Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 10, issue 11
Articles | Volume 10, issue 11
https://doi.org/10.5194/amt-10-4317-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-10-4317-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 11
Research article
 | 
14 Nov 2017
Research article |  | 14 Nov 2017

Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI

Johan Strandgren, Jennifer Fricker, and Luca Bugliaro

Data sets

CALIPSO/CALIOP Level 2, Lidar Cloud Layer Data, version 3.01 CALIPSO Science Team https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmCLay-Prov-V3-01_L2-003.01

CALIPSO/CALIOP Level 2, Lidar Cloud Layer Data, version 3.02 CALIPSO Science Team https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmCLay-Prov-V3-02_L2-003.02

CALIPSO/CALIOP Level 2, Lidar Aerosol Layer Data, version 3.01 CALIPSO Science Team https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmALay-Prov-V3-01_L2-003.01

CALIPSO/CALIOP Level 2, Lidar Aerosol Layer Data, version 3.02 CALIPSO Science Team https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmALay-Prov-V3-02_L2-003.02

Download
Short summary
We characterise the the performance of a set of artificial neural networks used for the remote sensing of cirrus clouds from the geostationary Meteosat Second Generation satellites. The retrievals show little interference with the underlying land surface type as well as with possible liquid water clouds or aerosol layers below the cirrus cloud. We also characterise the retrievals as a funtion of optical thickness and top height and gain better understanding of the retrival uncertainties of CiPS
Read more