Analyzing the Onset of Tropical Cyclone Rapid Intensification with an Adjoint-Derived Ensemble

The onset of tropical cyclone (TC) rapid intensification (RI) remains a challenge as there is still uncertainty about key processes important for development. Uncertainty in the environment surrounding the TC can add additional challenges to predicting the onset of RI. Through targeted changes to a variety of the TC’s structure and/or its surrounding environment (e.g., sea level pressure, wind speed, moisture, upper-level divergence), an ensemble can be made where members seek to change a particular aspect of the forecast. Adjoint-derived sensitivities and perturbations can be used to evaluate the importance of these processes and environmental conditions, as perturbations to the forecast are dependent on the definition of the response function (e.g., forecast aspect being evaluated). This concept has been successfully developed and applied in the mid-latitudes for a mesoscale convective system (Xu et al., 2001). Since adjoint sensitivities typically contain both structured mesoscale, and synoptic-scale patterns along with smaller-scale, highly noisy patterns it is possible that by using the ensemble approach, the dominant physical processes can be revealed. Evaluating the evolution of perturbations and variance of the ensemble members can elucidate the likely scenarios for potential impacts to development throughout the forecast. Furthermore, through analyzing initial perturbation distribution, regions where there is enhanced uncertainty for how the TC will develop can be elucidated.

The presented work uses the U. S. Navy's Coupled-Ocean Atmosphere Mesoscale Prediction System (COAMPS) and its adjoint to evaluate the application of an adjoint-derived ensemble for TCRI onset. Through this approach, the ensemble is designed to capture the potential spread in the forecast to assess the predictability of the TC. This includes what regions and processes in the forecast are important for multiple metrics related to development. Results can be compared to typical ensemble members to understand the ability to capture the forecasted spread in intensity and track.