section: Methodsmentioning

confidence: 99%

“…After the first attempt by Flores et al (2000), different approaches have been proposed (for a review, cf. Bender & Dick, 2021). In general, the development of tomographic algorithms has relied on short-duration field experiments, severely limiting the robustness of its evaluation.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Optimizing Boundary Conditions in GNSS Tomography: A Continuous 7‐Month Case Study in the Amazon

Miranda,

Adams,

Tomé

et al. 2023

Geophysical Research Letters

View full text Add to dashboard Cite

Different estimates of the regional water vapor scale height, taken from ERA5 reanalysis, in situ observations and the direct optimization of retrieved water vapor profiles in GNSS tomography, are found to have major impact in the performance of tomographic inversions, with the better results displaying mean errors comparable to radiosondes. The analysis uses 7 months of GNSS (Global Navigation Satellite Systems) observations in the Amazon Dense GNSS Network near Manaus, Brazil, in 2011–2012, to compute a time series of water vapor profiles, with a tomographic technique capable of producing quasi‐instantaneous inversions with minimal external data or constraints. Results compare very well with 12‐hourly in situ radiosondes, especially in the lower troposphere above 2 km, and its daily‐to‐seasonal variability compares well with WRF (Weather Research and Forecasting) convective‐permitting simulations driven by ERA5 boundary conditions, suggesting that GNSS tomography may be an important source of atmospheric water vapor data for different applications.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing Boundary Conditions in GNSS Tomography: A Continuous 7‐Month Case Study in the Amazon

Miranda,

Adams,

Tomé

et al. 2023

Geophysical Research Letters

View full text Add to dashboard Cite

show abstract

“…Its direct impact in the quality of weather forecasting has been found to be positive, but modest (e.g., Mateus et al., 2018), especially in comparison with similar but much higher resolution PWV fields from InSAR (Mateus & Miranda, 2022; Miranda et al., 2019). Unlike InSAR, however, GNSS data can be explored to estimate vertical profiles of water vapor density, and different tomographic algorithms have been proposed (e.g., Bender et al., 2011; Champollion et al., 2004; Flores et al., 2000; Nilsson et al., 2007; Van Baelen et al., 2011, for a review cf., Bender & Dick, 2021), generally combining observations corresponding to the different paths (slants) connecting each station with each available satellite (Slant Integrated Water Vapor, SIWV) with a set of extra data coming from other sensors, and from numerical weather prediction models, with various conditions constraining the variability of water vapor within the tomographic domain. As a result of the complex set of data and constraints required by those algorithms to converge to sensible solutions, the added value of tomography has also been limited, and too sensitive to the quality of its first guess.…”

Section: Introductionmentioning

confidence: 99%

Improved GNSS Water Vapor Tomography With Modified Mapping Functions

Miranda

Mateus

2022

Geophysical Research Letters

View full text Add to dashboard Cite

The use of optimized GNSS mapping functions is here shown to lead to significant improvements in the performance of a water vapor tomographic model, totally driven by GNSS observations. The method improves a recent proposal for unconstrained tomographic inversions and is developed and validated with data from the Manaus dense GNSS network and in‐situ radiosondes, covering different seasons and synoptic conditions. The optimization uses a Monte Carlo technique to find the minimum root‐mean‐square error in a two‐dimensional parameter space. A set of fixed parameters, computed by optimizing over a small subset of cases, is found to lead to results that are overall significantly better than those produced by the three more common mapping functions used for geodesy applications. The quality of inversions is, however, found to have significant spread, an indication of the impact of varying GNSS data quality and a reminder of the need of further improvements in the method.

show abstract

“…The wet tropospheric delay measurements contain information about the structure of the wet refractivity index in the atmosphere; therefore, spatial and temporal structure of the wet refractivity index can be estimated from the wet tropospheric delay measurements in a dense network of GNSS stations using a tomographic approach [6]. Tregoning [7] evaluated the capability of Global Positioning System (GPS) for estimating the water vapor and other tropospheric components by comparing the estimates with radiosonde measurements. Bevis [8] and Emardson [3] compared water vapor estimated from GPS observations with radiometer data.…”

Section: Introductionmentioning

confidence: 99%

The retrieval of wet refractivity index by tomography using spherical cap harmonics

Dehvari¹,

Farzaneh²,

Sharifi³

2022

JSST

View full text Add to dashboard Cite

High spatial and temporal variability of the tropospheric wet refractivity index, makes it difficult to present an accurate model for this variable. Up to now, Radiosonde stations data have been used for monitoring atmosphere parameters. Furthermore, because of the sparse distribution of radiosonde stations to monitor the lower levels of the atmosphere, the numerical weather models do not have enough accuracies for atmospheric parameters. Using the GPS tropospheric delay measurements and tomography approaches, the wet refractivity index can be estimated. In this research, three-dimensional and four-dimensional basis-function tomography is used to demonstrate the distribution of wet refractivity index of the troposphere. In this model, spherical cap harmonics are used for the horizontal distribution of the wet refractivity index, and empirical orthogonal functions are used for the vertical distribution of the index. In addition, temporal changes are considered by correlating the unknown coefficients using fourier series. The region of study is in the west California State, and the wet refractivity index is retrieved from the wet tropospheric delay measurements. To validate the results, radiosonde profiles were compared to the tomographically retrieved profiles. The results show that wet refractivity indices can be retrieved using functional models with RMSE about 2.4 ppm till 3.9 in the four-dimensional method. The comparisons show that the four-dimensional retrieved profiles show improvement up to 34 and 42 percentages in mid-day tomography epochs compared to the three-dimensional tomography results. Also it can be seen that in mid-night epochs, the three-dimensional tomography has higher accuracy compared to four-dimensional method because of low variation of wet refractivity indices.

show abstract

GNSS Water Vapor Tomography

Cited by 4 publications

References 64 publications

Optimizing Boundary Conditions in GNSS Tomography: A Continuous 7‐Month Case Study in the Amazon

Optimizing Boundary Conditions in GNSS Tomography: A Continuous 7‐Month Case Study in the Amazon

Improved GNSS Water Vapor Tomography With Modified Mapping Functions

The retrieval of wet refractivity index by tomography using spherical cap harmonics

Contact Info

Product

Resources

About