Conference,

Determination of water vapor content using low-cost dual-frequency GNSS receivers

, , , and .
(2021)

Abstract

With Global Navigation Satellite Systems (GNSS), the near real-time remote sensing of water vapor content in the troposphere is performed in Europe under the E-GVAP (http://egvap.dmi.dk/) program, but the spatial resolution is limited by the density of permanent GNSS stations. However, low-cost receivers are a promising solution to the problem of densifying GNSS networks, while the availability of multi-GNSS real-time corrections allows reducing the temporal latency, i.e. to switch to real-time processing. Although the performance of dual-frequency low-cost receivers was investigated in several geoscience applications, their use in GNSS meteorology has not been well documented yet. We used prototype dual-frequency low-cost units for GNSS meteorology and co-located them with the International GNSS Service (IGS) station WROC. During a test period of several weeks, we investigated the accuracy of real-time and near real-time Zenith Total Delay (ZTD) with respect to the IGS Final products. Several processing strategies and hardware configurations were investigated, that varied e.g. in GNSS selection, antenna model, and ground plate application. In best cases, we achieved accuracy better than 5 mm and 9 mm with survey-grade and patch antenna, respectively, despite the dynamic weather conditions. Finally, we deployed a network of 17 low-cost receivers in and around the city of Wroclaw, Poland. From estimated ZTDs and atmospheric parameters derived from a high-resolution numerical weather model WRF (Weather Research and Forecasting), we determined Integrated Water Vapor (IWV). We estimated the IWV uncertainty at a 95% confidence level of 2.3 kg per m2. We observed significant under- or over-determination of the IWV for the entire test area, as well as small-scale IWV variations with respect to WRF forecasts. Therefore we conclude, that low-cost receivers have a great potential of monitoring weather phenomena at a high spatio-temporal scale and support nowcasting application.

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