Objective identification, typing and tracking of the complete life-cycles of cyclonic features at high spatial resolution

Abstract

Synoptic-scale cyclonic features provide an inescapable focal point for operational forecasting, whilst the merits of tracking such features are increasingly being recognized in the climate change field. Close association with adverse and extreme weather is the main motivator. Here a new and highly sophisticated set of techniques to detect, classify and track the full range is developed. A revised conceptual model of cyclone development provided the initial framework, ensuring a solid bond with forecasting practice, whilst also connecting closely to baroclinic life-cycle concepts. Building on this, cyclones are detected using a hybrid of geopotential minimum/vorticity maximum techniques, whilst incorporating important extensions to ensure that vorticity can be used at high resolution (∼50 km) and that features on fronts take priority. To track the features across time, at intervals of 12 h or less, feature attributes in the association process are used. Additionally, an upper-tropospheric steering wind is employed to estimate future and past positions. This facilitates 'half-time tracking', a new approach that has clear-cut advantages over 'full-time tracking' employed elsewhere.In detection tests, comparing with subjectively-drawn charts, the feature hit rate was 84%, and the false alarm ratio 17%, whilst in a simple tracking test the association failure rate was just 2%. These values compare very favourably with previous studies.One key application is discussed. This involves processing ensemble output to provide wide-ranging real-time products tailor-made to forecasters' needs. Products include track-following plume diagrams, for various cyclone attributes, and storm-track strike probability plots for different thresholds of severity.

Cyclones, windstorms and the IMILAST project

“…Panel (a) shows the cyclone track (black), with spots denoting positions equally separated in time, and numbered according to the cyclone life-cycle phases in panel (b). Spot colour relates to the identification method and objective typing used in Hewson and Titley (2010), green being a diminutive frontal wave, orange a frontal wave cyclone, and black a barotropic low. Shading denotes the footprints, or nominal damage swathes, attributable to the warm jet/ warm conveyor (yellow), the cold jet/cold conveyor (orange) and the sting jet (red).…”

Winter westerly disturbance dynamics and precipitation in the western Himalaya and Karakoram: a wave-tracking approach

“…Over recent decades, many diagnostic methods of objectively identifying extratropical cyclones have been developed (Murray and Simmonds 1991;Hodges 1995;Serreze 1995;Blender et al 1997;Sinclair 1994;Simmonds et al 1999;Lionello et al 2002;Benestad and Chen 2006;Trigo 2006;Wernli and Schwierz 2006;Akperov et al 2007;Rudeva and Gulev 2007;Inatsu 2009;Kew et al 2010;Hewson and Titley 2010;Hanley and Caballero 2012). Depending on the definition of a cyclone, these automated algorithms utilize differing variables and tracking techniques (Hoskins and Hodges 2002).…”

CycloTRACK (v1.0) – tracking winter extratropical cyclones based on relative vorticity: sensitivity to data filtering and other relevant parameters

“…Hodges (1995) built the feature tracks by minimizing the cost function of the feature's track smoothness, while Hewson and Titley (2010) built the feature tracks by applying a likelihood score to its physical characteristics. Our cost function measures the absolute average difference of the relative vorticity weighted by the distance between two consecutive time steps:…”

“…In order to avoid the detection of a frontal zone, additional criteria of high or low complexity should be considered (e.g., Hewson and Titley, 2010). However, such criteria could be dependent on several factors -such as the spatial resolution of the data set -and would result in a "stricter" cyclone definition.…”

“…Examples of recent works include the mesoscale convective systems (MCSs; e.g., Machado et al, 1998), the conveyor belts (e.g., Eckhardt et al, 2004), the cut-off lows (Wernli and Sprenger, 2007), the fronts (Hewson and Titley, 2010), the jet streams (Limbach et al, 2012) and the dry air intrusions (Roca et al, 2005;Flaounas et al, 2012). However, the most investigated atmospheric features targeted by identification and tracking algorithms are the tropical and extratropical cyclones (e.g., Hodges, 1999;Blender and Schubert, 2000;Hoskins and Hodges 2002;Ulbrich et al, 2009;Inatsu, 2009).…”