Abstract
Tropical cyclone intensity forecasts remain a significant challenge in forecast models. In-situ observations of TC thermodynamics are generally limited to dropsondes and aircraft data. Vertical profiling of TC thermodynamics from passive microwave and infrared sensors has been limited due to coarse vertical resolution and signal degradation. High vertical resolution Global Navigation Satellite System (GNSS) radio occultation (RO) soundings are insensitive to clouds and precipitation and provide a unique opportunity to study TC thermodynamic vertical structure. However, the quality of RO observations in the TC environment has not been thoroughly evaluated.
In this study, GNSS-RO profiles from COSMIC-1 (2006-2019) and COSMIC-2 (2019-2021) are analyzed in conjunction with colocated dropsondes in the TC environment. COSMIC-1 and COSMIC-2 demonstrate remarkable consistency in RO refractivity and bending angle for use across RO missions (less than ± 0.25% differences in the troposphere). Overall median refractivity difference between GNSS-RO profiles and colocated dropsondes are within ± 0.5% at altitudes of 8 km or higher, confirming the high quality of GNSS-RO measurements of the TC upper-troposphere with minimal bias. Below 8 km, refractivity differences generally range between −3% and −1% depending on altitude as well as subset criteria and sampling characteristics. Refractivity bias is mostly unaffected by colocation criteria, in terms of distance in space and time between observations, but accounting for RO ray path orientation significantly reduces biases. Results from this study indicate that additional RO colocation criteria are required in regions, or around weather events, that have large gradients in temperature and moisture to accurately represent their effects in an analysis. Thorough understanding of GNSS-RO quality in the TC environment will allow for the use of GNSS-RO in TCs to fill in observational gaps gaps to improve our understanding of TC thermodynamics and their evolution over time and provide more consistent observations for TC forecasting.
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