Leveraging Ensemble Prediction Developments in HAFS to Evaluate Tropical Cyclone Hazards

Few studies have addressed the impact of data assimilation (DA) assumptions on tropical cyclone (TC) prediction despite numerous examinations of the influence of observations, their errors, and their coverage. Results presented capitalize on experimental ensemble DA configurations within NOAA’s Hurricane Analysis and Forecast System (HAFS) that encompass the entirety of the Atlantic and Eastern Pacific TC basins, cycling over a large, fixed regional domain. An assessment of TC characteristics will be shown using forecasts initialized from the operational DA method (Ensemble Kalman Filter – 3D Variational Hybrid) alongside non-Gaussian ensemble DA methods such as the localized particle filter and its variants. These emerging DA techniques show promise for known NWP issues related to nonlinear model dynamics, as existing operational DA strategies assume Gaussian and linear error characteristics which inhibit the optimal use of observations for initialization. Furthermore, TC-oriented modeling systems perform cycling DA on regional storm-following nests initialized only after individual TCs have been identified, thus limiting data to regions within high-resolution nests and preventing land-based observations from providing substantial benefit to long-term TC forecasts. Results reveal benefits for TC prediction that have previously been omitted due to a limited use of high-resolution ensemble DA over extensive domains. A comparison of ensemble-based DA methods highlights the importance of reliable probabilistic predictions for interpreting impacts to TC-focused metrics, such as track, intensity, and surface wind field extent. While impacts to TC track and intensity will be discussed, results focus on TC surface wind structure, as the push towards more hazard-based metrics becomes favored. Probabilistic quantities such as ensemble mean and spread of various TC metrics will be considered.