The Impacts of GPS Radio Occultation Data on Tropical Cyclone Prediction

The atmospheric limb sounding technique making use of radio signals transmitted by the Global Position System (GPS) satellites has emerged as a powerful and relatively inexpensive approach for sounding the global atmosphere in all weather. As demonstrated by the proof-of-concept GPS Meteorology (GPS/MET) experiment and more recently by the CHAMP and SAC-C missions, the GPS radio occultation (RO) sounding data are shown to be of high precision, accuracy and vertical resolution. The launch of the joint U.S.-Taiwan COSMIC/FORMOSAT-3 (hereafter COSMIC) mission, a constellation of six microsatellites, in April 2006 marked the beginning of a new era of GPS atmospheric remote sensing. Since its launch, COSMIC has been providing large number GPS RO soundings to support the research and operational communities. As of December 2009, COSMIC has taken more than 2 million neutral atmospheric RO soundings, serving more than 1200 users from 54 countries. The COSMIC system is designed to provide ~2,000 GPS RO soundings per day, uniformly distributed around the globe. With the ability to penetrate deep into the lower troposphere using an advanced open-loop tracking technique, COSMIC data have shown the capability to observe the structure of the tropical atmospheric boundary layer, providing valuable information on low-level atmospheric water vapor. This is particularly important for tropical cyclone prediction. In this paper, we will present results from real data experiments that examine the impacts of GPS RO data on tropical cyclone prediction. We show that the assimilation of COSMIC GPS RO data can significantly improve the model's ability to predict the genesis of tropical storms, as well as the forecast of hurricanes including both track and intensity. NOAA is now actively planning a COSMIC follow-on mission, known as COSMIC-II, in collaboration with Taiwan. Observing system simulation experiments show that different constellation design can significantly impact the data density over the tropics, and consequently, the prediction of tropical cyclones.