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Correcting Sky Quality Meter Measurements for Aging Effects MNRAS 000,1–6 (2020) Preprint 9 December 2020 Compiled using MNRAS LATEX style file v3.0 Correcting Sky Quality Meter measurements for aging effects using twilight as calibrator Johannes Puschnig,1¢ Magnus Näslund,2 Axel Schwope,3 Stefan Wallner4 1Universität Bonn, Argelander-Institut für Astronomie, Auf dem Hügel 71, D-53121 Bonn, Germany 2Department of Astronomy, Stockholm University, AlbaNova University Centre, SE-10691 Stockholm, Sweden 3Leibniz-Institut für Astrophysik Potsdam (AIP), An der Sternwarte 16, 14482 Potsdam, Germany 4Universität Wien, Institut für Astrophysik, Türkenschanzstraße 17, A-1180 Wien, Austria Submitted to MNRAS, Dec 7, 2020 ABSTRACT In the last decade numerous Sky Quality Meters (SQMs) were installed throughout the globe, aiming to assess the temporal change of the night sky brightness (NSB), and thus the change in light pollution. However, it has become clear that SQM readings may be affected by aging effects such as degradation of the sensor sensitivity and/or loss of transmissivity of optical components (filter, housing window). To date, the magnitude of the darkening has not been assessed in a systematic way. We report for the first time on the quantification of the SQM aging effect and describe the applied method. We combine long-term SQM measurements obtained between 2011 and 2019 in Potsdam-Babelsberg (23 km to the southwest of the center of Berlin), Vienna and Stockholm with a readily available empirical twilight model, which serves as calibrator. Twilight SQM observations, calibrated for changing sun altitudes, reveal a linear degradation of the measurement systems (SQM + housing window) with the following −2 −1 slopes: 34±4, 46±2 and 53±2 milli-magSQM arcsec yr for Stockholm, Potsdam-Babelsberg and Vienna. With the highest slope found in Vienna (latitude ∼48◦) and the lowest one found in Stockholm (latitude ∼59◦), we find an indication for the dependence of the trend on solar irradiance (which is a function of geographic latitude). Key words: light pollution – atmospheric effects – methods: data analysis 1 INTRODUCTION most of the SQMs mounted at weather stations. Similarly, starting end of 2009, in Galicia (Spain) a number of 20 SQMs were installed It was shown by numerous studies that the ever increasing amount at weather stations of the official Galician meteorological agency2 in of artificial light at night (ALAN) has far reaching consequences, cooperation with the University of Santiago de Compostela (Bará not only for astronomy, energy consumption and carbon footprint, et al. 2019). Being part of a meteorological monitoring system but also for animal and human health (Chepesiuk 2009; Haim & makes these two networks to showcases for future monitoring cam- Portnov 2013; Cho et al. 2015; Garcia-Saenz et al. 2018), ecosys- paigns around the globe. However, other SQM networks of similar tems (Longcore & Rich 2004) and biodiversity (Hölker et al. 2010). scale exist: The Spanish Light Pollution Research collaboration3 Because ALAN impacts life on Earth in such a drastic way, and runs 18 devices, the ‘NachtMeetnet’ network (Schmidt & Spoelstra arXiv:2012.04042v1 [astro-ph.IM] 7 Dec 2020 on a global scale, it was proposed to monitor light pollution in a 2020) counts 15 SQMs, the University of Hong Kong4 owns 19 sta- similar manner as other pollutants. Although no uniform measuring tions, and seven SQMs are permanently installed in Italy’s Veneto standard has been implemented to date, in recent years, a number of region (Bertolo et al. 2019). Many other small scale networks or individuals, observatories and organisations have started to monitor single SQMs are found around the globe. Kyba et al.(2015) have the night sky brightness (NSB) using different methods and devices compiled many of these heterogeneous data sets and analyzed it in (Hänel et al. 2018), of which the so called Sky Quality Meter (SQM), a consistent way to quantify the properties of skyglow. is probably the most widely used one. At present, scientific interpretation in terms of long-term trend SQM networks of larger scale have been established, for ex- analysis of the large amount of readily available SQM data, is hin- ample, in Upper Austria1, where a total of 23 SQMs are operational dered by the fact that the magnitude of degradation of SQM mea- since end of 2015 (Posch et al. 2018; Puschnig et al. 2020), with 2 http://www.meteogalicia.gal/Caire/brillodoceo.action ¢ E-mail: [email protected] 3 http://guaix.fis.ucm.es/splpr/SQM-REECL 1 https://www.land-oberoesterreich.gv.at/159659.htm 4 http://nightsky.physics.hku.hk/ © 2020 The Authors 2 Puschnig, Näslund, Schwope, Wallner surements (due to change in sensor sensitivity, filters and/or housing available only through forecast models with a validity given in 3- window) on operation timescales of several years is unknown. It is hour steps. The spatial resolution of the data grid is 30 and 80km for thus a timely matter to understand how SQMs loose the initial ERA5 and CAMS Near-real-time respectively. Furthermore, some calibration when operated over multiple years. In this paper, we parameters (e.g. particulate matter) are only available starting from quantify the aging effect for the first time, using long-term SQM 2015. Relevant parameters for the three cities of Vienna, Potsdam measurements, re-calibrated during post-processing with twilight and Stockholm are shown in Figures A1–A3. models. 