A 39-year Convective Systems database using the TOOCAN cloud tracking algorithm and METEOSAT thermal infrared archive

Mesoscale convective systems (MCs) are central to the water and energy cycle of the tropical region. Geostationary satellite observations can provide a useful resource to constraint theoretical and modelling perspectives of the convective systems. Thus, the MCS life cycle information can only be readily obtained using high frequency imagery available from the geostationary orbit. The METEOSAT series of satellites operated by EUMETSAT observe continuously the African and Atlantic region since more than 40 years and offer us the opportunity to improve our understanding of the MCS and to analyze their climatological trends over the region.

We will introduce a MCS database over the African and Atlantic regions built from the long-term thermal infrared METEOSAT first and second-generation archive and from a cloud tracking algorithm called TOOCAN spanning the 1981-2020 period.

The METEOSAT first and second-generation imagers exhibit some spectral window channels disparities, different temporal resolutions, and slight variability in the spatial resolution of the sensors. Moreover, the imagers of the early METEOSAT satellites were designed for qualitative analyses of weather patterns, and the quality of their data do not comply with climate requirements. Finally, the calibration procedure of each instrument is also performed at the individual level with instruments specifics mode of operation. The cloud tracking can be impacted by these various sources of inhomogeneity, and some technical specifications are then required to ensure the validity of the cloud-tracking and to build a 39-year homogenous MCS dataset.

First, by using the multi-sensor infrared channel calibration (MSICC) algorithm relied on the IASI, AIRS and HIRS/2 as reference observations, an intercalibration and spectral band adjustment of the IR long-term database has been performed to reduce the METEOSAT sensors differences. The spatial resolution has been homogenized by remapping each METEOSAT native projection to a 0.04° longitude-latitude equal-angle grid. A final effort has been performed to correct the limb darkening effect, and a careful quality control has been applied on each infrared image. The TOOCAN cloud tracking algorithm has then been applied to this homogenous long-term METEOSAT infrared dataset at a 30-min temporal frequency to build a 39-year tropical convective systems database giving an access to the morphological parameters of around 14×10⁶ MCS along their life cycles.

Finally, we will present our preliminary analyses showing significant trends in MCS occurrence for different geographical regions.