2C.1 Role of In-Cloud Turbulent Mixing in the Eyewall and Rainbands in Tropical Cyclone Intensification

Monday, 16 April 2018: 10:30 AM
Champions ABC (Sawgrass Marriott)
Ping Zhu, Florida International Univ., Miami, FL; and B. P. Tyner, J. Zhang, S. Abarca, E. Aligo, F. D. Marks Jr., Z. Zhang, and V. Tallapragada

Turbulent mixing is commonly regarded as a planetary boundary layer (PBL) process. Indeed the turbulent PBL in fair-weather conditions is often cleanly separated from the free atmosphere above by a capping inversion. Except for the intermittent clear-sky turbulence, turbulent mixing is generally negligible above the PBL. The PBL schemes used in the state-of-the-art numerical modeling systems were designed to best represent this vertical structure of turbulent mixing. In the tropical cyclone environment, however, turbulence is no longer solely generated by the shear production and buoyance production associated with the surface processes. It can also be generated by the cloud processes above the PBL due to cloud-top radiative cooling, evaporative cooling, and inhomogeneous diabatic heating and cooling in the clouds. While the concept of PBL height may still be applicable in the TC environment, it cannot be simply defined based on turbulent mixing in the same way as that in fair-weather conditions. This is particularly true in the eyewall and convective rainbands since there is no physical interface that can separate the turbulence generated by the surface processes and cloud processes. Currently, no PBL schemes can appropriately account for the intense in-cloud turbulent mixing in the eyewall and rainbands. It is found that lack of consideration of intense in-cloud turbulent mixing in the eyewall and rainbands is one of the culprits for HWRF not to reproduce the observed intensity of several major TCs. In this study, we improve HWRF model physics by including in-cloud turbulent mixing parameterization in the operational HWRF PBL scheme. Our preliminary tests show that the revised PBL scheme improves the intensity forecasting skills of HWRF.
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