CELLTRACK — Convective cell tracking algorithm and its use for deriving life cycle characteristics

Kyznarová, Hana; Novák, Petr

doi:10.1016/j.atmosres.2008.09.019

Cited by 44 publications

(40 citation statements)

References 8 publications

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“…[4] Various objective tracking methods have been developed and advanced during the last 2 decades, mainly for the purpose of nowcasting strong convective events like thunderstorms [Rosenfeld, 1987;Dixon and Wiener, 1993;Wilson et al, 1998;Johnson et al, 1998;Handwerker, 2002;Hering et al, 2005;Novo et al, 2013] but also the mean life cycles of the cell size and statistics of tracked rain cells have been studied [Kysnarova and Novak, 2009;Peleg and Morin, 2012;Rigo et al, 2012]. Other tracking methods have been applied not only to radar data but also to other high-resolution observational data sets such as satellite data [Lakshmanan et al, 2009;Feng et al, 2012].…”

Section: Introductionmentioning

confidence: 99%

Probing the precipitation life cycle by iterative rain cell tracking

Moseley

Berg

Haerter³

2013

JGR Atmospheres

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[1] Monitoring the life cycle of convective rain cells requires a Lagrangian viewpoint where the observer moves with the dominant background flow. To adopt such a moving reference frame, we design, validate, and apply a simple rain cell tracking method-which we term iterative rain cell tracking (IRT)-for spatio-temporal precipitation data. IRT iteratively identifies the formation and dissipation of rain cells and determines the large-scale flow. The iteration is repeated until reaching convergence. As validated using reanalysis wind speeds, repeated iterations lead to substantially increased agreement of the background flow field and an increased number of complete tracks. Our method is thereby able to monitor the growth and intensity profiles of rain cells and is applied to a high-resolution (5 min and 1 1 km 2 ) data set of radar-derived rainfall intensities over Germany. We then combine this data set with surface temperature observations and synoptic observations to group tracks according to convective and stratiform conditions. Convective tracks show clear life cycles in intensity, with peaks shifted off-center toward the beginning of the track, whereas stratiform tracks have comparatively featureless intensity profiles. Our results show that the convective life cycle can lead to convection-dominating precipitation extremes at short time scales, while track-mean intensities may vary much less between the two types. The observed features become more pronounced as surface temperature increases, and in the case of convection even exceeded the rates expected from the Clausius-Clapeyron relation.Citation: Moseley, C., P. Berg, and J. O. Haerter (2013), Probing the precipitation life cycle by iterative rain cell tracking,

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Section: Introductionmentioning

confidence: 99%

Probing the precipitation life cycle by iterative rain cell tracking

Moseley

Berg

Haerter³

2013

JGR Atmospheres

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“…-Celltrack ext (history). This method is the same as Celltrack mentioned above but the extrapolation forecast is made using movement vectors that are derived by identification of the cell movement from 1 hour history with the weight decreasing for older movement vectors [5]. -INCA cz.…”

Section: The Methodsmentioning

confidence: 99%

“…Extrapolation forecast is made using movement vectors defining cell shift between the last two radar measurements. [5]. -Celltrack ext (history).…”

Section: The Methodsmentioning

confidence: 99%

New Methods of Flash Flood Forecasting in the Czech Republic

Březková

Šálek

Novák

et al. 2011

IFIP Advances in Information and Communication Technology

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“…However, reflectivity within the bright band or around ground clutter often exceeds 30 dBZ (Handwerker 2002;Kyznarova and Novak 2009), which would impede the performance of cell identification and tracking.…”

Section: Input Data For Aitccmentioning

confidence: 99%

“…Based on radar reflectivity, automated algorithms have been developed in some nowcasting systems for the purpose of cell identification and tracking (TITAN: Dixon and Wiener 1993;SCIT: Johnson et al 1998;TRACE3D: Handwerker 2002; TRT: Hering et al 2004; CELLTRACK: Kyznarova and Novak 2009). Most such identification algorithms define a CC as a contiguous region around the local maximum in the reflectivity field for a given height.…”

mentioning

confidence: 99%