Articles | Volume 21, issue 16
Atmos. Chem. Phys., 21, 12273–12290, 2021
https://doi.org/10.5194/acp-21-12273-2021
Atmos. Chem. Phys., 21, 12273–12290, 2021
https://doi.org/10.5194/acp-21-12273-2021
Research article
17 Aug 2021
Research article | 17 Aug 2021

Understanding the model representation of clouds based on visible and infrared satellite observations

Stefan Geiss et al.

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Cited articles

Bachmann, K., Keil, C., Craig, G. C., Weissmann, M., and Welzbacher, C. A.: Predictability of deep convection in idealized and operational forecasts: Effects of radar data assimilation, orography, and synoptic weather regime, Mon. Weather Rev., 148, 63–81, 2020. a
Baldauf, M., Gebhardt, C., Theis, S., Ritter, B., and Schraf, C.: Beschreibung des operationellen Kürzestfristvorhersagemodells COSMO-D2 und COSMO-D2-EPS und seiner Ausgabe in die Datenbanken des DWD (2018), available at: https://www.dwd.de/SharedDocs/downloads/DE/modelldokumentationen/nwv/cosmo_d2/cosmo_d2_dbbeschr_version_1_0_201805.pdf?__blob=publicationFile&v=3 (last access: 14 August 2021) 2018. a
Baum, B. A., Yang, P., Heymsfield, A. J., Bansemer, A., Cole, B. H., Merrelli, A., Schmitt, C., and Wang, C.: Ice cloud single-scattering property models with the full phase matrix at wavelengths from 0.2 to 100 µm, J. Quant. Spectrosc. Ra., 146, 123–139, https://doi.org/10.1016/j.jqsrt.2014.02.029, 2014. a
Bechtold, P., Semane, N., Lopez, P., Chaboureau, J.-P., Beljaars, A., and Bormann, N.: Representing Equilibrium and Nonequilibrium Convection in Large-Scale Models, J. Atmos. Sci., 71, 734–753, https://doi.org/10.1175/JAS-D-13-0163.1, 2014. a
Böhme, T., Stapelberg, S., Akkermans, T., Crewell, S., Fischer, J., Reinhardt, T., Seifert, A., Selbach, C., and Van Lipzig, N.: Long-term evaluation of COSMO forecasting using combined observational data of the GOP period, Meteorol. Z., 20, 119–132, 2011. a
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Short summary
This study demonstrates the benefits of using both visible and infrared satellite channels to evaluate clouds in numerical weather prediction models. Combining these highly resolved observations provides significantly more and complementary information than using only infrared observations. The visible observations are particularly sensitive to subgrid water clouds, which are not well constrained by other observations.
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