515 Thermodynamic Structure and Evolution of the Tropical Cyclone Middle Troposphere as Observed by GNSS Radio Occultation Profling

Tuesday, 30 January 2024
Hall E (The Baltimore Convention Center)
Kevin J. Nelson, Jet Propulsion Laboratory, Pasadena, CA; and C. O. Ao

Long-range tropical cyclone (TC) intensity forecasts remain one of the biggest challenges in numerical weather prediction models and forecast errors have been attributed to several observational deficits. It has been noted in previous studies that the TC middle troposphere is under-observed, and that remote sensing techniques are ideal for filling the observational gaps. In-situ observations of the TC middle troposphere are generally limited to targeted dropsondes launched at higher altitudes that pass through as they fall. Remote sensing methods offer additional information about TCs, particularly in areas where in-situ observations are sparse. Vertical profiling of TC thermodynamics from conventional passive microwave and infrared sensors has historically been limited due to coarse vertical resolution and signal degradation from clouds and precipitation. High vertical resolution Global Navigation Satellite System (GNSS) radio occultation (RO) soundings are insensitive to clouds and precipitation and provides a unique opportunity to study TC thermodynamic vertical structure.

In this study, GNSS RO profiles from COSMIC-1 (2006-2019) and COSMIC-2 (2020-2023) colocated to TC tracks are analyzed in conjunction with dropsondes and model reanalysis in the TC environment. Vertical profiles of atmospheric refractivity, temperature, and moisture are binned radially outward from the center of their respective TCs are used to create median profile composites at each distance. Subsets of the colocated profiles are also used to determine differences in vertical thermodynamic structure between different TC intensities and TC quadrants. A more complete understanding of the vertical thermodynamic structure of the TC middle troposphere and its evolution over time will likely improve model representation and reduce model forecast errors.

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner