The 2017 Hurricanes season was one of the most powerful severe weather events producing catastrophic socio-economic and environmental effects on the east coast of the United States. Therefore, tracking their path accurately is extremely useful. Today Global Navigation Satellite Systems (GNSS) tropospheric products, such as Zenith Wet Delays (ZWD), and Integrated Water Vapor (IWV) are used as complementary data sets in Numerical Weather Prediction (NWP) models. In this study, we employed GPS-derived IWV and horizontal tropospheric gradient information to monitor and investigate the complicated characteristics of hurricane events in their spatial and temporal distribution using a dense ground network of GPS stations. Our results show that a surge in GPS-derived IWV occurred several hours prior to the manifestation of the major hurricanes Harvey and Irma. We used the derived GPS-derived IWV information as input to spaghetti lines weather models, allowing us to predict the paths of Harvey and Irma hurricanes. As such, a parameter directly estimated from GPS can provide an additional resource for improving the monitoring of hurricane path
