Continuous estimates of precipitable water vapor from Caribbean GPS stations during the 2007 Atlantic hurricane season [poster]
Braun, J., Iwabuchi, T., & Van Hove, T. (2007). Continuous estimates of precipitable water vapor from Caribbean GPS stations during the 2007 Atlantic hurricane season [poster]. In AGU Fall Meeting 2007. San Francisco, CA, US.
Hurricanes derive their strength through water vapor that is both evaporated from warm ocean surfaces and the existing moisture in the surrounding atmospheric environment. Observationally, there are relatively few instruments that can accurately measure water vapor in the presence of clouds and r... Show moreHurricanes derive their strength through water vapor that is both evaporated from warm ocean surfaces and the existing moisture in the surrounding atmospheric environment. Observationally, there are relatively few instruments that can accurately measure water vapor in the presence of clouds and rain. Retrievals of precipitable water vapor (PW) using Global Positioning System (GPS) stations may be the most reliable way to continuously monitor column integrated water vapor. This presents PW estimates collected during the 2007 Atlantic hurricane season derived from a network of GPS stations recently installed in the Caribbean. This network produced 30-minute estimates of PW from more than 20 stations in the region. Preliminary results indicate a wet bias in the Global Forecast System (GFS) analysis fields of approximately 1 mm in PW, with root mean square differences greater than 3 mm. Both of these statistical comparisons are significantly larger than those derived from analysis fields over the continental United States, which suggests that there is significant room for improvement in the initial conditions used for numerical weather prediction forecasts. It presents results in the temporal and spatial changes in PW as hurricanes Dean and Felix moved through the Caribbean. It also shows what impact these observations have on the Weather Research and Forecasting (WRF) model forecasts using various data assimilation strategies and methods. Show less