3 MODELS AND METHODS 2 MEASUREMENTS AND DATA In order to reveal the existence of darkening of SQM measurements We are operating SQMs in Potsdam-Babelsberg (23 km to the south- over time, we use empirical twilight models previously published by west of the center of Berlin), Vienna and Stockholm since 2011, Patat et al.(2006) as calibration source. In particular, the temporal 2012 and 2015 respectively, aiming to monitor light pollution over change of the difference between predicted NSB (mdl) and the long time scales. The exact geographical coordinates of the mea- observed one (obs), Δmdl−obs, will be used as a proxy of the aging surement stations are given in Table1. As the atmospheric com- effect. The models are based on a statistically significant sample of position (aerosols, ozone, particulate matter) plays a major role in thousands of UBVRI (Johnson-Cousins system) twilight sky flats interpreting NSB measurements, we further use meteorological pa- obtained during several years on mount Paranal (Chile) as part of rameters obtained through the Copernicus Climate Change Service an instrument calibration procedure. Hence, they are valid only ◦ (C3S) information 2020 and the Copernicus Atmosphere Monitor- for a limited range of solar zenith distances (z), i.e. 95–105 . The ing Service (CAMS) Information 2020 (Inness et al. 2019), in order functional (polynomial) form for the zenithal NSB during twilight to monitor any systematic changes of the atmospheric composition is shown in equation1. The coefficients for the BVR filters are found within the period of time under consideration. in Table2. 2 #(퐵 = 00 ¸ 01 ∗ ¹I − 95º ¸ 02 ∗ ¹I − 95º (1) 2.1 SQM measurements Given the fact that the calculation of atmospheric diffuse flux is a rather complicated problem that requires a detailed treatment of A first analysis of the SQM data obtained in Potsdam-Babelsberg multiple scatterings (Kocifaj & Bará 2019) and knowledge of atmo- and Vienna was previously presented in (Puschnig et al. 2014b) spheric composition such as aerosols, ozone and particulate matter and (Puschnig et al. 2014a), and some of the Stockholm data was (Joseph et al. 1991; Ściężor & Kubala 2014; Ściężor & Czaplicka already discussed in (Posch et al. 2018, Section 4.3 therein). 2020). it is expected that the residuals of Δ show discrepan- The exact model designation is SQM-LE, indicating that the mdl−obs cies. However, the average magnitude of these discrepancies should devices are equipped with a front lens (L) and ethernet (E) con- be independent of time, as long as the average atmospheric com- nector. The SQMs are permanently installed and point towards the position has not changed significantly. In that case, and as long as zenith. Given the Gaussian-like angular response as described in a large number of independent observations were carried out, one Cinzano(2007) the reported radiances in units of mag arcsec−2 SQM would expect that the residual of Δ , measured over multiple are representative for the average zenithal sky brightness found in a mdl−obs years, is given by a constant, plus scatter with a roughly constant circum-zenithal region with a radius of 10 degrees. Our SQMs are magnitude, caused by complex atmospheric physics. We may thus controlled via home-brewed Perl scripts, producing measurements formulate the null hypothesis that any temporal trend (slope) in the at frequencies of approximately 0.14 (IFA, STO) and 0.5 (BA1) Hz. residual (twilight model minus SQM measurements) is caused by Further technical details are found in Cinzano(2005) and Bará et al. instrumental effects (i.e. the SQM aging effect), as long as the av- (2019). erage composition of the atmosphere has not changed significantly (towards lower abundances) within the same period of time. We 2.2 Archival Meteorological Data would further expect that the degree of darkening (aging effect) is a function of solar surface radiance. Given the range in geographic lat- Large-scale atmospheric and meteorological parameters were ob- itudes of our measurement stations it is thus expected that the effect tained through MARS, the Meteorological Archival and Retrieval of darkening is weakest in Stockholm and strongest in Vienna. System, which provides users with meteorological forecast and anal- In addition to the previously described scatter of Δmdl−obs, ysis results from the European Centre for Medium-Range Weather clouds have a strong impact on NSB (Puschnig et al. 2014b; Jechow Forecasts (ECMWF) in GRIB format. In particular, we retrieved et al.
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