Routine Measurement of Water Vapour Using GNSS in the Framework of the Map-Io Project

Abstract

The "Marion Dufresne Atmospheric Program - Indian Ocean" (MAP-IO) project is a research program that aims to collect long-term atmospheric observations in the under-instrumented Indian and Austral Oceans. As part of this project, a Global Navigation Satellite System (GNSS) antenna was installed on the research vessel (R/V) Marion Dufresne in October 2020. GNSS raw data is intended to be used to retrieve Integrated Water Vapour (IWV) content along the Marion Dufresne route, which cruises more than 300 days per year in the tropical and austral Indian Ocean. This paper presents a first assessment of this GNSS-based IWV retrieval, based on the analysis of 9 months of GNSS raw data acquired along the route of the R/V Marion Dufresne in the Indian Ocean. A first investigation of GNSS raw data collected during the first 5 months of operation has highlighted the bad positioning of the antenna on the R/V that makes it prone to interference. Changing the location of the antenna has been shown to improve the quality of the raw data. Then, ship-borne GNSS-IWV are compared with IWV estimates deduced using more conventional techniques such as European Centre for Medium-range Weather Forecasts (ECMWF) fifth reanalysis (ERA5), ground-launched radiosondes and permanent ground GNSS stations operating close to the route of the R/V Marion Dufresne. The rms difference of 2.79 kg m−2 shows a good match with ERA5 and subsequently improved after the change in location of the GNSS antenna (2.49 kg m−2). The match with ground-based permanent GNSS stations fluctuates between 1.30 and 3.63 kg m−2, which is also shown to be improved after the change in location of the GNSS antenna. However, differences with ground-launched radiosondes still exhibit large biases (larger than 2 kg m−2). Finally, two operational daily routine analyses (at day+1 and day+3) are presented and assessed: the rms of the differences are shown to be quite low (1 kg m−2 for the day+1 analyses, 0.7 kg m−2 for the day+3 analysis), which confirms the quality of these routine analysis. These two routine analyses are intended to provide a continuous monitoring of water vapour above the Indian Ocean and deliver ship-borne IWV with a low latency for the entire scientific community.

Sensitivity of Shipborne GNSS Estimates to Processing Modeling Based on Simulated Dataset

“…[ 67 ]. They are also commonly used elevation cut-off angles for processing shipborne GNSS data [ 22 , 23 , 27 , 68 ].…”

“…Several studies focused on the precise extraction of ZWD from raw GNSS measurements [ 18 , 19 , 20 , 21 , 22 , 23 ]. ZWD has long been estimated from ground GNSS antennas [ 20 , 24 , 25 , 26 , 27 ].…”

“…It is then important to obtain more data on the sea to study these phenomena. This is the reason why more and more studies are retrieving water vapor at sea by processing shipborne GNSS antennas [ 22 , 23 , 27 , 41 , 42 , 43 , 44 ].…”

MAP-IO, an atmospheric and marine observatory program onboard Marion Dufresne over the Southern Ocean

“…This restitution is common for fixed terrestrial GNSS antennas (Bosser and Bock, 2021) but is original for a ship-borne GNSS antenna, all the more so in the operational context of an atmospheric observatory. The complete methodology for GNSS IWV retrieval is described in Bosser et al (2022). The analysis of raw GNSS phase measurements is performed in PPP (Precise Point Positioning) mode.…”

Retrieving Accurate Precipitable Water Vapor Based on GNSS Multi‐Antenna PPP With an Ocean‐Based Dynamic Experiment

“…Many studies used PPP to obtain PWV and achieved comparable accuracy with relative positioning (Gendt et al., 2004; Byun & Bar‐Sever, 2009; X. Li et al., 2015). PPP was also applied to obtain oceanic PWV on floating platforms such as buoys and ships, which is helpful to expand GNSS meteorology to the vast ocean and benefits satellite altimetry and weather forecasting (Bosser et al., 2021, 2022; Fujita et al., 2008; Männel et al., 2021; Rocken et al., 2005; Wang et al., 2019). However, PPP generally requires a long convergence or reconvergence time, say, 20–30 min, before reaching cm‐level positioning accuracy (Kouba & Héroux, 2001).…”