Assessing the Impact of Cumulus Convection and Turbulence Parameterizations on Typhoon Precipitation Forecast

Abstract

Improving typhoon precipitation forecast with convection-permitting models remains challenging. This study investigates the influence of cumulus parameterizations and turbulence models, including the Reconstruction and Nonlinear Anisotropy (RNA) turbulence scheme, on precipitation prediction in multiple typhoon cases. Incorporating the cumulus and RNA schemes increases domain-averaged precipitation, improves recall scores, and lowers relative error across various precipitation thresholds, which is substantial in three out of four studied typhoon cases. Applying appropriate cumulus parameterization schemes alone also contributes to enhancing heavy precipitation forecasts. In Typhoon Hato, the RNA and Grell-3 schemes demonstrated a doubled recall rate for extreme rainfall compared to simulations without any cumulus scheme. The improved forecasting ability is attributed to the RNA's capacity to model dissipation and backscatter. The RNA scheme can dynamically reinforce typhoon circulation with upgradient momentum transport in the lower troposphere and enhance the buoyancy by favorable heat flux distribution, which is conducive to developing heavy precipitation.