Abstract
Abstract. A novel approach for the detection of cirrus clouds and the retrieval of optical thickness and top altitude based on the measurements of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the geostationary Meteosat Second Generation (MSG) satellite is presented. Trained with 8 000 000 co-incident measurements of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission the new "cirrus optical properties derived from CALIOP and SEVIRI algorithm during day and night" (COCS) algorithm utilizes a backpropagation neural network to provide accurate measurements of cirrus optical depth τ at λ = 532 nm and top altitude z every 15 min covering almost one-third of the Earth's atmosphere. The retrieved values are validated with independent measurements of CALIOP and the optical thickness derived by an airborne high spectral resolution lidar.
Highlights
High ice clouds hold an exceptional position within the large variety of clouds since they most probably generate positive net radiative forcing and contribute to warming the Earth’s atmosphere (Chen et al, 2000)
A new approach was followed to combine the advantages of polar-orbiting active and geostationary passive remote sensing: the cirrus optical properties derived from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) day and night (COCS) algorithm based on an artificial neural network, which retrieves cirrus optical thickness and cloud top altitude from the thermal infrared channels of SEVIRI allowing day and night observations
The training data set of COCS consists of three data sets: the cirrus optical thickness τ and the cirrus top height z derived from CALIOP, seven different infrared brightness temperatures and brightness temperature differences measured by SEVIRI, and so-called auxiliary data, such as latitude, viewing zenith angle of SEVIRI, and a land–sea mask
Summary
High ice clouds hold an exceptional position within the large variety of clouds since they most probably generate positive net radiative forcing and contribute to warming the Earth’s atmosphere (Chen et al, 2000). Coverage and optical properties of ice clouds can be derived by measurements of geostationary satellites equipped with passive instruments – for example METEOSAT Second Generation (MSG) carrying the Spinning Enhanced Visible and Infrared Imager (SEVIRI), which covers about onethird of the Earth’s atmosphere from 80◦ N to 80◦ S and from 80◦ W to 80◦ E with a resolution of 3 km × 3 km at subsatellite point repeating its measurements every 15 min. A new approach was followed to combine the advantages of polar-orbiting active and geostationary passive remote sensing (high sensitivity and accuracy of CALIOP with the high temporal resolution and spatial coverage of SEVIRI): the cirrus optical properties derived from CALIOP and the SEVIRI day and night (COCS) algorithm based on an artificial neural network, which retrieves cirrus optical thickness and cloud top altitude from the thermal infrared channels of SEVIRI allowing day and night observations.
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