Storm Surge Modeling with CYGNSS Winds

Hurricane storm surge is a problem of growing importance due to increasing population density along the coasts, rising sea levels, and the possibility of greater frequency and severity of storm occurrence. Storm surge modeling can provide guidance for warning and evacuation systems. However, modeling accuracy, particularly with respect to the intensity forecast (as opposed to storm track) is limited by a lack of high resolution meteorological data. Currently, methods of data collection within a tropical cyclone's eyewall are limited to `hurricane hunter' type aircraft and dropsondes. Neither of these methods can provide the needed temporal and spatial data sampling necessary for accurate storm surge forecasting. We examine the potential to improve storm surge forecast skill using simulated, high-temporal resolution remote sensing data products from NASA's Cyclone Global Navigation Satellite System (CYGNSS) mission. We present and compare ADCIRC 2DDI storm surge hindcasting results of Hurricane Irene using four meteorological forcing scenarios: 1) "True" meteorological data obtained from HWRF reanalysis runs; 2) "Worst-case forecast" using low-resolution NOGAPS forecast wind and pressures; 3) "Best-case forecast" using high-resolution HWRF forecast winds and pressures; and 4) a simulated "CYGNSS forecast" with wind field given by a parameterized model trained using CYGNSS-derived values for the maximum wind speed and radius of maximum winds. The results suggest that the increased spatial and temporal resolution of the CYGNSS-derived winds have a positive impact on storm surge modeling predictions and that the mission's data products will improve storm surge forecasts.