DOI: 10.14768/1be7fd53-8b81-416e-90d5-002b36b30cf8
title: TOOCAN Database V2.08 – Tracking Of Organized Convection Algorithm using a 3-dimensional segmentation

description: TOOCAN is a cloud tracking algorithm, described in Fiolleau and Roca 2013, to detect and track Deep Convective Systems (DCSs) from the geostationary infrared observations. Here, the TOOCAN algorithm has been applied on the homogenized infrared observations from the fleet of multi-agencies meteorological geostationary satellites data (Fiolleau etal 2019) to monitor the entire tropical belt for the 2012-2020 period. Each geostationary platform permits the processing of a specific region (Eastern-Pacific, America, Africa, India, Western-Pacific). About 12×106 DCSs have been identified over the entire tropics for the 9-year period allowing the documentation of their morphological characteristics along their life cycles. A homogeneous level-2 database, called TOOCAN database, has then been built from this processing. It gives an access to the integrated morphological parameters of each DCS (Location and time of initiation and dissipation, lifetime duration, propagated distance, maximum extent…). DCSs are also described by their morphological characteristics at each 30min-step of their life cycles (Cold cloud surface at different level of brightness temperature thresholds, excentricity, instantaneous velocity… ).
The TOOCAN database is composed by two types of files:
• Regional segmented images at a 0.04° spatial resolution and a 30 minute temporal frequency (in NETCDF).
• Regional and monthly tracking files (in ASCII or in NETCDF) documenting the DCS integrated morphological parameters (lifetime duration, distance of propagation … ) and the evolution of the DCS parameters along their life cycles.

The TOOCAN version 2.08 differs from the previous version 2.06 (DOI) in several ways:

- The TOOCAN algorithm identifies convective systems by working in a spatio-temporal volume of infrared images. The first brightness temperature threshold is still set at 190K to detect the convective seeds in the volume of IR images, and the the iterative detection and dilation process is stopped when the 235K threshold is reached, corresponding to the high cold cloud shield boundaries. However, to improve the quality of convective system segmentation, the detection step has been reduced from 5K to 2K in the iterative detection and dilation process.
- New classifications of convective systems have been established, according to the definition of MCC, PECS, MβCCS, and MβECS defined in Jirak etal (2003) and Maddox (1981).
- Also, taking advantage of the IBTrACS database, convective systems located within a radius of 1000km from a cyclone have been flagged.
- The monthly tracking files have been saved in a NETCDF format in addition to the traditional ASCII format. 
- The TOOCAN segmented images in a NETCDF format have been supplemented with the information on the scanning time of the geostationary satellites.

-Jirak, I. L., W. R. Cotton, and R. L. McAnelly, 2003: Satellite and Radar Survey of Mesoscale Convective System Development. Mon. Wea. Rev., 131, 2428–2449, https://doi.org/10.1175/1520-0493(2003)131&lt;2428:SARSOM&gt;2.0.CO;2
-Knapp, K. R., and Coauthors, 2011: Globally Gridded Satellite observations for climate studies. Bull Am Meteorol Soc, 92, 893–907, https://doi.org/10.1175/2011BAMS3039.1.
-Maddox, R. A., 1980: Mesoscale Convective Complexes. 1374–1387 pp.

References: 
-Fiolleau, T. and R. Roca, 2013: An Algorithm for the Detection and Tracking of Tropical Mesoscale Convective Systems Using Infrared Images From Geostationary Satellite, IEEE Trans. Geosci. Remote Sens., vol. 51, no. 7, pp. 4302–4315. doi: 10.1109/TGRS.2012.2227762
-Fiolleau, T., R. Roca, S. Cloché, D. Bouniol, P. Raberanto, 2020: Homogenization of geostationary infrared imager channels for cold cloud studies using Megha-Tropiques/ScaRaB. IEEE Trans. Geosci. Remote Sens., vol 58, no. 9, pp. 6609-6622. doi: 10.1109/TGRS.2020.2978171