A typhoon is a highly destructive weather event, causing severe damage and losses to the economy and lives. The impacts of typhoons can be mitigated by adequately predicting their path in advance. The progress of a typhoon can be investigated by analyzing the significant changes in Precipitable Water Vapor (PWV) in a given region. Global Navigation Satellite Systems
(GNSS) have been utilized to monitor the changes in PWV since GNSS meteorology was introduced in the early 1990s. In this study, PWV variations of two typhoons, Soulik and Kongrey in 2018, are investigated based on GNSS derived PWV (GNSS-PWV). The variations in GNSS-PWV are used for predicting the typhoon path, provided that the movement direction of PWV corresponds to that of a typhoon. In the study area, GNSS stations are densely distributed like an array so that the array enables to monitor the variations of PWV over the region more sensibly than a conventional PWV monitoring method, which is the radiosonde based PWV observations (RS-PWV). Comparing the results of GNSS-PWV to the RS-PWV, GNSS-PWV is proved to be comparable and even provides better spatiotemporal resolution. It should be noted that the GNSS-PWV is investigated with meteorological parameters together during a typhoon event. The pattern of GNSS-PWV showed the opposite pattern of air pressure, which is one of the essential factors in conventional typhoon forecast methods. As the GNSS-PWV and the pressure are strongly correlated, this study only focused on the GNSS-PWV for analyzing the meteorological variations and predicting the path of typhoons. To predict the typhoon’s subsequent location, the research proposes the concept of Predicted Location of Typhoon (PLT). PLT is calculated as follows: 1) estimated all the GNSS-PWV in the study area during a typhoon event. 2) The top five GNSS stations showing the highest PWV are selected. 3) The 2-dimension mean position of them is calculated. 4) This position indicates the PLT corresponding to the process of the typhoon. The research model predicted a typhoon’s subsequent location approximately 5 hours in advance with an average distance discrepancy of 14 km in two experiments. When the typhoon approaches the landfall point, the research model managed to predict the landfall point, approximately 5.3 hours in advance, with an average distance discrepancy from the actual path of 13.19 km in two experiments. Although the proposed method used only GNSS-PWV using existing GNSS stations, it still yielded comparable results of typhoon paths, comparing those issued by Korea Meteorological Administration (KMA), who considerably applied the conventional and certified method for forecasts. These results imply that GNSS-PWV is promising for the prediction of the typhoon path seamlessly in a cost-effective and environmentally friendly manner, and it can be used for the supplementary information for the current typhoon forecast.
Keywords: GNSS meteorology, PWV, Typhoon, Predicted Location of Typhoon