24 Feb 2021

24 Feb 2021

Review status: this preprint is currently under review for the journal ACP.

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

Stefan Geiss1, Leonhard Scheck1,2, Alberto de Lozar2, and Martin Weissmann3 Stefan Geiss et al.
  • 1Hans-Ertel Centre for Weather Research, Ludwig-Maximilians-Universität, Munich, Germany
  • 2Deutscher Wetterdienst, Offenbach, Germany
  • 3Institut für Meteorologie und Geophysik, Universität Wien, Vienna, Austria

Abstract. Satellite observations provide a wealth of information on atmospheric clouds and cover almost every region of the globe with high spatial resolution. The measured radiances constitute a valuable data set for evaluating and improving clouds and radiation representation in climate and numerical weather prediction (NWP) models. An accurate, bias-free representation of clouds and radiation is crucial for data assimilation and the increasingly important solar photovoltaic (PV) power production prediction. The present study demonstrates that visible (VIS) and infrared (IR) Meteosat SEVIRI observations contain valuable and complementary cloud information for these purposes.

We analyse systematic deviations between satellite observations and convection-permitting, semi-free ICON-D2 hindcast simulations for a 30-day period with strong convection. Both visible and infrared satellite observations reveal significant deviations between the observations and model equivalents. The combination of infrared brightness temperature and visible solar reflectance allowed to attribute individual deviations to specific model shortcomings. Furthermore, we investigate the sensitivity of model-derived VIS and IR observation equivalents to modified model and visible forward operator settings to identify dominant error sources. The results reveal that model assumptions on subgrid-scale water clouds are the primary source of systematic deviations in the visible spectrum. Visible observations are, therefore, well-suited to advance this essential model assumption. The visible forward operator uncertainty is lower than uncertainties introduced by model parameter assumptions by one order of magnitude. In contrast, infrared satellite observations are very sensitive to ice cloud model assumptions. Finally, we show a strong negative correlation between VIS solar reflectance and global horizontal irradiance. This implies that improvements in VIS satellite reflectance prediction will coincide with improvements in the prediction of surface irradiation and PV power production.

Stefan Geiss et al.

Status: open (until 21 Apr 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-5', Anonymous Referee #1, 01 Apr 2021 reply
  • RC2: 'Comment on acp-2021-5', Matthew Igel, 09 Apr 2021 reply

Stefan Geiss et al.

Data sets

Understanding the model representation of clouds based on visible and infrared satellite observations - a data set Geiss, Stefan; Scheck, Leonhard; de Lozar, Alberto; Weissmann, Martin

Stefan Geiss et al.


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Short summary
The present study facilitates a novel approach based on both visible and infrared satellite observations to evaluate and improve the representation of clouds and radiation in climate and numerical weather prediction (NWP) models. The combination of observations in these two spectral ranges provides significantly more and complementary information than the use of only infrared observations pursued in previous studies.