The Forecasts of Hurricanes using Large-Ensemble Outputs (FHLO) model is a probabilistic tropical cyclone (TC) forecast framework that quantifies the forecast uncertainty of a TC. This is achieved by generating probabilistic forecasts of track, intensity, and wind speed that incorporate the state-dependent uncertainty in the large-scale field. The main goal of FHLO is to provide useful probabilistic forecasts of wind at fixed points in space, but these require large-ensembles (O(1000)) to flesh out the tails of the distributions. FHLO accomplishes this by using a computationally inexpensive framework, which consists of three components: (1) a track model that generates synthetic tracks from the TC tracks of an ensemble numerical weather prediction (NWP) model, (2) a simplified intensity model (FAST - see Emanuel (2017)
) that predicts the intensity along each synthetic track, and (3) a TC wind field model that estimates the time-varying two-dimensional surface wind field. The intensity and wind field of a TC evolve as though the TC were embedded in a time-evolving environmental field, which is derived from the forecast fields of ensemble NWP models. See Lin et. al. (2020)
for more details.