The Method to Inverse PWV Using VMF1 Grid Data
In this paper, the method interpolating Vienna Mapping Function (VMF1) grid data to estimate Precipitable Water Vapor (PWV) is introduced. Using precise point positioning technique, the PWV values are estimated without external meteorological measurements
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The Method to Inverse PWV Using VMF1 Grid Data Min Wong, Hongzhou Chai, Zongpeng Pan and Yanli Chen
Abstract In this paper, the method interpolating Vienna Mapping Function (VMF1) grid data to estimate Precipitable Water Vapor (PWV) is introduced. Using precise point positioning technique, the PWV values are estimated without external meteorological measurements. The experiment results demonstrate a good agreement with those derived with current state-of-the-art procedure that relies on site-specific meteorological observations. The zenith path delay (ZPD) results are compared with International GNSS Service (IGS) troposphere product and the RMS of differences are within 8.3 mm. Compared with sounding data, the RMS of the PWV difference are less than 5 mm, which preliminarily validates the feasibility to apply this method to regional weather monitoring. Keywords Precipitable water vapor Precise point positioning
Vienna
Mapping Function 1 (VMF1)
27.1 Introduction Since the technique to inverse the Precipitable Water Vapor (PWV) from Global Positioning System (GPS) signal was come up two decades ago [2], the method has been widely applied because of its advantages such as high spatial and temporal resolution, low observation cost and immunity from bad weather condition. However, the conventional ground GPS-based PWV estimation needs ground
M. Wong (&) H. Chai Z. Pan Institute of Geospatial Information, Information Engineering University, Zhengzhou 450052, China e-mail: [email protected]; [email protected] Y. Chen 68011 Troops of PLA, Lanzhou 730030, China
J. Sun et al. (eds.), China Satellite Navigation Conference (CSNC) 2013 Proceedings, Lecture Notes in Electrical Engineering 244, DOI: 10.1007/978-3-642-37404-3_27, Ó Springer-Verlag Berlin Heidelberg 2013
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meteorological parameter derived with external sensors, which increases the construction cost of observation station and limits its application range. The applications of Numerical Weather Models (NWM) provided by the United States National Centers for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) have provided an alternative way to obtain surface meteorological data. Schüler [8] validate the possibility to estimate PWV using meteorological parameters interpolated from NCEP Global Data Assimilation System (GDAS) reanalysis data. Jade and Vijayan [7] compute the PWV time series in Indian subcontinent based on NCEP reanalysis data, and the accuracy of PWV values is comparable to those from radiosonde measurement. Chang and He [5] perform a similar technique to estimate high accuracy PWV values over Shanghai, which shows the technique can be helpful to the precipitation forecast of the hydropower station with GPS even when nearby meteorological sensors are unavailable. The Vienna Mapping Function (VMF1) grid data are released by the University of Technology of Vienna, which contain the Zenith Hydrostatic Delay (ZHD) and Zenith Wet Delay (ZWD) derived from ECMWF-NWM and para
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