09 May 2022
09 May 2022
Status: a revised version of this preprint is currently under review for the journal AMT.

A CO2-free cloud mask from IASI radiances for climate applications

Simon Whitburn1, Lieven Clarisse1, Marc Crapeau2, Thomas August2, Tim Hultberg2, Pierre François Coheur1, and Cathy Clerbaux1,3 Simon Whitburn et al.
  • 1Spectroscopy, Quantum Chemistry and Atmospheric Remote Sensing (SQUARES), Université libre de Bruxelles (ULB), Brussels, Belgium
  • 2European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), Darmstadt, Germany
  • 3LATMOS/IPSL, Sorbonne Université, UVSQ, CNRS, Paris, France

Abstract. With more than 15 years of continuous and consistent measurements, the Infrared Atmospheric Sounding Interferometer (IASI) radiance dataset is becoming a reference climate data record. To be exploited to its full potential, it requires a cloud filter that is both accurate, unbiased over the full IASI lifespan, and strict enough to be used in satellite data retrieval schemes. Here, we present a new cloud detection algorithm which combines (1) a high sensitivity, (2) a good consistency over the whole IASI time series and between the different copies of the instrument flying on board the suite of Metop satellites and (3) simplicity in its parametrization. The method is based on a supervised neural network (NN) and relies, as input parameters, on the IASI radiance measurements only. The robustness of the cloud mask over time is ensured in particular by avoiding the IASI channels that are influenced by CO2, N2O, CH4, CFC-11 and CFC-12 absorption lines and those corresponding to the ν2 H2O absorption band. As a reference dataset for the training, the latest version of the operational IASI Level 2 (L2) cloud product is used. We provide different illustrations of the NN cloud product, including comparisons with other existing products. We find a very good agreement overall with the last version of the operational IASI L2 with an identical mean annual cloud amount and a pixel-by-pixel correspondence of about 87 %. The comparison with the other cloud products shows a good correspondence in the main cloud regimes but with sometimes large differences in the mean cloud amount (up to 10 %) due to the specificities of each of the different products. We also show the good capability of the NN product to differentiate clouds from dust plumes.

Simon Whitburn et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-127', Anonymous Referee #1, 25 May 2022
    • AC1: 'Reply on RC1', Simon Whitburn, 05 Sep 2022
  • RC2: 'A good methodological work, which needs some generalization and improvement', Artem Feofilov, 24 Jun 2022
    • AC2: 'Reply on RC2', Simon Whitburn, 05 Sep 2022

Simon Whitburn et al.

Simon Whitburn et al.


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Short summary
With more than 15 years of measurements, the IASI radiance dataset is becoming a reference climate data record. Their exploitation for satellite applications requires an accurate and unbiased detection of the 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 reference the last version of the operational IASI L2 cloud product.