Mixture-Based Path Clustering for Synthesis of ECMWF Ensemble Forecasts of Tropical Cyclone Evolution

Abstract

In this article, three tropical cyclones and their 120-h, 50-member ECMWF Integrated Forecasting System (IFS) ensemble track forecasts at 10 initialization times are considered. The IFS forecast tracks are clustered with a regression mixture model, and two traditional diagnostics (the Bayesian information criterion and a measure of strength of cluster assignment) are used to determine the optimal polynomial order and number of clusters to use in the model. In addition, cross-validation versions of the two diagnostics are formulated and computed to further aid in model selection. Both traditional and cross-validation diagnostics suggest that third-order polynomials and five clusters are effective options—although the evidence is less conclusive for the number of clusters than for the polynomial order, and the cross-validation diagnostics favor a smaller number of clusters than the traditional ones. Path clustering of IFS tropical cyclone track forecasts with this third-order polynomial, five-cluster regression mixture model produces interpretable partitions by direction and speed of motion for each of the storms and initialization times considered. Thus, this approach effectively synthesizes the forecast spreads within the IFS into a small number of representative trajectories. Based on how forecasts distribute across clusters, this approach also provides information on the likelihood of each such representative trajectory. If used operationally, this information has the potential to aid forecasters in parsing and quantifying the uncertainty in tropical cyclone track forecasts.

Use of Multiensemble Track Clustering to Inform Medium-Range Tropical Cyclone Forecasts

“…Although optimal cluster characteristics vary forecast-to-forecast, results across all forecasts are examined to select a single combination of polynomial order and number of clusters to evaluate. For mixturemodel clustering of ensemble TC tracks, Kuruppumullage Don et al (2016) and Kowaleski and Evans (2016) found that selecting the optimal polynomial order is more straightforward than selecting the optimal number of clusters; therefore, we evaluate polynomial order first.…”

A Statistical Assessment of Southern Hemisphere Tropical Cyclone Tracks in Climate Models

“…The total TC count for each cluster is indicated next to the cluster number. applications of this cluster analysis include a comparison of tracks of TCs in a reanalysis dataset to observations (Bell et al 2018), analysis of the skill of tropical cyclone forecasts (Don et al 2016, Kowaleski and, and in the development of statistical-dynamical seasonal forecasts (Zhang et al 2016). The application we focus on here is to examine the ability of climate models to simulate observed track types and if/how they change under anthropogenic climate change.…”

Relationship between the Track and Structural Evolution of Hurricane Sandy (2012) Using a Regional Ensemble

“…Kuruppumullage Don et al (2016) and Kowaleski and Evans (2016), using the method developed by Gaffney et al (2007). We focus on results from track clustering throughout the paper; CPS path clustering is performed to help select the optimal track cluster partition.…”

“…Regression mixture-model clustering requires the selection of cluster shape, polynomial order, and number of clusters prior to clustering. All clusters are prescribed to have a circular error covariance matrix as in Kuruppumullage Don et al (2016) TABLE 1. Model configuration used in WRF, version 3.8, simulations of Hurricane Sandy.…”

“…Kuruppumullage Don et al (2016) and Kowaleski and Evans (2016) found that when clustering TC tracks or CPS paths, selecting the optimal polynomial order is more straightforward than selecting the optimal number of clusters. Bayesian information criterion (BIC) and mean squared displacement (MSD) are used to select the optimal polynomial orders for track and CPS cluster partitions.…”