Cirrus Observation with METEOSAT-SEVIRI IR Data
Kox, Stephan; Huber, Isabelle; Vazquez-Navarro, Margarita; Bugliaro, Luca; Mannstein, Hermann
German Aerospace Center, DLR, GERMANY
Cirrus clouds have a substantial impact on the radiation budget of the earth and therefore on climate. However, their representation in weather and climate prediction models is still suffering from a lack of validation data. For a better understanding, especially of thin cirrus clouds, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission was launched in April 2006 providing global observations of aerosols and clouds with its Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP).
In this work the results of the COCS algorithm (Cirrus Optical properties derived from CALIOP and SEVIRI during day and night time) are validated and discussed. The COCS algorithm, based on an Artificial Neural Network, calculates optical and physical properties of cirrus clouds (e.g. ice optical thickness, cloud top altitude, ice water path, and effective radius) based on the brightness temperatures measured by SEVIRI (Spinning Enhanced Visible and Infrared Imager) onboard of the operational geostationary satellites of the METEOSAT series.
The Neural Network was trained on a basis of two years temporally and geographically collocated data of CALIOP and SEVIRI.
Hence the algorithm combines the advantages of CALIOP (high vertical and spatial resolution) and SEVIRI (high temporal resolution of 5 to 15 min., covering an area from around 80 S to 80 N and 80 W to 80 E), which are eminent for the studies of the lifecycle, diurnal cycles and seasonal variations of cirrus properties and cirrus coverage.
Here we present analyses of the diurnal variation of cirrus coverage, optical depth, and other properties for selected regions, as well as observations of contrails and contrail cirrus derived by the combination of the Automatic Contrail Tracking Algorithm (ACTA) and COCS.