Deep Convective Tracking Database

Overview

ISCCP has produced a global dataset (currently covering the period 1983-2004) describing the properties of clouds (Schiffer and Rossow 1983, Rossow and Schiffer 1999). This dataset can be used more effectively for the study of specific cloud processes or other related meteorological phenomena if the information about particular types of clouds can be separated from these general, global statistics. To support studies related to 'deep' convection, the primary process by which the tropical atmosphere is heated by precipitation and radiation, the ISCCP dataset has been analyzed to identify and describe the properties of mesoscale deep convective cloud systems.

The first step in the analysis (Machado and Rossow 1993) is to identify all spatially adjacent clusters of satellite image pixels in the ISCCP DX dataset, (Rossow et al. 1996) that contain clouds in the upper troposphere, determined by cloud top temperatures < 245 K. Such clusters are referred to as Convective Systems (CS), whether or not they actually contain convective clouds. A further test identifies Convective Clusters (CC) as adjacent cloudy pixels with cloud top temperatures < 220 K. The location, date and time (at nominal 3-hr intervals GMT) of each cluster is collected together with a statistical summary of the CS and CC cloud properties, based on the ISCCP determinations of cloud top temperature and optical thickness. In addition, several properties describing the shape and size of the CS are calculated.

The second step of the analysis, which produces the Convection Tracking (CT) Database, is to 'track' each CS over time to form time-associated 'families' (Machado et al. 1998). Since the satellite images used for the ISCCP analysis are separated by 3-hr time intervals and spatially sampled at intervals of about 30 km, the evolution of smaller CS is not followed, so the CT Database only contains families formed from CS that are at least 90 km in radius (about 30 pixels in the ISCCP DX dataset). However, the full CS dataset does contain information about the smaller systems.

The CT Database contains the morphological and cloud property information for each member of all the families of CS identified by the analysis, even if CC never occurs. The CT Database currently covers the period July 1983 to November 2004.

References

Machado, L.A.T., and W.B. Rossow, 1993: Structural characteristics and radiative properties of tropical cloud clusters. Mon. Wea. Rev, 121, 3234-3260.

Machado, L.A.T., W.B. Rossow, R.L. Guedes, and A.W. Walker, 1998: Life cycle variations of mesoscale convective systems over the Americas. Mon. Wea. Rev., 126, 1630-1654.

Rossow, W.B., A.W. Walker, D. Beuschel and M. Roiter, 1996: International Satellite Cloud Climatology Project (ISCCP) Description of New Cloud Datasets. WMO/TD - No. 737, World Climate Research Programme (ICSU and WMO), Geneva, February 1996, pp. 115.

Rossow, W.B., and R.A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc., 80, 2261-2287.

Schiffer, R.A., and W.B. Rossow, 1983: The International Satellite Cloud Climatology Project (ISCCP): The first project of the World Climate Research Programme. Bull. Amer. Meteor. Soc., 64, 779-784.

Schiffer, R.A., and W.B. Rossow, 1985: ISCCP global radiance data set: A new resource for climate research. Bull. Amer. Meteor. Soc., 66, 1498-1505.

ACCESSING THE DATABASE

To download data, read software and sample output click here

The data is available in yearly tar'd files:

CTYYYY.tar.gz
src.tar.gz

File src.tar.gz is a zipped file containing a README , software and sample output. The read software searches all geostationary satellite positions for an input time and latitude-longitude. A match is declared if the specified latitude-longitude are within the CS radius (or the specified search window) for the specified date and time. The time range is implicitly (+/-) 1.5 hours. For every match found, the program returns a header followed by the complete information for the entire convective tracking family containing the matched CS. Each family is uniquely identified for each month by a 3 letter geographical satellite position code and a family number.

To illustrate the use of the CT software and database to augment the analysis of TRMM observations, the sample output corresponds to examples below which show a match - up to TRMM data. The samples were chosen by searching for families at times and locations where there were known occurrences of TRMM TMI 85.5 GHZ data. The sample output is labelled with the location name.

EXAMPLES

PHILIPPINES

CONGO

BAY OF BENGAL