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Chia-chi Wang
and
Gudrun Magnusdottir

Abstract

The ITCZ in the central and eastern Pacific on synoptic time scales is highly dynamic. The active season extends roughly from May through October. During the active season, the ITCZ continuously breaks down and re-forms, and produces a series of tropical disturbances. The life span of the ITCZ varies from several days to 3 weeks.

Sixty-five cases of ITCZ breakdown have been visually identified over five active seasons (1999–2003) in three independent datasets. ITCZ breakdown can be triggered by two mechanisms: 1) interaction with westward-propagating disturbances (WPDs) and 2) the vortex rollup (VR) mechanism. Results show that the frequency of occurrence of ITCZ breakdown from these two mechanisms is the same. The VR mechanism may have been neglected because the produced disturbances are rather weak and they may dissipate quickly. The ITCZ shows a strong tendency to re-form within 1–2 days in the same location. The ITCZ may break down via the VR mechanism without any other support, and thus it may continuously generate numerous tropical disturbances throughout the season.

There are two main differences between the two mechanisms: 1) The WPDs-induced ITCZ breakdown tends to create one or two vortices that may be of tropical depression strength. The VR-induced ITCZ breakdown generates several nearly equal-sized weak disturbances. 2) The WPDs tend to disturb the ITCZ in the eastern Pacific only. Disturbances generally move along the Mexican coast after shedding off from the ITCZ and do not further disturb the ITCZ in the central Pacific. Therefore, the VR mechanism is observed more clearly and is the dominating mechanism for ITCZ breakdown in the central Pacific.

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Lucas Scharenbroich
,
Gudrun Magnusdottir
,
Padhraic Smyth
,
Hal Stern
, and
Chia-chi Wang

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.

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