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An Objective Algorithm for Detecting and Tracking Tropical Cloud Clusters: Implications for Tropical Cyclogenesis Prediction

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  • 1 University of North Carolina at Asheville, Asheville, North Carolina
  • | 2 The Florida State University, Tallahassee, Florida
  • | 3 NOAA/National Climatic Data Center, Asheville, North Carolina
  • | 4 NOAA/National Weather Service, Melbourne, Florida
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Abstract

An algorithm to detect and track global tropical cloud clusters (TCCs) is presented. TCCs are organized large areas of convection that form over warm tropical waters. TCCs are important because they are the “seedlings” that can evolve into tropical cyclones. A TCC satisfies the necessary condition of a “preexisting disturbance,” which provides the required latent heat release to drive the development of tropical cyclone circulations. The operational prediction of tropical cyclogenesis is poor because of weaknesses in the observational network and numerical models; thus, past studies have focused on identifying differences between “developing” (evolving into a tropical cyclone) and “nondeveloping” (failing to do so) TCCs in the global analysis fields to produce statistical forecasts of these events.

The algorithm presented here has been used to create a global dataset of all TCCs that formed from 1980 to 2008. Capitalizing on a global, Gridded Satellite (GridSat) infrared (IR) dataset, areas of persistent, intense convection are identified by analyzing characteristics of the IR brightness temperature (Tb) fields. Identified TCCs are tracked as they move around their ocean basin (or cross into others); variables such as TCC size, location, convective intensity, cloud-top height, development status (i.e., developing or nondeveloping), and a movement vector are recorded in Network Common Data Form (NetCDF). The algorithm can be adapted to near-real-time tracking of TCCs, which could be of great benefit to the tropical cyclone forecast community.

Corresponding author address: Christopher C. Hennon, CPO #2450, 1 University Heights, University of North Carolina at Asheville, Asheville, NC 28804. E-mail: chennon@unca.edu

Abstract

An algorithm to detect and track global tropical cloud clusters (TCCs) is presented. TCCs are organized large areas of convection that form over warm tropical waters. TCCs are important because they are the “seedlings” that can evolve into tropical cyclones. A TCC satisfies the necessary condition of a “preexisting disturbance,” which provides the required latent heat release to drive the development of tropical cyclone circulations. The operational prediction of tropical cyclogenesis is poor because of weaknesses in the observational network and numerical models; thus, past studies have focused on identifying differences between “developing” (evolving into a tropical cyclone) and “nondeveloping” (failing to do so) TCCs in the global analysis fields to produce statistical forecasts of these events.

The algorithm presented here has been used to create a global dataset of all TCCs that formed from 1980 to 2008. Capitalizing on a global, Gridded Satellite (GridSat) infrared (IR) dataset, areas of persistent, intense convection are identified by analyzing characteristics of the IR brightness temperature (Tb) fields. Identified TCCs are tracked as they move around their ocean basin (or cross into others); variables such as TCC size, location, convective intensity, cloud-top height, development status (i.e., developing or nondeveloping), and a movement vector are recorded in Network Common Data Form (NetCDF). The algorithm can be adapted to near-real-time tracking of TCCs, which could be of great benefit to the tropical cyclone forecast community.

Corresponding author address: Christopher C. Hennon, CPO #2450, 1 University Heights, University of North Carolina at Asheville, Asheville, NC 28804. E-mail: chennon@unca.edu
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