Hurricane Dynamics and Predictability: Coupling boundary-layer to cloud observations in a Nonlinear Data Assimilation Framework

The tropical cyclone (TC) boundary layer is the interface where energy is transferred from the ocean surface to the vortex aloft and plays a critical role in the TC intensification. Previous studies have shown that coherent turbulent structures (CTSs) in the TC boundary layer can be involved in inverse energy cascade to larger-scale vortex via wave-mean flow nonlinear interactions (Guimond et al., 2018; Li and Pu, 2023; Sroka and Guimond, 2021). However, the role of CTSs in shaping the TC structure, and thus predictability of TC, remains unclear. We will use the nonlinear data assimilation (DA) to examine the nonlinear interaction of the observed CTSs with the vortex thermodynamics. Specifically, we use the Particle Flow Filter, a fully nonlinear DA technique for high-dimensional geophysical systems (Van Leeuwen et al. 2019) and is now available in JEDI (Hu and Van Leeuwen, 2021). This filter is fully nonlinear, appropriate for the highly nonlinear Hurricane physics in the boundary layer. The working of the filter is similar to that of ensemble 3DVars, with the difference that the ensemble members communicate with one another during the minimizations, and the prior does not have to be Gaussian (Hu and Van Leeuwen, 2021).

In this research, we will perform data denial experiments by the state-of-the-art Model for Prediction Across Scales (MPAS), leveraging high-resolution (125 m horizontal and 30 m vertical grid spacing) Doppler radar observations in the TC boundary layer with the particle Flow Filter. We aim to investigate how to use fully nonlinear DA methods to integrate turbulence resolving radar observations into the numerical model; examine the influence of CTSs on the predictability of a hurricane; and to analyze the dynamical pathways from the CTSs to the large-scale vortex during intensification. At the very first step, we examine the CTSs in radar observations and compare with 3-km non-DA MPAS simulation. Preliminary results show that MPAS underestimates the activity of CTSs and their contribution to the energy spectrum in boundary layer, thus underestimating TC intensification rate. This highlights the importance of turbulence-resolving simulation coupling with nonlinear DA, which could improve MPAS model capability to resolve CTSs, and therefore the predictability of TC intensity.