GNSS troposphere tomography based on two-step reconstructions using GPS observations and COSMIC profiles

Abstract. Traditionally, balloon-based radiosonde soundings are used to study the spatial distribution of atmospheric water vapour. However, this approach cannot be frequently employed due to its high cost. In contrast, GPS tomography technique can obtain water vapour in a high temporal resolution. In the tomography technique, an iterative or non-iterative reconstruction algorithm is usually utilised to overcome rank deficiency of observation equations for water vapour inversion. However, the single iterative or non-iterative reconstruction algorithm has their limitations. For instance, the iterative reconstruction algorithm requires accurate initial values of water vapour while the non-iterative reconstruction algorithm needs proper constraint conditions. To overcome these drawbacks, we present a combined iterative and non-iterative reconstruction approach for the three-dimensional (3-D) water vapour inversion using GPS observations and COSMIC profiles. In this approach, the non-iterative reconstruction algorithm is first used to estimate water vapour density based on a priori water vapour information derived from COSMIC radio occultation data. The estimates are then employed as initial values in the iterative reconstruction algorithm. The largest advantage of this approach is that precise initial values of water vapour density that are essential in the iterative reconstruction algorithm can be obtained. This combined reconstruction algorithm (CRA) is evaluated using 10-day GPS observations in Hong Kong and COSMIC profiles. The test results indicate that the water vapor accuracy from CRA is 16 and 14% higher than that of iterative and non-iterative reconstruction approaches, respectively. In addition, the tomography results obtained from the CRA are further validated using radiosonde data. Results indicate that water vapour densities derived from the CRA agree with radiosonde results very well at altitudes above 2.5 km. The average RMS value of their differences above 2.5 km is 0.44 g m−3.

[1]  G. Ruffini,et al.  Tropospheric Tomography using GPS Estimated Slant Delays , 2008 .

[2]  Witold Rohm,et al.  The precision of humidity in GNSS tomography , 2012 .

[3]  Christian Rocken,et al.  Validation of line‐of‐sight water vapor measurements with GPS , 2001 .

[4]  D. S. MacMillan,et al.  Atmospheric gradients from very long baseline interferometry observations , 1995 .

[5]  Richard G. Jones,et al.  An inter-comparison of regional climate models for Europe: model performance in present-day climate , 2007 .

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

[7]  Jim Galvin,et al.  Back to basics: Radiosondes: Part 1 –The instrument , 2003 .

[8]  S. Schlüter,et al.  A GPS based three-dimensional ionospheric imaging tool: Process and assessment , 2006 .

[9]  Xuenan Liu,et al.  Assessment of COSMIC radio occultation retrieval product using global radiosonde data , 2012 .

[10]  Gabor T. Herman Algebraic Reconstruction Techniques , 2009 .

[11]  Marie-Noëlle Bouin,et al.  GPS water vapour tomography: preliminary results from the ESCOMPTE field experiment , 2005 .

[12]  I. Shapiro,et al.  Geodesy by radio interferometry: Effects of atmospheric modeling errors on estimates of baseline length , 1985 .

[13]  L. J. Romans,et al.  Imaging the ionosphere with the global positioning system , 1994, Int. J. Imaging Syst. Technol..

[14]  Brian Golding,et al.  Pluvial flooding: new approaches in flood warning, mapping and risk management , 2009 .

[15]  Liang Xing-hui,et al.  Influence Analysis of Constraint Conditions on GPS Water Vapor Tomography , 2010 .

[16]  Debao Wen,et al.  Tomographic reconstruction of ionospheric electron density based on constrained algebraic reconstruction technique , 2010 .

[17]  Shuanggen Jin,et al.  GPS observations of the ionospheric F2-layer behavior during the 20th November 2003 geomagnetic storm over South Korea , 2008 .

[18]  J. Zumberge,et al.  Precise point positioning for the efficient and robust analysis of GPS data from large networks , 1997 .

[19]  Larry J. Romans,et al.  Observing tropospheric water vapor by radio occultation using the Global Positioning System , 1995 .

[20]  Christian Rocken,et al.  Obtaining single path phase delays from GPS double differences , 2000 .

[21]  H. Shao Assimilation of GPS Radio Occultation Observations , 2005 .

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

[23]  Jonathan H. Jiang,et al.  Global (50°S–50°N) distribution of water vapor observed by COSMIC GPS RO: Comparison with GPS radiosonde, NCEP, ERA-Interim, and JRA-25 reanalysis data sets , 2011 .

[24]  Giulio Ruffini,et al.  GPS tomography of the ionospheric electron content with a correlation functional , 1998, IEEE Trans. Geosci. Remote. Sens..

[25]  Chen-Joe Fong,et al.  FORMOSAT-3/COSMIC GPS Radio Occultation Mission: Preliminary Results , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[26]  W. Menke Geophysical data analysis : discrete inverse theory , 1984 .

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

[28]  Christian Rocken,et al.  The COSMIC/FORMOSAT-3 Mission: Early Results , 2008 .

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

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

[31]  Y. Gao,et al.  Real-time Water Vapor Sensing/Measurements with Precise Point Positioning Algorithm and Canadian Geodetic (GPS) Network , 2007 .

[32]  Y. Kuo,et al.  Global GNSS Radio Occultation Mission for Meteorology, Ionosphere & Climate , 2010 .

[33]  Giovanni Emilio Perona,et al.  Tomographic reconstruction of wet and total refractivity fields from GNSS receiver networks , 2011 .

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

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

[36]  Jiexian Wang,et al.  Inversion of Ionospheric Electron Density Based on a Constrained Simultaneous Iteration Reconstruction Technique , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Thomas A. Herring,et al.  Effects of atmospheric azimuthal asymmetry on the analysis of space geodetic data , 1997 .

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