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

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.

[1]  Douglas W. Nychka,et al.  Tracking of multiple merging and splitting targets: A statistical perspective , 2009 .

[2]  Da‐Lin Zhang,et al.  Genesis of Tropical Storm Eugene (2005) from Merging Vortices Associated with ITCZ Breakdowns. Part I: Observational and Modeling Analyses , 2008 .

[3]  C. Snyder,et al.  A Vortex-Based Perspective of Eastern Pacific Tropical Cyclone Formation , 2008 .

[4]  G. Magnusdottir,et al.  Intertropical Convergence Zones during the Active Season in Daily Data , 2008 .

[5]  T. Stocker,et al.  Tropical cyclones in ERA‐40: A detection and tracking method , 2008 .

[6]  Songhwai Oh,et al.  Bayesian Formulation of Data Association and Markov Chain Monte Carlo Data Association , 2008 .

[7]  Padhraic Smyth,et al.  Graphical models for statistical inference and data assimilation , 2007, Physica D: Nonlinear Phenomena.

[8]  M. Fiorino,et al.  Objectively Determined Resolution-Dependent Threshold Criteria for the Detection of Tropical Cyclones in Climate Models and Reanalyses , 2007 .

[9]  M. Esch,et al.  Tropical cyclones in a T159 resolution global climate model: comparison with observations and re-analyses , 2007 .

[10]  Jouko Lampinen,et al.  Rao-Blackwellized particle filter for multiple target tracking , 2007, Inf. Fusion.

[11]  E. Kj,et al.  Objectively Determined Resolution-Dependent Threshold Criteria for the Detection of Tropical Cyclones in Climate Models and Reanalyses , 2007 .

[12]  G. Magnusdottir,et al.  The ITCZ in the central and eastern pacific on synoptic time scales , 2006 .

[13]  F. Chauvin,et al.  Response of hurricane-type vortices to global warming as simulated by ARPEGE-Climat at high resolution , 2006 .

[14]  S. Godsill,et al.  Monte Carlo filtering for multi target tracking and data association , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[15]  D. Jacob,et al.  Tracking cyclones in regional model data: the future of Mediterranean storms , 2005 .

[16]  Jouko Lampinen,et al.  Rao-Blackwellized Monte Carlo Data Association for Multiple Target Tracking , 2004 .

[17]  Jouko Lampinen,et al.  Probabilistic methods in multiple target tracking - Review and bibliography , 2004 .

[18]  Songhwai Oh,et al.  Markov chain Monte Carlo data association for general multiple-target tracking problems , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[19]  Tim Hesterberg,et al.  Monte Carlo Strategies in Scientific Computing , 2002, Technometrics.

[20]  Linda G. Shapiro,et al.  Computer and Robot Vision (Volume II) , 2002 .

[21]  James L. Franklin,et al.  Eastern North Pacific Hurricane Season of 2007 , 2001 .

[22]  Gregory D. Hager,et al.  Probabilistic Data Association Methods for Tracking Complex Visual Objects , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Fredrik Gustafsson,et al.  Monte Carlo data association for multiple target tracking , 2001 .

[24]  Arnaud Doucet,et al.  Markov chain Monte Carlo data association for target tracking , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[25]  Kevin I. Hodges,et al.  Adaptive Constraints for Feature Tracking , 1999 .

[26]  Andrew Gelman,et al.  General methods for monitoring convergence of iterative simulations , 1998 .

[27]  Michael I. Jordan.  Graphical Models , 1998 .

[28]  C. Jauffret,et al.  A formulation of multitarget tracking as an incomplete data problem , 1997, IEEE Transactions on Aerospace and Electronic Systems.

[29]  Michael A. West,et al.  Bayesian Forecasting and Dynamic Models (2nd edn) , 1997, J. Oper. Res. Soc..

[30]  Henry Leung,et al.  Statistical performance analysis of track initiation techniques , 1997, IEEE Trans. Signal Process..

[31]  Ingemar J. Cox,et al.  An Efficient Implementation of Reid's Multiple Hypothesis Tracking Algorithm and Its Evaluation for the Purpose of Visual Tracking , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Peter Green,et al.  Markov chain Monte Carlo in Practice , 1996 .

[33]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[34]  Kevin I. Hodges,et al.  A General Method for Tracking Analysis and Its Application to Meteorological Data , 1994 .

[35]  Ingemar J. Cox,et al.  An efficient implementation and evaluation of Reid's multiple hypothesis tracking algorithm for visual tracking , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[36]  Roy L. Streit,et al.  Maximum likelihood method for probabilistic multihypothesis tracking , 1994, Defense, Security, and Sensing.

[37]  J. R. Rohlicek,et al.  ML estimation of a stochastic linear system with the EM algorithm and its application to speech recognition , 1993, IEEE Trans. Speech Audio Process..

[38]  D. Rubin,et al.  Inference from Iterative Simulation Using Multiple Sequences , 1992 .

[39]  Chee-Yee Chong,et al.  Performance evaluation of track initiation in dense target environments , 1992, [Proceedings 1992] IEEE International Conference on Systems Engineering.

[40]  Linda G. Shapiro,et al.  Computer and Robot Vision , 1991 .

[41]  Lixion A. Avila.  Eastern North Pacific Hurricane Season of 1990 , 1991 .

[42]  J. C. McMillan,et al.  Data Association Algorithms for Multiple Target Tracking , 1990 .

[43]  M. West,et al.  Bayesian forecasting and dynamic models , 1989 .

[44]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[45]  Yaakov Bar-Shalom,et al.  Adaptive Detection Threshold Optimization for Tracking in Clutter , 1984, 1984 American Control Conference.

[46]  D. Reid.  An algorithm for tracking multiple targets , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.

[47]  A.H. Haddad,et al.  Applied optimal estimation , 1976, Proceedings of the IEEE.

[48]  Robert E. Tarjan,et al.  Efficiency of a Good But Not Linear Set Union Algorithm , 1972, JACM.

[49]  W. M. Gray,et al.  GLOBAL VIEW OF THE ORIGIN OF TROPICAL DISTURBANCES AND STORMS , 1968 .

[50]  C. Striebel,et al.  On the maximum likelihood estimates for linear dynamic systems , 1965 .