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Journal Articles Sensors Year : 2019

Analysis and Comparison of GPS Precipitable Water Estimates between Two Nearby Stations on Tahiti Island

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Jean-Pierre Barriot
Guochang Xu
  • Function : Author
  • PersonId : 1047957
Marania Hopuare
  • Function : Author
  • PersonId : 997574

Abstract

Since Bevis first proposed Global Positioning System (GPS) meteorology in 1992, the precipitable water (PW) estimates retrieved from Global Navigation Satellite System (GNSS) networks with high accuracy have been widely used in many meteorological applications. The proper estimation of GNSS PW can be affected by the GNSS processing strategy as well as the local geographical properties of GNSS sites. To better understand the impact of these factors, we compare PW estimates from two nearby permanent GPS stations (THTI and FAA1) in the tropical Tahiti Island, a basalt shield volcano located in the South Pacific, with a mean slope of 8% and a diameter of 30 km. The altitude difference between the two stations is 86.14 m, and their horizontal distance difference is 2.56 km. In this paper, Bernese GNSS Software Version 5.2 with precise point positioning (PPP) and Vienna mapping function 1 (VMF1) was applied to estimate the zenith tropospheric delay (ZTD), which was compared with the International GNSS Service (IGS) Final products. The meteorological parameters sourced from the European Center for Medium-Range Weather Forecasts (ECMWF) and the local weighted mean temperature (T m) model were used to estimate the GPS PW for three years (May 2016 to April 2019). The results show that the differences of PW between two nearby GPS stations is nearly a constant with value 1.73 mm. In our case, this difference is mainly driven by insolation differences, the difference in altitude and the wind being only second factors.
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Dates and versions

hal-03155673 , version 1 (02-03-2021)

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Fangzhao Zhang, Jean-Pierre Barriot, Guochang Xu, Marania Hopuare. Analysis and Comparison of GPS Precipitable Water Estimates between Two Nearby Stations on Tahiti Island. Sensors, 2019, 19 (24), pp.5578. ⟨10.3390/s19245578⟩. ⟨hal-03155673⟩

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