Uncertainty Considerations for the Comparison of Water Vapour Derived from Radiosondes and GNSS
The integrated water vapour (IWV) can be estimated from the tropospheric delays of GNSS signals. These estimations are usually validated by radiosonde observations. However, very limited information is available on the precision of the IWV determined by radiosondes. In this paper the methodology of the computation of IWV retrieved from radiosonde data is revised using the atmospheric profiles of pressure, relative humidity and temperature. The formulae to calculate the uncertainty of the estimated values are derived, where the correlation of the neighbouring atmospheric layers is also taken into account. The results show that the mean uncertainty of the IWV from radiosonde observations reaches the level of ±0.26 kg/m2 in case of the Vaisala RS-92 radiosondes in Central and Eastern Europe. However, it increases to ±0.7 to 0.8 kg/m2 in summertime.Since the zenith hydrostatic delay (ZHD) must be modeled accurately to estimate the IWV from GNSS observations, the Saastamoinen, the Hopfield and the Black tropospheric delay models have been validated with ZHD values computed from radiosonde observations in Central Europe. Moreover some local models have also been derived in order to minimize the bias in IWV caused by the existing tropospheric models. In order to take the effect of the masses above the topmost level of the radiosonde profile in consideration, the International Standard Atmosphere has been used. Since the radiosonde observations terminate at different altitudes and pressure levels, which certainly affect the accuracy of the computed ZHD values, the omission error has been modeled with a simple exponential function. The results showed that the best ZHD model fitted to the radiosonde observations with the bias and standard deviation of +0.8 and ±1.2 mm, respectively. This means that the GNSS derived IWV is biased by −0.1 kg/m2. This value is approximately 50 % lower than the bias caused by the Saastamoinen model.Finally, the calculation of the scale factor between the zenith wet delay (ZWD) and the IWV is studied. Various models exist to determine this scale factor. There are models that derive the scale factor as a direct function of the surface temperature, while other models use a linear regression model of the surface temperature to compute the mean temperature of water vapour in the troposphere and derive the scale parameter from physical equations. Radiosonde profiles were used to test the two approached in Central and Eastern Europe. The results showed that the prior model showed no bias, while the latter one showed a relative bias of approximately 0.3 %.
KeywordsGNSS meteorology Integrated water vapour Radiosonde observations
The author acknowledges the support of the Hungarian Scientific Research Fund within the framework of project K-83909.
This work is connected to the “Development of quality-oriented and harmonized R + D + I strategy and functional model at BME” project. This project is supported by the New Hungary Development Plan (Project ID: TÁMOP-4.2.1/B-09/1/KMR-2010-0002).
The valuable comments of the anonymous reviewers are greatly appreciated. They helped to significantly improve the quality of the manuscript.
- Bosy J, Rohm W, Sierny J (2010) The concept of near real time atmosphere model based on the GNSS and meteorological data from the ASG-EUPOS reference stations. Acta Geodyn Geomater 7:253–263Google Scholar
- Dach R, Hugentobler U, Fridez P, Meindl M (2007) Bernese GPS software version 5.0. Astronomical Institute, University of Bern, BernGoogle Scholar
- Hofmann-Wellenhof B, Lichtenegger H, Wasle E (2008) GNSS: global navigation satellite systems. Springer, Wien New YorkGoogle Scholar
- Igondova M, Cibulka D (2010) Integrated water vapour and Zenith Total Delay time series and models over Slovakia and vicinity. Contrib Geophys Geod 40:299–312Google Scholar
- International Organization for Standardization (1975) ISO2533:1975 Standard Atmosphere. ISOGoogle Scholar
- Liu Y, Chen Y, Baki Iz H (2000) Precision of integrated water vapour from radiosonde data for GPS solutions. Geomatica 54:171–175Google Scholar
- Nash J, Oakley T, Vömel H, Wei L (2011) WMO intercomparison of high quality radiosonde systems. WMO instruments and observing methods, Report no. 107Google Scholar
- Rózsa S (2011) Estimation of integrated water vapour from GNSS observations using local models in Hungary. IAG Symp Ser 136:817–824Google Scholar
- Rózsa S, Weidinger T, Gyöngyösi AZ, Kenyeres A (2012) The role of the GNSS infrastructure in the monitoring of atmospheric water vapour. Időjárás Q J Hung Meteorol Serv 116:1–20Google Scholar
- Vaisala (2010) Vaisala Radiosonde RS92-D. Technical Data. Ref. B210763EN-B. http://www.vaisala.com/Vaisala%20Documents/Brochures%20and%20Datasheets/RS92-D-Datasheet-B210763EN-B-LoRes.pdf. Accessed 20 Oct 2012
- World Meteorological Organization (WMO) (2007) Observing stations and WMO catalogue of radiosondes WMO-No. 9, vol A. http://www.wmo.int/pages/prog/www/ois/volume-a/vola-home.htm. Accessed 20 Oct 2012
- World Meteorological Organization (WMO) (2008) Guide to meteorological instrument and methods of observations. WMO-No. 8Google Scholar