Articles | Volume 16, issue 21
https://doi.org/10.5194/amt-16-5305-2023
https://doi.org/10.5194/amt-16-5305-2023
Research article
 | 
09 Nov 2023
Research article |  | 09 Nov 2023

A neural-network-based method for generating synthetic 1.6 µm near-infrared satellite images

Florian Baur, Leonhard Scheck, Christina Stumpf, Christina Köpken-Watts, and Roland Potthast

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Cited articles

Baum, B. A., Soulen, P. F., Strabala, K. I., King, M. D., Ackerman, S. A., Menzel, W. P., and Yang, P.: Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS: 2. Cloud thermodynamic phase, J. Geophys. Res.-Atmos., 105, 11781–11792, https://doi.org/10.1029/1999JD901090, 2000. a
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Baum, B. A., Yang, P., Nasiri, S., Heidinger, A. K., Heymsfield, A., and Li, J.: Bulk Scattering Properties for the Remote Sensing of Ice Clouds. Part III: High-Resolution Spectral Models from 100 to 3250 cm−1, J. Appl. Meteorol. Clim., 46, 423, https://doi.org/10.1175/JAM2473.1, 2007. a
Bormann, N., Lawrence, H., and Farnan, J.: Global observing system experiments in the ECMWF assimilation system, ECMWF Technical Memorandum 839, ECMWF, https://doi.org/10.21957/sr184iyz, 2019. a
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Short summary
Near-infrared satellite images have information on clouds that is complementary to what is available from the visible and infrared parts of the spectrum. Using this information for data assimilation and model evaluation requires a fast, accurate forward operator to compute synthetic images from numerical weather prediction model output. We discuss a novel, neural-network-based approach for the 1.6 µm near-infrared channel that is suitable for this purpose and also works for other solar channels.