A Dynamic Smagorinsky Model for Horizontal Turbulence Parameterization in Tropical Cyclone Simulation

The horizontal turbulence parameterization is vital for the intensity and structure forecasting of tropical cyclone (TC) in numerical weather prediction (NWP) models. The default two‐dimensional (2D) standard Smagorinsky model with a single universal constant in Weather and Research Forecasting (WRF) model has been proven to be over dissipative for TC, leading to underprediction of TC intensity. This study provides the first attempt to implement the physically based 2D dynamic Smagorinsky model (DSM) for horizontal turbulence parameterization in WRF model for TC forecasts. The DSM dynamically computes the Smagorinsky coefficient as a function of the resolved flow during the simulation, avoiding the need to prescribe the coefficient a prior. The test results of the DSM in a TC NWP model show that the DSM can significantly improve the wind intensity forecasts compared to the standard Smagorinsky model. Plain Language Summary The representation of horizontal turbulent mixing in numerical models is important for the tropical cyclone (TC) forecasting. However, existing horizontal turbulence models (i.e., traditional Smagorinsky model with a constant coefficient) in numerical models underpredict the observed maximum surface wind speed. A dynamic Smagorinsky model with dynamically calculated coefficient according to the state of flow avoids the need for case‐by‐case tuning of the coefficient. The simulations of five TC cases using the dynamic Smagorinsky model present the improved wind intensity forecasts compared to the traditional Smagorinsky model. Key Points Standard Smagorinsky model with default constant coefficient is overly dissipative for tropical cyclone (TC), underpredicting TC intensity We are the first to attempt to implement the dynamic Smagorinsky model for horizontal turbulence parameterization in TC mesoscale numerical weather prediction model Dynamic Smagorinsky model significantly reduces the bias in TC intensity forecasts