Abstract
Remote sensing of water vapor using Global Navigation Satellite Systems (GNSS) is a well-established tool for weather and climate monitoring. The current challenges of GNSS meteorology are real-time performance and the inclusion of emerging GNSS, such as Galileo. We demonstrate that real-time GPS-only, Galileo-only, and GPS+Galileo solutions are consistent among each other. However, our results show that the Galileo-only solutions tend to underestimate Zenith Total Delay (ZTD) with respect to GPS. The Galileo-only real-time ZTD is less accurate as the one from GPS. The combination of both GNSS leads to a superior product. The daily solution availability increases by up to 50%, and the overall gain is 0.7% over the entire year. The accuracy improves by 3.7% to 8.5% and uncertainty is reduced by a factor of 1.5–2. A combined GPS and Galileo solution suppresses artifacts in a real-time ZTD product which otherwise would be attributed to high-frequency orbital effects.
Highlights
R EMOTE sensing of the troposphere using Global Navigation Satellite Systems (GNSS), called GNSS meteorology, is a well-established tool for weather and climate monitoring [1]
We demonstrate that Galileo and supporting services are already mature enough to provide a reliable information on troposphere state in real-time
A major improvement of the Galileo-only solution is noticed after February 11, 2019, since the newest four satellites are supported in the real-time service
Summary
R EMOTE sensing of the troposphere using Global Navigation Satellite Systems (GNSS), called GNSS meteorology, is a well-established tool for weather and climate monitoring [1]. The standard product of GNSS meteorology is the zenith total delay (ZTD), which is operationally assimilated into numerical weather models by meteorological agencies [2]. Horizontal gradients and slant total delays are still considered as advanced tropospheric products, for which the assimilation has been performed only in a few case studies [3]. Using ZTD together with pressure and temperature determined from in situ measurements or a numerical weather model, integrated water vapor (IWV) content can be quantified with a similar accuracy as classical meteorological sensors, that is, water vapor radiometers [4]. The drawback of GNSS is that the most accurate results are obtained in the postprocessing mode, for which products are available with a significant latency due to batch
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