Articles | Volume 10, issue 11
Atmos. Meas. Tech., 10, 4317–4339, 2017
https://doi.org/10.5194/amt-10-4317-2017
Atmos. Meas. Tech., 10, 4317–4339, 2017
https://doi.org/10.5194/amt-10-4317-2017

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.

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Cited articles

Ackerman, S. A., Smith, W. L., Revercomb, H. E., and Spinhirne, J. D.: The 27–28 October 1986 FIRE IFO cirrus case study: spectral properties of cirrus clouds in the 8–12 µm window, Mon. Weather Rev., 118, 2377–2388, https://doi.org/10.1175/1520-0493(1990)118<2377:TOFICC>2.0.CO;2, 1990.
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CALIPSO Science Team: CALIPSO/CALIOP Level 2, Lidar Cloud Layer Data, version 3.01, Hampton, VA, USA: NASA Atmospheric Science Data Center (ASDC), https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmCLay-Prov-V3-01_L2-003.01, 2015a.
CALIPSO Science Team: CALIPSO/CALIOP Level 2, Lidar Cloud Layer Data, version 3.02, Hampton, VA, USA: NASA Atmospheric Science Data Center (ASDC), https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmCLay-Prov-V3-02_L2-003.02, 2015b.
CALIPSO Science Team: CALIPSO/CALIOP Level 2, Lidar Aerosol Layer Data, version 3.01, Hampton, VA, USA: NASA Atmospheric Science Data Center (ASDC), https://doi.org/10.5067/CALIOP/CALIPSO/CAL_LID_L2_05kmALay-Prov-V3-01_L2-003.01, 2015c.
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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