GEOgrid_coldcloud : a homogenization of the IR observation of a fleet of geostationary satellites

Infrared (IR) observations from the fleet of multi-agencies meteorological geostationary satellites have a great potential to support scientific and operational investigations at a quasi-global scale. In particular, the observation of Deep Convective Systems over the entire tropical belt can then be performed by applying tracking algorithms on a full space/time resolution thermal infrared measurements obtained from the operational fleet of geostationary satellites (GEOring). Yet, this GEOring dataset is far from being homogeneous preventing the realization of its potential. A number of sources of inhomogeneities is identified ranging from spatio-temporal resolutions, to spectral characteristics of the IR channels and calibration methodologies. While previous efforts have attempted to correct such issues, the adjustment of the cold part of the infrared spectrum remains unfit for cold cloud studies.
To overcome these issues, a homogenization of the IR observation of a fleet of geostationary satellites from 2012 to 2020 has been carried out for a specific analysis of the high cold clouds. The resulting level 1C IR dataset is called GEOgrid_colcloud. This dataset is a consistent 10.5-12.5 μm IR equivalent BT dataset, with a homogeneous re-calibration, and a spectral correction, a 0.04°×0.04° spatial resolution, a 30-minute common time resolution and a correction for limb darkening effect. The dataset covers the 2012-2020 period. The global homogeneity of the IR GEOring dataset regardless of the variation of VZA is then characterized by a standard deviation of 1.43 K within any of two geostationary satellites for brightness temperatures < 245K. The homogenization procedure has been fully described in Fiolleau etal (2020)

- 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