Optimization of GPS water vapor tomography technique with radiosonde andCOSMIC historical data

Abstract. The near-real-time high spatial resolution of atmospheric water vapor distribution is vital in numerical weather prediction. GPS tomography technique has been proved effectively for three-dimensional water vapor reconstruction. In this study, the tomography processing is optimized in a few aspects by the aid of radiosonde and COSMIC historical data. Firstly, regional tropospheric zenith hydrostatic delay (ZHD) models are improved and thus the zenith wet delay (ZWD) can be obtained at a higher accuracy. Secondly, the regional conversion factor of converting the ZWD to the precipitable water vapor (PWV) is refined. Next, we develop a new method for dividing the tomography grid with an uneven voxel height and a varied water vapor layer top. Finally, we propose a Gaussian exponential vertical interpolation method which can better reflect the vertical variation characteristic of water vapor. GPS datasets collected in Hong Kong in February 2014 are employed to evaluate the optimized tomographic method by contrast with the conventional method. The radiosonde-derived and COSMIC-derived water vapor densities are utilized as references to evaluate the tomographic results. Using radiosonde products as references, the test results obtained from our optimized method indicate that the water vapor density accuracy is improved by 15 and 12 % compared to those derived from the conventional method below the height of 3.75 km and above the height of 3.75 km, respectively. Using the COSMIC products as references, the results indicate that the water vapor density accuracy is improved by 15 and 19 % below 3.75 km and above 3.75 km, respectively.

[1]  Liu Yan Precise Determination of Dry Zenith Delay for GPS Meteorology Applications , 2000 .

[2]  Ying-Hwa Kuo,et al.  Comparison of GPS radio occultation soundings with radiosondes , 2005 .

[3]  H. Black An easily implemented algorithm for the tropospheric range correction , 1978 .

[4]  E. García‐Ortega,et al.  Verification of the MM5 model using radiosonde data from Madrid–Barajas Airport , 2013 .

[5]  Alain Geiger,et al.  Determination of the spatial and temporal variation of tropospheric water vapour using CGPS networks , 2006 .

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

[7]  O. Bock,et al.  Diurnal Cycle of Water Vapor as Documented by a Dense GPS Network in a Coastal Area during ESCOMPTE IOP2 , 2007 .

[8]  Zhizhao Liu,et al.  GNSS troposphere tomography based on two-step reconstructions using GPS observations and COSMIC profiles , 2013 .

[9]  David D. Turner,et al.  Mesoscale GPS tomography applied to the 12 June 2002 convective initiation event of IHOP_2002 , 2009 .

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

[11]  Galina Dick,et al.  GPS tomography: validation of reconstructed 3-D humidity fields with radiosonde profiles , 2013 .

[12]  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 .

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

[14]  James Foster,et al.  GPS Meteorology: Sliding-Window Analysis* , 2005 .

[15]  H. S. Hopfield Tropospheric Effect on Electromagnetically Measured Range: Prediction from Surface Weather Data , 1971 .

[16]  Joao P. S. Catalao,et al.  On the inclusion of GPS precipitable water vapour in the nowcasting of rainfall , 2015 .

[17]  Armin Raabe,et al.  Preconditions to ground based GPS water vapour tomography , 2007 .

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

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

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

[21]  Witold Rohm,et al.  Near real-time estimation of water vapour in the troposphere using ground GNSS and the meteorological data , 2012 .

[22]  G. Veis The Use of Artificial Satellites for Geodesy , 1963 .

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

[24]  Dorota A. Grejner-Brzezinska,et al.  GPS-PWV estimation and validation with radiosonde data and numerical weather prediction model in Antarctica , 2012, GPS Solutions.

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

[26]  Thomas M. Hamill,et al.  Conditional Probabilities of Significant Tornadoes from RUC-2 Forecasts , 2000 .

[27]  J. Stoer,et al.  Introduction to Numerical Analysis , 2002 .

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

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

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

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

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

[33]  Steven Businger,et al.  GPS Meteorology: Mapping Zenith Wet Delays onto Precipitable Water , 1994 .

[34]  Thomas A. Herring,et al.  Impact of a priori zenith hydrostatic delay errors on GPS estimates of station heights and zenith total delays , 2006 .

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

[36]  Pedro Benevides,et al.  Bridging InSAR and GPS Tomography: A New Differential Geometrical Constraint , 2016, IEEE Transactions on Geoscience and Remote Sensing.

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

[38]  Giovanni Nico,et al.  Maps of PWV Temporal Changes by SAR Interferometry: A Study on the Properties of Atmosphere's Temperature Profiles , 2014, IEEE Geoscience and Remote Sensing Letters.

[39]  Douglas Hunt,et al.  Estimates of the precision of GPS radio occultations from the COSMIC/FORMOSAT‐3 mission , 2007 .

[40]  The Use of Hourly Model-Generated Soundings to Forecast Mesoscale Phenomena. Part II: Initial Assessment in Forecasting Nonconvective Strong Wind Gusts , 1999 .

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

[42]  Z. Adavi,et al.  4D tomographic reconstruction of the tropospheric wet refractivity using the concept of virtual reference station, case study: northwest of Iran , 2014, Meteorology and Atmospheric Physics.

[43]  Bob Hardy,et al.  ITS-90 FORMULATIONS FOR VAPOR PRESSURE, FROSTPOINT TEMPERATURE, DEWPOINT TEMPERATURE, AND ENHANCEMENT FACTORS IN THE RANGE -100 TO +100 C , 1998 .