the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Dec 2025
Characterisation of cloud shadow transition signatures using a dense pyranometer network
Abstract. Small-scale variability of solar radiation including 3D radiative effects is poorly observed and understood. In this study, we characterise the transition of global solar horizontal irradiance from sunshine to cloud shadow and attribute the transition signature to 3D radiative effects. This analysis is based on 5 case days with shallow cumulus clouds at the ARM Southern Great Plains Central Observatory. Observations are conducted by PyrNet, a network of 60 autonomous pyranometer stations deployed during a field campaign in summer 2023. Complementary observations of cloud mask and shadow motion are derived from the Clouds Optically Gridded by Stereo (COGS) product. Concentrating on shallow cumulus clouds, we explore how geometrical effects and the macro- and microphysical properties of clouds affect the pattern of solar irradiance variation close to the cloud shadow edge. Individual cloud entities and cloud motion vectors are identified using COGS. We discovered that the amplitude of radiation enhancement can reach 20 % above the clear sky values. Significant influence factors are the size of the cloud gaps, the height of the cloud base and the geometry between the sun and the clouds. The distance from the cloud at which radiation enhancement remains significant depends on the effective radius of the cloud droplets, cloud optical depth, and solar zenith angle. Our findings underscore the necessity of accounting for these 3D effects in atmospheric modelling to enhance the representation of solar radiation processes and are a step towards the development of transition signature parametrisations for photovoltaic energy applications.
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Status: open (until 16 Jan 2026)
- RC1: 'Comment on egusphere-2025-5808', Anonymous Referee #1, 23 Dec 2025 reply
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Characterisation of cloud shadow transition signatures using a dense pyranometer network: Code, Notebooks and Datasets Jonas Witthuhn et al. https://doi.org/10.5281/zenodo.17482466
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- 1
This study analyses data from the network of pyranometers with a focus on cloud radiation enhancement. The network was installed at the ARM-SGP site, well-known for its cloud measuring capacities, resulting in a unique data set for detailed cloud enhancement analyses. Along with statistics, the authors investigate effects of macro- and microphysical cloud parameters on enhancement. I find this study interesting; below are my comments.
General comments:
To me, the study lacks quantitative information about the effects. Many violin plots are provided visualizing orange vs blue data, but how close or different those are? Are the differences orange-blue statistically significant? For example, L 291 mentions ‘significant’: in which sense significant? Also comparison with other studies: when similarities are mentioned, is it possible to say if they are quantitatively similar? How big effects can be expected?
I would appreciate a better explanation of the analyses of transition signatures with regards to cloud shadow mask/with regards to flow (Sec. 4.1 vs Sec. 4.2).
Specific comments:
L 20-21: Plant photosynthesis and feedbacks belong to the previous sentence about land-atmosphere interactions, and photovoltaic power generation is more of an engineering application.
L 46: I would remove ‘Building on this network’, it does not anything to the sentence if the same data set was used.
L 88: ‘high concentrations of aerosols from the atmospheric boundary layer’: change ‘from’ to ‘in’
L 95-96: I do not understand why ‘however’ is used, and why not just report cloud shadow speeds, their direction and cloud cover in one sentence.
L 105-106: these instruments, data from which is not used in this study, why they need to be mentioned? instead you could give a bit more information including producers and precision of the instruments that were used for the purposes of this study.
Caption to Fig. 4 ’shadow chord lengths represent arbitrary cross-sections of the shadow shape due to the stationary nature of the measurements’ – this point is not completely clear to me.
L 195: ‘-60m towards shadow and 600m towards…’ It is confusing when speaking about time series in the previous sentence, one uses length as limits. Can you explain better, how you come to these limits?
Fig 5, y-axis: ‘normalized irradiance’ – is not it transmittance? also T is used. I think, Lennard-Jones in the caption should be changed to Buckingham (quite innovative use of potential functions, I found it amusing). I also suggest to clarify definition of epsilon; current is ɛ = Tpeak/clearsky (%) but from the figure it looks like CE in % should be simply (Tpeak-1)*100?
L 225: could authors show an example of fitting in Fig. 5?
Intro to Section 4: side-escape, forward-escape and albedo-enhancement mechanisms: would be good to have a short discussion how those mechanisms could be separated from each other or how they manifest themselves in the current framework. It would also benefit a reader, if the authors could name there all the cloud variables to be considered.
Caption to Fig. 6: ‘distance to the respective cloud (orange) or shadow (blue) mask edge’. Only shadow is mentioned in x-axis label. Also probably I missed it, but how were the masks (distance) collocated with GHI analyses (time series)?
Section 4.1 title does not really say anything; cloud vs shadow mask?
L 271-272: reference is needed about 200 m distance
L 287: ‘is more pronounced on the sunlit side of the cloud, also depending on the cloud depth’. I would appreciate illustration of sunlit vs dark side somewhere in the figures. Then about this effect: how strong effect was found in this study?
L303: ’ shadow irradiance near the sunlit side showed a slight increase, highlighting the notable influence of the albedo-enhancement’ - I do not see this from the figure. What also puzzles me is that optically thickest clouds (b) show the same measured-to clear sky GHI ratio as all cases (a), about 40%. why is that? I would expect thickest clouds should have lower transmittance.
L 337: ‘radiation enhancement, approximately 2% above the existing enhancement level near the shadow’s edge’ – enhancement over enhancement level is quite confusing.
About x_e and epsilon: I think there is not much information about how much these vary, but results of correlation with different variables. It would be interesting to have these linear correlations visualized in the appendix.
Fig. 11: when feature importance is discussed, what method was used? Is it machine learning based? Which model? It would be good to add this information in Methods.
Fig. 11: It is interesting that epsilon and x_e show correlations of opposite sign with the same variable (e.g., solar zenith angle, droplet effective radius). Since both epsilon and x_e characterize the strength of CE, is it possible to make some kind of estimate of 'best' combinations? Are there optimal combinations of variables?