Is GOES-R Seeing "Hottish" Convective Towers in Hurricanes?

The placement of deep convection relative to the radius of maximum wind of tropical cyclones provides an indication of future changes intensity – latent heat release in the inner-core drives the secondary circulation. Given the difficulty with identifying the radius of maximum wind, the presence of satellite-based convective parameters such as lightning and tropical overshooting tops in the inner-core region of tropical cyclones have been shown to signify either the end of rapid intensification or the process of rapid weakening. This mixed signal complicates the usability of both metrics as indicators of short-term intensity changes. With the next generation of geostationary satellites, high temporal resolution imagery via the mesoscale sectors and additional hydrometer information in the CLAVR-x based GOES-R Level 2 products are providing crucial insight into quantifying the properties of eyewall convection.

Using GOES-16 Advanced Baseline Imager data during the 2017 and 2018 Atlantic and Eastern North Pacific hurricane seasons, this work demonstrates that the convective towers in the inner-core region of tropical cyclones have a unique hydrometer signal during the rapid intensification process. GOES-16 captures inner-core convective towers lofting large ice crystals to cloud top – a signal that is opposite of the glaciation observed in the middle-latitude thunderstorms where vigorous, overland updrafts are associated with small cloud-top ice crystals. While perfectly undiluted conditions likely only exist in the boundary layer of tropical cyclones, the convective towers in the eyewall must be reasonably undiluted or "hottish" to grow large ice crystals. Using a combination of CloudSat overpasses and idealized modeling, the physical mechanisms associated with the large ice crystal signature in the eyewall convection observed by GOES-16 are characterized. This information is used to explore applications to statistical–dynamical tropical cyclone intensity models and short-term forecasting.

Disclaimer: The views, opinions, and findings contained in this article are those of the authors and should not be construed as an official National Oceanic and Atmospheric Administration (NOAA) or U.S. Government position, policy, or decision.