Comparison of SEVIRI-derived cloud occurrence frequency and cloud-top height with a-train data

Chu Yong Chung, Peter N. Francis, Roger W. Saunders, Jhoon Kim

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5 Citations (Scopus)


To investigate the characteristics of Spinning Enhanced Visible and Infrared Imager (SEVIRI)-derived products from the UK Met Office algorithm, one year of cloud occurrence frequency (COF) and cloud-top height (CTH) data from May 2013 to April 2014 was analysed in comparison with Cloud Profiling Radar (CPR) and Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP) cloud products observed from the A-Train constellation. Because CPR operated in daylight-only data collection mode, daytime products were validated in this study. It is important to note that the different sensor characteristics cause differences in CTH retrievals. The CTH of active instruments, CPR and CALIOP, is derived from the return time of the backscattered radar or LiDAR signal, while the infrared sensor, SEVIRI, measures a radiatively effective CTH. Therefore, some systematic differences in comparison results are expected. However, similarities in spatial distribution and seasonal variability of COFs were noted among SEVIRI, CALIOP, and CPR products, although COF derived by the SEVIRI algorithm showed biases of 14.35% and -3.90% compared with those from CPR and CALIOP measurements, respectively. We found that the SEVIRI algorithm estimated larger COF values than the CPR product, especially over oceans, whereas smaller COF was detected by SEVIRI measurements over land and in the tropics than by CALIOP, where multi-layer clouds and thin cirrus clouds are dominant. CTHs derived from SEVIRI showed better agreement with CPR than with CALIOP. Further comparison with CPR showed that SEVIRI CTH was highly sensitive to the CO2 bias correction used in the Minimum Residual method. Compared with CPR CTHs, SEVIRI has produced stable CTHs since the bias correction update in November 2013, with a correlation coefficient of 0.93, bias of -0.27 km, and standard deviation of 1.61 km.

Original languageEnglish
Article number24
JournalRemote Sensing
Issue number1
Publication statusPublished - 2017

Bibliographical note

Funding Information:
This study was performed through collaboration of the UK Met Office and Korea Meteorological Administration (KMA). This research was supported by the National Meteorological Satellite Center (Project No. 153-3100-3137-305-210-13) of KMA and the work of Jhoon Kim was supported by the project titled 'Research for Applications of Geostationary Ocean Color Imager' (Project No. 1525004803), funded by the Ministry of Oceans and Fisheries, Korea. This work made use of the AVAC-S software owned by EUMETSAT and developed by Informus GmbH. Further information on AVAC-S can be obtained at

Publisher Copyright:
© 2016 by the authors; licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)


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