A Bayesian Framework for Storm Tracking Using a Hidden-State Representation

Abstract

Abstract A probabilistic tracking model is introduced that identifies storm tracks from feature vectors that are extracted from meteorological analysis data. The model assumes that the genesis and lysis times of each track are unknown and estimates their values along with the track’s position and storm intensity over time. A hidden-state dynamics model (Kalman filter) characterizes the temporal evolution of the storms. The model uses a Bayesian methodology for estimating the unknown lifetimes (genesis–lysis pairs) and tracks of the storms. Prior distributions are placed over the unknown parameters and their posterior distributions are estimated using a Markov Chain Monte Carlo (MCMC) sampling algorithm. The posterior distributions are used to identify and report the most likely storm tracks in the data. This approach provides a unified probabilistic framework that accounts for uncertainty in storm timing (genesis and lysis), storm location and intensity, and the feature detection process. Thus, issues such as missing observations can be accommodated in a statistical manner without human intervention. The model is applied to the field of relative vorticity at the 975-hPa level of analysis from the National Centers for Environmental Prediction Global Forecast System during May–October 2000–02, in the tropical east Pacific. Storm tracks in the National Hurricane Center best-track data (HURDAT) for the same period are used to assess the performance of the storm identification and tracking model.