Abstract
Global Navigation Satellite System (GNSS) tomography has proved its potential for sensing the three-dimensional (3D) distribution of atmospheric water vapor. Although the standard tomography model has improved, the geometry defect of GNSS acquisition remains a key problem and thus becomes the focus of this research. Thanks to the availability of high-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) precipitable water vapor (PWV) maps, the integration of GNSS and MODIS measurements for tropospheric tomography system is introduced and discussed to address this problem. A methodology based on the node tomography model is developed to exploit the MODIS signals, which overcomes the disadvantage of the standard voxel model in combining the MODIS observations. Three experimental schemes based on MODIS data and simultaneous GNSS observations over the Xuzhou area are implemented to validate the proposed approach. The results show that the mean number of newly crossed voxels (i.e., punctured only by the MODIS signals) increases from 2 in the top layer to 24 in the first layer. Consequently, the average number of total crossed voxels is increased from 272 to 447, which highlights the contribution of the MODIS observations to pass through the empty voxels. Besides, the mean number of tomographic observation equations is enhanced by 35.71% when the MODIS signals are combined. Two-dimensional (2D) profiles of water vapor from radiosonde and 3D distributions of it from ERA5 data are considered to validate the proposed method. It is noted that when using the proposed approach, the mean root-mean-square error (RMSE) of the 2D tomographic profiles is decreased by a value of about 1.06 g/m3 and the overall accuracy of the 3D water vapor distribution is improved by 30.40%. Both the 2D and 3D validations demonstrate the satisfactory performance of the new integrated method to optimize the tomographic water vapor field.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
MODIS data used for this study can be downloaded from the Web site (https://ladsweb.modaps.eosdis.nasa.gov/search/). Radiosonde data of station 58,027 can be downloaded from the Web site (http://weather.uwyo.edu/upperair/sounding.html).
References
Benevides P, Catalao J, Nico G, Miranda PMA (2018) 4D wet refractivity estimation in the atmosphere using GNSS tomography initialized by radiosonde and AIRS measurements: results from a 1-week intensive campaign. GPS Solut 22(4):91. https://doi.org/10.1007/s10291-018-0755-5
Benevides P, Nico G, Catalao J, Miranda PMA (2016) Bridging InSAR and GPS tomography: a new differential geometrical constraint. IEEE Trans Geosci Remote Sens 54(2):697–702. https://doi.org/10.1109/tgrs.2015.2463263
Benevides P, Nico G, Catalao J, Miranda PMA (2017) Analysis of galileo and GPS integration for GNSS tomography. IEEE Trans Geosci Remote Sens 55(4):1936–1943. https://doi.org/10.1109/tgrs.2016.2631449
Bevis M, Stevenbusinge TH (1992) GPS meteorology remote sensing of atmospheric water vapor using global positioning system. J Geophys Res Atmos 97:15787–15801. https://doi.org/10.1029/92JD01517
Boehm J, Schuh H (2004) Vienna mapping functions in VLBI analyses. Geophys Res Lett 31:277. https://doi.org/10.1029/2003GL018984
Chang L, Gao G, Jin S, He X, Xiao R, Guo L (2015) Calibration and evaluation of precipitable water vapor from MODIS infrared observations at night. IEEE Trans Geosci Remote Sens 53(5):2612–2620. https://doi.org/10.1109/TGRS.2014.2363089
Davis JL, Herring TA, Shapiro II, Rogers AEE, Elgered G (1985) Geodesy by radio interferometry: effects of atmospheric modeling errors on estimates of baseline length. Radio Sci 20(6):1593–1607. https://doi.org/10.1029/RS020i006p01593
Ding N, Zhang S, Zhang Q (2017) New parameterized model for GPS water vapor tomography. Ann Geophys 35(2):311–323. https://doi.org/10.5194/angeo-35-311-2017
Ding N, Zhang SB, Wu SQ, Wang XM, Zhang KF (2018) Adaptive node parameterization for dynamic determination of boundaries and nodes of gnss tomographic models. J Geophys Res Atmos 123:1990–2003. https://doi.org/10.1002/2017jd027748
