An Improved Tomography Approach Based on Adaptive Smoothing and Ground Meteorological Observations

Using the Global Navigation Satellite System (GNSS) to sense three-dimensional water vapor (WV) has been intensively investigated. However, this technique still heavily relies on the a priori information. In this study, we propose an improved tomography approach based on adaptive Laplacian smoothing (ALS) and ground meteorological observations. By using the proposed approach, the troposphere tomography is less dependent on a priori information and the ALS constraints match better with the actual situation than the constant constraints. Tomography experiments in Hong Kong during a heavy rainy period and a rainless period show that the ALS method gets superior results compared with the constant Laplacian smoothing (CLS) method. By validation with radiosonde and European Centre for Medium-Range Weather Forecasts (ECMWF) data, we found that the introduction of ground meteorological observations into tomography can solve the perennial problem of resolving the wet refractivity in the lower troposphere and thus significantly improve the tomography results. However, bad data quality and incompatibility of the ground meteorological observations may introduce errors into tomography results.

[1]  Eric Pottiaux,et al.  Inter-technique validation of tropospheric slant total delays , 2017 .

[2]  Witold Rohm,et al.  Limited constraint, robust Kalman filtering for GNSS troposphere tomography , 2014 .

[3]  Giulio Ruffini,et al.  Estimation of Tropospheric Zenith Delay and Gradients over the Madrid Area Using GPS and WVR Data , 1999 .

[4]  Wang Xiaoying,et al.  Tropospheric wet refractivity tomography using multiplicative algebraic reconstruction technique , 2014 .

[5]  Kazuro Hirahara,et al.  Local GPS tropospheric tomography , 2000 .

[6]  Witold Rohm,et al.  The verification of GNSS tropospheric tomography model in a mountainous area , 2011 .

[7]  Harald Schuh,et al.  Retrieving high‐resolution tropospheric gradients from multiconstellation GNSS observations , 2015 .

[8]  A. Wexler.  Vapor Pressure Formulation for Ice , 1977, Journal of Research of the National Bureau of Standards. Section A, Physics and Chemistry.

[9]  Witold Rohm,et al.  The ground GNSS tomography – unconstrained approach , 2013 .

[10]  Pierre Héroux,et al.  Precise Point Positioning Using IGS Orbit and Clock Products , 2001, GPS Solutions.

[11]  J. Saastamoinen.  Atmospheric Correction for the Troposphere and Stratosphere in Radio Ranging Satellites , 2013 .

[12]  Christian Rocken,et al.  Near real‐time GPS sensing of atmospheric water vapor , 1997 .

[13]  Xingxing Li,et al.  Retrieving of atmospheric parameters from multi‐GNSS in real time: Validation with water vapor radiometer and numerical weather model , 2015 .

[14]  Maorong Ge,et al.  Development of a GNSS water vapour tomography system using algebraic reconstruction techniques , 2011 .

[15]  Hajime Nakamura,et al.  Three-dimensional distribution of water vapor estimated from tropospheric delay of GPS data in a mesoscale precipitation system of the Baiu front , 2000 .

[16]  Maorong Ge,et al.  Real‐time GPS sensing of atmospheric water vapor: Precise point positioning with orbit, clock, and phase delay corrections , 2014 .

[17]  Pedro Elosegui,et al.  Sensing atmospheric structure using small‐scale space geodetic networks , 1999 .

[18]  Fabian Hurter,et al.  4D GPS water vapor tomography: new parameterized approaches , 2011 .

[19]  T. Manning,et al.  Determining the 4D Dynamics of Wet Refractivity Using GPS Tomography in the Australian Region , 2014 .

[20]  Zhizhao Liu,et al.  Voxel-optimized regional water vapor tomography and comparison with radiosonde and numerical weather model , 2014, Journal of Geodesy.

[21]  Paul Tregoning,et al.  Accuracy of absolute precipitable water vapor estimates from GPS observations , 1998 .

[22]  Zhu Wen-yao,et al.  3D water-vapor tomography with Shanghai GPS network to improve forecasted moisture field , 2006 .

[23]  Kaihua Ding,et al.  Methods of determining weight scaling factors for geodetic–geophysical joint inversion , 2009 .

[24]  Arnold Wexler,et al.  Vapor Pressure Formulation for Water in Range 0 to 100 °C. A Revision , 1976, Journal of research of the National Bureau of Standards. Section A, Physics and chemistry.

[25]  Steven Businger,et al.  Sensing atmospheric water vapor with the global positioning system , 1993 .

[26]  Steven Businger,et al.  GPS Meteorology: Direct Estimation of the Absolute Value of Precipitable Water , 1996 .

[27]  Véronique Ducrocq,et al.  GPS zenith delay sensitivity evaluated from high‐resolution numerical weather prediction simulations of the 8–9 September 2002 flash flood over southeastern France , 2006 .

[28]  A. Niell.  Global mapping functions for the atmosphere delay at radio wavelengths , 1996 .

[29]  G. Ruffini,et al.  4D tropospheric tomography using GPS slant wet delays , 2000 .

[30]  Yanyan Liu,et al.  Near real-time water vapor tomography using ground-based GPS and meteorological data: long-term experiment in Hong Kong , 2014 .

[31]  Witold Rohm,et al.  Local tomography troposphere model over mountains area , 2008 .

[32]  Antonio Rius,et al.  Tomography of the lower troposphere using a small dense network of GPS receivers , 2001, IEEE Trans. Geosci. Remote. Sens..

[33]  Tobias Nilsson,et al.  Water vapor tomography using GPS phase observations: simulation results , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[34]  L. Mervart,et al.  Bernese GPS Software Version 5.0 , 2007 .

[35]  T. Herring,et al.  GPS Meteorology: Remote Sensing of Atmospheric Water Vapor Using the Global Positioning System , 1992 .