Regulating Fine-Scale Resolved Convection in High-Resolution Models for Better Hurricane Track Prediction

High-resolution atmospheric models are powerful tools for hurricane track and intensity predictions. Although using high resolution contributes to better representation of hurricane structure and intensity, its value in the prediction of steering flow and storm tracks is uncertain. Here we present experiments suggesting that biases in the predicted North Atlantic hurricane tracks in a high-resolution (approximately 3 km grid-spacing) model originates from the model's explicit simulation of deep convection. Differing behavior of explicit convection leads to changes in the synoptic-scale pattern and thereby to the steering flow. Our results suggest that optimizing small-scale convection activity, for example, through the model's horizontal advection scheme, can lead to significantly improved hurricane track prediction (& SIM;10% reduction of mean track error) at lead times beyond 72 hr. This work calls attention to the behavior of explicit convection in high-resolution models, and its often overlooked role in affecting larger-scale circulations and hurricane track prediction.

Automated summary

High-resolution atmospheric models are valuable in predicting hurricane track and intensity. Optimizing small-scale convection activity leads to significantly improved hurricane track predictions, with a mean track error reduction of over 10% beyond 72-hour lead times. This study highlights the importance of explicit convection behavior in high-resolution models and its impact on larger-scale circulations and hurricane track predictions.