Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 15, issue 22
Articles | Volume 15, issue 22
https://doi.org/10.5194/amt-15-6653-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-6653-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 22
Research article
 | 
21 Nov 2022
Research article |  | 21 Nov 2022

A CO2-independent cloud mask from Infrared Atmospheric Sounding Interferometer (IASI) radiances for climate applications

Simon Whitburn, Lieven Clarisse, Marc Crapeau, Thomas August, Tim Hultberg, Pierre François Coheur, and Cathy Clerbaux

Supplement

https://doi.org/10.5194/amt-15-6653-2022-supplement

Data sets

A CO2-independant cloud mask from IASI radiances for climate applications (from IASI/Metop-A) Simon Whitburn https://doi.org/10.21413/IASI-FT_METOPA_CLD_L2_ULB-LATMOS

A CO2-independant cloud mask from IASI radiances for climate applications (from IASI/Metop-B) Simon Whitburn https://doi.org/10.21413/IASI-FT_METOPB_CLD_L2_ULB-LATMOS

A CO2-independant cloud mask from IASI radiances for climate applications (from IASI/Metop-C) Simon Whitburn https://doi.org/10.21413/IASI-FT_METOPC_CLD_L2_ULB-LATMOS

Download
Short summary
With more than 15 years of measurements, the IASI radiance dataset is becoming a reference climate data record. Its exploitation for satellite applications requires an accurate and unbiased detection of cloud scenes. Here, we present a new cloud detection algorithm for IASI that is both sensitive and consistent over time. It is based on the use of a neural network, relying on IASI radiance information only and taking as a reference the last version of the operational IASI L2 cloud product.
Read more