The impact of assimilating CYGNSS Data on Tropical Cyclone Analyses and Forecasts in a Regional OSSE Framework

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Tuesday, 30 June 2015: 8:45 AM
Salon A-2 (Hilton Chicago)
Brian D. McNoldy, Univ. of Miami/RSMAS, Miami, FL; and B. Annane, J. Delgado, L. Bucci, R. Atlas, and S. J. Majumdar

The Cyclone Global Navigation Satellite System (CYGNSS), to be launched in October 2016, is a constellation of eight micro-satellites that will utilize direct and reflected GPS signals to retrieve wind speed data at the ocean surface. The orbits are designed to optimize coverage over the tropics and subtropics, resulting in more frequent revisits over tropical cyclones than is possible with current scatterometers. Furthermore, CYGNSS will be able to retrieve winds under all precipitation conditions, and over a large range of wind speeds in a tropical cyclone.

The objective of this study is to assess quantitatively the effect that the assimilation of future CYGNSS data would have on operational-quality regional model analyses and forecasts of tropical cyclone track, structure and intensity. To accomplish this, an Observing System Simulation Experiment (OSSE) has been designed. A 'true' state is represented by a calibrated, high-resolution 'nature run' simulation of a tropical cyclone using the WRF-ARW model. Synthetic CYGNSS wind speed data with appropriate error characteristics are then sampled from the 13-day nature run. Using NOAA's Gridpoint Statistical Interpolation (GSI) data assimilation scheme and Hurricane Weather Research and Forecast (HWRF) model, the impact of assimilating CYGNSS data is then evaluated. Results indicate a consistent improvement to analyses and short-term forecasts, with a larger volume of lower-resolution data being more effective than a smaller volume of higher-resolution (and lower quality) data. Work to incorporate the hybrid GSI / Ensemble Kalman Filter data assimilation scheme used at NCEP is ongoing.