Impact of CYGNSS Data on Hurricane Analyses and Forecast in Regional OSSEs with HWRF Model

The NASA Cyclone Global Navigation Satellite System (CYGNSS) has been planned for launch in October 2016. The satellite system will make frequent and accurate measurements of ocean surface winds throughout the life cycle of tropical storms and hurricanes with the aims to improve the hurricane intensity forecast. In this study, the impact of CYGNSS ocean surface wind data on numerical analyses and prediction of hurricanes is assessed with the NCEP operational hurricane weather research and forecasting (HWRF) model in a regional Observing System Simulation Experiment (OSSE) framework. The ECMWF nature run data at T511 resolution was used to provide the first guess, initial and boundary conditions for the OSSEs. The simulated CYGNSS data provided by University of Michigan are assimilated into HWRF by using the NCEP gridpoint statistical interpolation (GSI)-based hybrid data assimilation system. The OSSE results are verified by hurricane nature run developed by RSMAS/University of Miami and NOAA/AOML.

A series of OSSEs has been conducted in various cases in terms of data coverage, phases of hurricane evolution, and configurations of the data assimilation system. Results show that hurricane track and intensity forecasts with HWRF can be improved by assimilating the CYGNSS ocean surface wind speeds, although degrees of the improvement vary case by case. For instance, compared with the data thinning, assimilation of the super-obbed CYGNSS wind data results in larger improvements in hurricane track forecasts. Further analyses also indicate that the assimilation of CYGNSS ocean surface wind speeds can directly improve the surface wind field and enhance the low-level inflow, resulting in more efficient transportation of moisture air from environment filed into the vortex core region and thus lead to improved hurricane intensity forecast. Details will be presented during the conference.