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

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.

The advanced algorithm for tracking objects (AALTO)

“…AALTO was developed to incorporate practices from radar-based tracking algorithms such as TITAN (Dixon and Wiener, 1993), SCIT , the NSSL MDA , w2segmotion (Lakshmanan et al, 2007(Lakshmanan et al, , 2009, ThOR (Barjenbruch and Houston, 2006;Barjenbruch, 2008;Lahowetz et al, 2010), E-TITAN (Han Scharenbrouch et al, 2010;Root et al, 2011;Lakshmanan et al, 2013;Miller et al, 2013) and satellite-based tracking algorithms (Zahraei et al, 2012;Sieglaff et al, 2013). Each of these algorithms adopts different approaches to forecasting object motion, searching for new objects and identifying the best object to use in continuing a track.…”

“…This approach that considers future positions of the object in addition to the past has been adopted in some automated tracking algorithms including MHT (e.g. Reid, 1979;Cox and Hingorani, 1996;Blackman, 2004;Lakshmanan and Smith, 2010;Scharenbrouch et al, 2010;Root et al, 2011;Lakshmanan et al, 2013;Miller et al, 2013) and ThOR (Barjenbruch and Houston, 2006;Barjenbruch, 2008;Lahowetz et al, 2010) and is employed in AALTO. Future positions are examined by building a search tree in which each node represents the position of an object along a candidate track, which refers to a possible continuation of the existing track.…”

Detecting the ITCZ in Instantaneous Satellite Data using Spatiotemporal Statistical Modeling: ITCZ Climatology in the East Pacific

“…Weak WPDs (or easterly waves) have been observed to propagate through the ITCZ cloud envelope (e.g., Scharenbroich et al 2010) and, in general, it is impractical to attempt to separate the easterly wave signal out of the ITCZ signal. Magnusdottir and Wang (2008) reached the same conclusion when using spectral analysis on 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) 850-hPa relative vorticity.…”

Variability of Intertropical Convergence Zone (ITCZ) and Extreme Weather Events

“…Weak WPDs (or easterly waves) have been observed to propagate through the ITCZ cloud envelope (e.g., Scharenbroich et al, 2010) and, in general, it is impractical to attempt to separate the easterly wave signal out of the ITCZ signal. (Magnusdottir & Wang, 2008) reached the same conclusion when using spectral analysis on 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) 850-hPa relative vorticity.…”