Numerical Simulation on Retrieval of Meso-γ Scale Precipitable Water Vapor Distribution with the Quasi-Zenith Satellite System (QZSS)

摘要

 A simulation study was conducted to investigate the retrieval of meso-γ scale precipitable water vapor (PWV) distribution with the Quasi-Zenith Satellite System (QZSS) using output from a non-hydrostatic model (JMA NHM). The evaluation was performed on PWV values obtained by simulating three different methods: using all GPS satellites above an elevation angle higher than 10° (PWVG) (conventional Global Navigation Satellite System (GNSS) meteorology method), using only the QZSS satellite at the highest elevation (PWVQ), and using only the GPS satellite at the highest elevation (PWVHG). The three methods were compared by assuming the vertically integrated water vapor amounts of the model as true PWV. As a result, the root mean square errors of PWVG, PWVQ, and PWVHG were 2.78, 0.13, and 0.59 mm, respectively, 5 min before the rainfall. The time series of PWVHG had a large discontinuity (˜ 2 mm) when the GPS satellite with the highest elevation changed, while that of PWVQ was small because the elevation at which the highest QZSS satellites change was much higher. The standard deviation of PWVQ was smaller than those of PWVG and PWVHG, which vary significantly depending on GPS satellite geometry. When the spatial distributions of PWVG and PWVQ were compared to the meso-γ scale distribution of the reference PWV, PWVG smoothed out the PWV fluctuations, whereas PWVQ captured them well, due to the higher spatial resolution achievable using only high-elevation slant paths. These results suggest that meso-γ scale water vapor fluctuations associated with a thunderstorm can be retrieved using a dense GNSS receiver network and analyzing PWV from a single high-elevation GNSS satellite. In this study, we focus on QZSS, since this constellation would be especially promising in this context, and it would provide nearly continuous PWV observations as its highest satellite changes, contrary to using the highest satellites from multiple GNSS constellations.