Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 17, issue 10
Articles | Volume 17, issue 10
https://doi.org/10.5194/amt-17-3323-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-3323-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 10
Research article
 | 
31 May 2024
Research article |  | 31 May 2024

Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network

Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.

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Cited articles

Amell, A., Eriksson, P., and Pfreundschuh, S.: Ice water path retrievals from Meteosat-9 using quantile regression neural networks, Atmos. Meas. Tech., 15, 5701–5717, https://doi.org/10.5194/amt-15-5701-2022, 2022. 
Austin, R. T., Heymsfield, A. J., and Stephens, G. L.: Retrieval of ice cloud microphysical parameters using the CloudSat millimeter-wave radar and temperature, J. Geophys. Res., 114, D00A23, https://doi.org/10.1029/2008JD010049, 2009. 
Benjamin, S. G., James, E. P., Hu, M., Alexander, C. R., Ludwig, T. T., Brown, J. M., Weygandt, S. S., Turner, D. D., Minnis, P., Smith Jr., W. L., and Heidinger, A. K.: Stratiform cloud hydro-meteor assimilation for HRRR and RAP model short-range weather prediction, Mon. Weather Rev., 149, 2581–2598, https://doi.org/10.1175/MWR-D-20-0319.1, 2021. 
Cerdeña, A., Gonzalez, A., and Perez, J. C.: Remote sensing of water cloud parameters using neural networks, J. Atmos. Ocean. Tech., 24, 52–63, https://doi.org/10.1175/JTECH1943.1, 2007. 
Chang, F.-L. and Li, Z.: A new method for detection of cirrus overlapping water clouds and determination of their optical properties, J. Atmos. Sci., 62, 3993–4009, https://doi.org/10.1175/JAS3578.1, 2005. 
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Multilayer clouds (MCs) affect the radiation budget differently than single-layer clouds (SCs) and need to be identified in satellite images. A neural network was trained to identify MCs by matching imagery with lidar/radar data. This method correctly identifies ~87 % SCs and MCs with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.
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