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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 10, issue 11
Atmos. Meas. Tech., 10, 4317–4339, 2017
https://doi.org/10.5194/amt-10-4317-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 10, 4317–4339, 2017
https://doi.org/10.5194/amt-10-4317-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

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 et al.
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

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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
We characterise the the performance of a set of artificial neural networks used for the remote...
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