Dong Z, Jin S (2018) 3-D water vapor tomography in Wuhan from GPS BDS and GLONASS Observations. Remote Sens. https://doi.org/10.3390/rs10010062
Duan J et al (1996) GPS meteorology: direct estimation of the absolute value of precipitable water. J Appl Meteorol 35:830–838. https://doi.org/10.1175/1520-0450(1996)035%3c0830:gmdeot%3e2.0.co;2
Flores A, Ruffini G, Rius A (2000) 4D tropospheric tomography using GPS slant wet delays. Ann Geophys 18(2):223–234. https://doi.org/10.1007/s00585-000-0223-7
Gao B, Kaufman YJ (2003) Water vapor retrievals using Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared channels. J Geophys Res. https://doi.org/10.1029/2002JD003023
Ghaffari Razin M-R, Voosoghi B (2020) Estimation of tropospheric wet refractivity using tomography method and artificial neural networks in Iranian case study. GPS Solut 24:65. https://doi.org/10.1007/s10291-020-00979-y
Heublein M, Alshawaf F, Erdnüß B, Zhu XX, Hinz S (2019) Compressive sensing reconstruction of 3D wet refractivity based on GNSS and InSAR observations. J Geodesy 93(2):197–217. https://doi.org/10.1007/s00190-018-1152-0
Li Z, Muller JP, Cross PA (2003) Comparison of precipitable water vapor derived from radiosonde, GPS, and Moderate-Resolution Imaging Spectroradiometer measurements. J Geophys Res 108:D20. https://doi.org/10.1029/2003JD003372
Perler D, Geiger A, Hurter F (2011) 4D GPS water vapor tomography: new parameterized approaches. J Geodesy 85:539–550. https://doi.org/10.1007/s00190-011-0454-2
Rohm W (2013) The ground GNSS tomography – unconstrained approach. Adv Space Res 51:501–513. https://doi.org/10.1016/j.asr.2012.09.021
Song S, Zhu W, Ding J, Peng J (2006) 3D water-vapor tomography with Shanghai GPS network to improve forecasted moisture field. Chinese Sci Bull 51(5):607–614. https://doi.org/10.1007/s11434-006-0607-5
Trzcina E, Rohm W (2019) Estimation of 3D wet refractivity by tomography, combining GNSS and NWP data: First results from assimilation of wet refractivity into NWP. Quart J Roy Meteorol Soc 145(720):1034–1051. https://doi.org/10.1002/qj.3475
Xia P, Cai C, Liu Z (2013) GNSS troposphere tomography based on two-step reconstructions using GPS observations and COSMIC profiles. Ann Geophys 31(10):1805–1815. https://doi.org/10.5194/angeo-31-1805-2013
Zhang W, Zhang S, Ding N, Ma P (2020a) An improved tropospheric tomography method based on the dynamic node parametrized algorithm. Acta Geodyn Geomater 17:191–206. https://doi.org/10.13168/AGG.2020.0014
Zhang W, Zhang S, Ding N, Zhao Q (2020b) A tropospheric tomography method with a novel height factor model including two parts: isotropic and anisotropic height factors. Remote Sens 12:1848. https://doi.org/10.3390/rs12111848
Zhao Q, Liu Y, Ma X, Yao W, Yao Y, Li X (2020a) An improved rainfall forecasting model based on GNSS observations. IEEE Trans Geosci Remot Sens 58(7):4891–4900. https://doi.org/10.1109/TGRS.2020.2968124
Zhao Q, Ma X, Liang L, Yao W (2020b) Spatial-temporal variation characteristics of multiple meteorological variables and vegetation over the Loess Plateau Region. Appl Sci 10(3):1000. https://doi.org/10.3390/app10031000
Zhao Q, Ma X, Yao W, Liu Y, Yao Y (2020c) Anomaly variation of vegetation and its influencing factors in Mainland China During ENSO period. IEEE Access 8:721–734. https://doi.org/10.1109/ACCESS.2019.2962787
Acknowledgements
This study is funded by the National Natural Science Foundation of China (Grant Number 41774026, 41904013, 41974039) and the Natural Science Basic Research Project of Shaanxi (grant number 2020JQ-738)
Author information
Authors and Affiliations
Corresponding authors
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhang, W., Zhang, S., Zheng, N. et al. A new integrated method of GNSS and MODIS measurements for tropospheric water vapor tomography. GPS Solut 25, 79 (2021). https://doi.org/10.1007/s10291-021-01114-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10291-021-01114-1