Precipitable Water Vapor Retrieval from Shipborne GNSS Observations on the Korean Research Vessel ISABU

Abstract

We estimate precipitable water vapor (PWV) from data collected by the low-cost Global Navigation Satellite System (GNSS) receiver at a vessel. The dual-frequency GNSS receiver that the vessel ISABU is equipped with that is operated by the Korea Institute of Ocean Science and Technology. The ISABU served in the Pacific Ocean for scientific research during a period from August 30 to September 21, 2018. It also performs radiosonde observations to obtain a vertical profile of troposphere on the vessel’s path. The GNSS-derived PWV is compared to radiosonde observations and the Atmospheric Infrared Sounder (AIRS) on NASA’s Aqua satellite output. A bias and root-mean-square (RMS) error between shipborne GNSS-PWV and radiosonde-PWV were −1.48 and 5.22 mm, respectively. When compared to the ground GNSS-PWV, shipborne GNSS-PWV has a relatively large RMS error in comparison with radiosonde-PWV. However, the GNSS observations on the vessel are still in good agreement with radiosonde observations. On the other hand, the GNSS-PWV is not well linearly correlated with AIRS-PWV. The RMS error between the two observations was approximately 8.97 mm. In addition, we showed that the vessel on the sea surface has significantly larger carrier phase multipath error compared to the ground-based GNSS observations. This also can result in reducing the accuracy of shipborne GNSS-PWV. However, we suggest that the shipborne GNSS has sufficient potential to derive PWV with the kinematic precise point positioning (PPP) solution on the vessel.

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

“…Comparisons with radiosondes exhibit a large difference in rms (4.29 kg m −2 and 3.59 kg m −2 for REUN and KERG, respectively) and bias (2.54 kg m −2 and 2.36 kg m −2 for REUN and KERG, respectively), with radiosonde wetter than ship-borne GNSS. These differences are larger than those presented in the majority of previous studies [33,49], even if larger ones are also observed [54]. We have previously mentioned that such radiosondes (Modem M10) were mainly known to have dry bias, which is not the case here.…”

Machine Learning-Based Estimation of Hourly GNSS Precipitable Water Vapour

“…A validation of the GPS PWV using radiosonde data found the system to be highly accurate, with a mean bias of −0.1 mm and root mean square of 1.7 mm. A method for analysing PWV using a low-cost GNSS receiver installed onboard the ISABU vessel, operated by the Korea Institute of Ocean Science and Technology, was developed by Sohn et al, (2020) [26]. Radiosonde data and GNSS PWV were found to be in close agreement in their validation.…”