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A systematic light pollution modelling bias in present night sky brightness predictions

Nature Astronomy volume 7, pages 269–279 (2023)Cite this article

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Abstract

Night sky brightness (NSB) measurements inform both the selection of new ground-based optical and near-infrared astronomical observatory sites and the management of night-time conditions near existing facilities. NSB modelling supports site characterization in cases where obtaining in situ measurements is difficult and provides additional information about the photic environment. Models of NSB conventionally assume Mie scattering, but we show that predominantly at low-altitude sites contaminated by artificial light, this assumption may result in significant errors up to a factor of ~2.5. This effect introduces systematic bias to models of anthropogenic skyglow, but more realistic modelling approaches tend to be computationally intensive. We demonstrate the significance of these effects by simulating NSB while accounting for increasingly realistic populations of aerosol components. The use of approximate scattering phase functions reduces computing time while yielding results that reasonably match field observations. This approach can speed up models used to predict NSB while increasing their accuracy.

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Fig. 1: Daylight versus night light: similarities and differences in the range of modelling complexity.
Fig. 2: Aerosol morphology impact on NSB distribution.
Fig. 3: Circumzenithal NSB minima tend to be deeper if spherical shapes dominate the population of atmospheric aerosols.
Fig. 4: Approximate scattering phase functions may compete with exact theories to model NSB.

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Data availability

The numerical results for the all-sky radiance distributions were computed using the model available in ref. 10. The scattering phase functions for dust particles and spherical particles are retrievable in graphical form from ref. 31. Normalized phase functions in digitized form and experimental and computed NSB distributions are available from the corresponding author upon reasonable request. All data that support this study are available in the Supplementary Information.

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Acknowledgements

This work was supported by the Slovak Research and Development Agency under contract no. APVV-18-0014. Computational work was supported by the Slovak National Grant Agency VEGA (grant no. 2/0010/20). This work is part of the project for which the financial means was provided by the European Union’s Horizon 2020 Research and Innovation Programme on the basis of the Grant Agreement under the Marie Skłodowska-Curie funding mechanism no. 945478 - SASPRO 2.

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Authors and Affiliations

  1. ICA, Slovak Academy of Sciences, Bratislava, Slovak Republic

    M. Kocifaj, L. Kómar, H. Lamphar & and S. Wallner

  2. Faculty of Mathematics, Physics, and Informatics, Comenius University, Mlynská Dolina, Bratislava, Slovak Republic

    M. Kocifaj

  3. Dark Sky Consulting, LLC, Tucson, AZ, USA

    J. Barentine

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Contributions

M.K. designed and performed the research. L.K. conducted Mie, T-matrix and discrete dipole approximation computations, while M.K. computed all-sky radiance using MSOS-LP1 and analysed and interpreted the results obtained. H.L. processed the data graphically, S.W. performed field experiments and J.B. reviewed and commented on findings. M.K., J.B. and S.W. wrote the paper.

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Correspondence to M. Kocifaj.

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Nature Astronomy thanks Stefano Cavazzani, Johannes Puschnig and Jessica Arnold for their contribution to the peer review of this work.

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Kocifaj, M., Kómar, L., Lamphar, H. et al. A systematic light pollution modelling bias in present night sky brightness predictions. Nat Astron 7, 269–279 (2023). https://doi.org/10.1038/s41550-023-01916-y

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