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

Abstract

Abstract. In this study we present a new cyclone identification and tracking algorithm, cycloTRACK. The algorithm describes an iterative process. At each time step it identifies all potential cyclone centers, defined as relative vorticity maxima embedded in smoothed enclosed contours of at least 3 × 10 −5 s −1 at the atmospheric level of 850 hPa. Next, the algorithm finds all the potential cyclone paths by linking the cyclone centers at consecutive time steps and selects the most probable track based on the minimization of a cost function. The cost function is based on the average differences of relative vorticity between consecutive track points, weighted by their distance. Last, for each cyclone, the algorithm identifies "an effective area" for which different physical diagnostics are measured, such as the minimum sea level pressure and the maximum wind speed. The algorithm was applied to the ERA-Interim reanalyses for tracking the Northern Hemisphere extratropical cyclones of winters from 1989 until 2009, and we assessed its sensitivity for the several free parameters used to perform the tracking.

Assessment of an ensemble of ocean–atmosphere coupled and uncoupled regional climate models to reproduce the climatology of Mediterranean cyclones

“…The algorithm finally chooses the track that presents the least average difference of RV between consecutive track points, weighted by the distance between the track points. This tracking method is presented in detail in Flaounas et al (2014).…”

Cyclone contribution to dust transport over the Mediterranean region

“…Cyclones are tracked during the 8‐year period of 2005–2012. For this reason we applied the method developed by Flaounas et al (), where cyclones centers and intensities are defined by the grid point and magnitude of relative vorticity local maxima at 850 hPa. Cyclones have been tracked using the 6‐hourly 850 hPa relative vorticity fields of ERA‐Interim in a standard horizontal grid spacing in longitude and latitude of 0.75° × 0.75° (Dee and Uppala, ).…”

A 4D feature‐tracking algorithm: A multidimensional view of cyclone systems

“…A number of cyclone tracking algorithms have been developed using different features to detect and track cyclones and anticyclones (Neu et al ., and references therein; Grieger et al, and references therein; Hodges, ; ; ). A widely used tracking method, the neighbour point tracking (NPT), consists of a two‐step approach in which first, the cyclonic system centres, defined as a local maximum or minimum value of a climatic variable, are detected at each time step and then the track based on the nearest‐neighbour concept (Kelemen et al, ) is obtained through the temporal connection between cyclone centres (Murray and Simmonds, 1991a,b; Satake et al, ; Flaounas et al, ). Most frequently used climate variables for tracking purposes include mean‐sea‐level pressure (MSLP), MSLP gradients, geopotential height, potential and relative vorticity, singly or in combination (hybrid approach), e.g.…”