Abstract
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.
Highlights
Current uncertainties in the roles played by clouds and aerosols in the Earth’s radiation budget limit our understanding of the climate system and the potential for global climate change [1], as well as weather analysis and forecasting
It should be noted that the numbers of co-located data in January and February 2014 were relatively small owing to the outage of Meteosat-10 for scheduled decontamination on 14–21 January 2014, in addition to a data collection failure for this study during that period
This study investigated the characteristics of the UK Met Office Spinning Enhanced Visible and Infrared Imager (SEVIRI) products of cloud occurrence frequency (COF) and cloud-top height (CTH) conducting an inter-comparison of the cloud products from the Cloud Profiling Radar (CPR) and the Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP) on board the A-Train constellation
Summary
Current uncertainties in the roles played by clouds and aerosols in the Earth’s radiation budget limit our understanding of the climate system and the potential for global climate change [1], as well as weather analysis and forecasting. Clouds are highly variable in space and time, and they influence both solar and thermal radiative transfer in addition to the water cycle. Low-level clouds usually have a negative net radiative forcing because their thermal effect is small, the reflection of the solar radiance dominates. The net radiative effect of high-level clouds is often positive because the thermal contrast between them and the surface is large [2,3]. Detailed monitoring of cloud properties is required to understand the distribution and impact of clouds on Remote Sens. Satellites can provide information on the global distribution of cloud cover and cloud properties and variations. Measurements from passive satellite imagers at visible to thermal infrared wavelengths on board many geostationary and polar orbiting satellites provide a wealth of information on cloud properties.
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