Articles | Volume 9, issue 11
https://doi.org/10.5194/gmd-9-3919-2016
https://doi.org/10.5194/gmd-9-3919-2016
Development and technical paper
 | 
02 Nov 2016
Development and technical paper |  | 02 Nov 2016

A method for retrieving clouds with satellite infrared radiances using the particle filter

Dongmei Xu, Thomas Auligné, Gaël Descombes, and Chris Snyder

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Cited articles

Ackerman, S. A., Strabala, K. I., Menzel, W. P., Frey, R. A., Moeller, C. C., and Gumley, L. E.: Discriminating clear sky from clouds with MODIS, Geophys. Res.-Atmos., 103, 32141–32157, 1998.
Auligné, T.: Multivariate minimum residual method for cloud retrieval. Part I: Theoretical aspects and simulated observation experiments, Mon. Weather Rev., 142, 4383–4398, 2014a.
Auligné, T.: Multivariate minimum residual method for cloud retrieval. Part II: Real observations experiments, Mon. Weather Rev., 142, 4399–4415, 2014b.
Auligné, T., Lorenc, A., Michel, Y., Montmerle, T., Jones, A., Hu, M., and Dudhia, J.: Toward a New Cloud Analysis and Prediction System, B. Am. Meteorol. Soc., 92, 207–210, 2011.
Aumann, H. H., Chahine, M. T., Gautier, C., Goldberg, M. D., Kalnay, E., McMillin, L. M., Revercomb, H., Rosenkranz, P. W., Smith, W. L., and Staelin, D. H.: AIRS/AMSU/HSB on the Aqua mission: Design, science objectives, data products, and processing systems, Geosci. Remote Sens., 41, 253–264, 2003.
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Short summary
This study proposed a new cloud retrieval method based on the particle filter (PF). The PF cloud retrieval method is compared with the Multivariate and Minimum Residual (MMR) method that was previously established and verified. Cloud retrieval experiments involving a variety of cloudy types are conducted with the PF and MMR methods with measurements of Infrared radiances on multi-sensors onboard both GOES and MODIS, respectively.