Assessing the impact of assimilating CYGNSS ocean surface winds on tropical cyclone analyses and forecasts with regional OSSEs

Tuesday, 19 April 2016: 2:45 PM
Ponce de Leon C (The Condado Hilton Plaza)
Shixuan Zhang, University of Utah, Salt Lake City, UT; and Z. Pu
Manuscript (1.2 MB)

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 Experiments (OSSE) framework. The NCEP hybrid gridpoint statistical interpolation (GSI) data assimilation system is employed. The regional nature run developed by RSMAS/University of Miami and NOAA/AOML is used for generating the simulated observations and verification purpose. A series of simulated CYGNSS ocean surface winds have been provided by University of Michigan. The ECMWF nature run data at T511 resolution was used to provide the first guess, initial and boundary conditions for the OSSEs. Various data assimilation and sensitivity studies have been conducted for rapid intensification and mature phases of a hurricane during 28 July to 11 August 2005 over the Atlantic Ocean. The results show that: 1) Assimilation of CYGNSS ocean surface winds improves HWRF analyses and forecasts of the hurricane in both its rapid intensification and mature phases. 2) Compared with non-cycled data assimilation, cycled data assimilation leads to better hurricane forecasts. Specifically, 3 hourly cycled data assimilation results in best analyses and forecasts among all the experiments. 3) Assimilation of CYGNSS data can lead to better initial position of hurricane center and significant improvement in the track forecast. More importantly, the data around the inner core region play an important role in improving both track and intensity forecast. 4) The degree of CYGNSS data impacts depends on the configuration of the HWRF vortex initialization and GSI data assimilation. When HWRF vortex relocation and intensity correction are not presented in the analysis cycle, the assimilation of CYGNSS data has larger impact on the track and intensity forecasts, whereas marginal positive impact is found in the short-range forecasts when HWRF vortex relocation and intensity correction are presented in the analysis cycle. Nevertheless, in some cases, assimilation of CYGNSS data helps mitigating the initial vortex spin-down problem.
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