An improved method to estimate reference cloud-free images for the visible band of geostationary satellites

ABSTRACT

Geostationary images have been used frequently in the past 50 years to derive geophysical information. As a complement to all-sky observations, clear-sky counterparts play an important role in the derivation of cloud properties. We investigated ways to improve estimates of top-of-atmosphere (TOA) visible clear-sky images, over the full spatial and temporal resolution of Meteosat First Generation (MFG) satellites. Estimation was based on TOA measurements in MFG’s visible channel, collected for a specific time of the day over the span of several days. In addition, a cloud climatology aided estimation.

Parameter optimization and the introduction of a spatial filter over ocean resulted in a bias of −1.0 to −2.0 digital counts (DC) and a root mean square error (RMSE) of 2.0–3.0 DC when averaged over the complete field of view. This excludes the Spring period which has up to −3.5 DC bias and up to 5.5 DC RMSE. Reasons for these exceptional differences were found in rapid greenness change, affecting reflectances over vegetated surfaces, and dust storms, with an effect over tropical land and ocean surfaces. Similarly, sea ice and snow affected polar regions seasonally. Applied to 24 years of MFG imagery, we successfully used improved clear-sky estimates to stably detect clouds. Additionally, these clear-sky estimates may prove useful for characterization of instrument degradation as well as cloud feedback studies.

Acknowledgements

We would like to thank Brandenburg University of Technology Cottbus-Senftenberg, Germany, for financial support through the Leonardo da Vinci mobility programme (funded by the Federal Institute for Vocational Education and Training under contract [DE/13/LLP-LdV/PLM/285168]), as RMIB in Brussels, Belgium, for funding, work space, and computing time.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Brandenburg University of Technology Cottbus-Senftenberg (Leonardo da Vinci mobility programme, funded by the Federal Institute for Vocational Education and Training under contract [DE/13/LLP-LdV/PLM/285168]) and RMIB.