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DevisTenerife2009

2009 IAC Invited Scientist project proposal



Martin Aubé, Ph.D.

CÉGEP de Sherbrooke, Canada

Université de Sherbrooke, Canada

Title

Assessing the contribution from different parts of Tenerife and La Palma islands to artificial spectral sky luminance levels over European Northern Observatories.

Summary

We suggest to use a third generation sky luminance model which account for heterogeneous distribution of light fixtures, their photometry, the ground reflectance and topography along with hyperspectral sky luminance measurements to infer contribution of different zones of Tenerife and La Palma islands to astronomical observation sites sky luminances. This sensitive study will allow the identification and evaluation of critical island zones. The project aim to identify and characterize zones at which any lighting level increase or decrease may have a larger impact on light pollution at both European Northern Observatory sites, and then help to control and/or reduce their light pollution levels.

Methodology

As a first step, a measurement campaign will be made across Tenerife and La Palma islands in order to get a relatively good spatial sampling of the spectral sky brightness levels. This field campaign should involve the acquisition over at least 20 sampling sites. Sites will be chosen in order to sample uniformly both islands and will fit with island modeling zones. Modeling zones may be defined for example as a central zone around the observatory and as 45 degrees radial subdivision of the remaining surface. For each of these sampling sites, measurement should be made toward zenith and forward/backward along observer to main nearby cities line of sight at 15 deg. above horizon. Half of that dataset will be used as tie down points to calibrate the gridded light pollution model. The second half will be used later to evaluate model errors. The instrument, which will be used to accomplish observations, is the third version of the Spectrometer for Aerosol Night Detection (SAND-3). SAND-3 have been designed by our group. The spectrometer is robotized so that it may operate by its own during all night long. The spectrometer will be recalibrated prior to the experiment at the Centre d'Application et de Recherche en TÉLédétection (CARTEL) laboratory located in Université de Sherbrooke, Canada. SAND have been used successfully for about 4 years to characterize light pollution on many astronomical sites across North America (e.g. Palomar, Kitt Peak National Observatory (KPNO), Fred Lawrence Whipple Observatory (FLWO), US Naval Observatory (USNO), Lowell and Mégantic). The field campaign proposed here may require more or less 2 month depending on clear sky conditions.

The second step of the project will be to acquire input database required to run our model called ILLUMINA (Aubé et al. 2005, Aubé 2007). ILLUMINA may be described as a third generation light pollution model (like Kocifaj 2007). First generation models (e.g. Walker 1977) mainly address the sky brightness to distance relationship. Second generation models (e.g. Garstang 1986, Cinzano 2000) implements mulitangular dependence and atmospheric properties but relies on some basic assumptions about the geometry of light distribution on the ground (circular cities) and ground homogeneity. They also used empirical parametrization of atmospheric transfer process and light output angular pattern. Improvements of ILLUMINA model reside in its capability to simulate any heterogeneous distribution of a variety of light fixture with their own intensities, spectral dependences, and angular light output pattern. The model may also account for shadowing effects associated with topography, for gridded variation in ground reflectance and subgrid obstacles (trees, buildings, etc.). Explicit 1st and 2nd order scattering and extinction from aerosols and molecules and the vertical profile of atmospheric constituants is taken into account. ILLUMINA also compute optical impact of size distribution and composition of aerosol content which may be quite useful during pollution events like important biomass burning or saharian sand storms. To perform a modeling experiment, we have to feed the model with gridded dataset of light types and intensity distribution, digital elevation model (DEM), and ground spectral reflectance. Model also requires typical mean light free path toward the ground and typical obstacles height, ground atmospheric pressure, aerosol optical depth and angstrom coefficient. Light type and intensities may be either obtained from local inventories or estimated from population density data and in-situ sampling. Ground spectral reflectance will be obtained from remotely sensed data of the MODerate-resolution Imaging Spectroradiometer (MODIS) (Vermote and Vermeulen 1999). Atmospheric pressure data will be taken from local meteorological station and aerosol optical properties from active NASA-AERONET sunphotometers (Holben et al. 2001) located on Tenerife island.

The third step will be to run the model for each observing night with the complete input dataset and compare model results with the first half of sky luminance measurements. As stated earlier this step aimed to calibrate the model in order to fit observations. Then the second half of measurements will be used to map typical model errors.

Finally we will perform about 40 model runs by "lighting on" alternatively only one sampling zone at a time. The goal of that experiment is to determine the contribution of each zone to the sky luminance at each observing site so that ones should be able to infer the impact of local change in lighting device inventory. That kind of results will be extremely useful to identify critical zones and then orient any future intervention/abatement. It may be used as a high level decision tool by local decision makers and authorities (IAC or others).

Time table

The project will take place over a six month period. Major project milestones along with estimated associated time are listed below .

  • September 2009 - Beginning of the project
  • September - October 2009 - Sky spectral luminance sampling experiment across Tenerife and La Palma
  • November 2009 - Preparation of the model input dataset
  • December 2009 - Model calibration and estimation of errors
  • January 2010 - Islands zones modeling experiments
  • February 2010 - Final report and/or publication writing

Budget requirements estimation

Cost Source
U. Sherb.
Source
FQRNT
Source
IAC
Task/item
International transport 1000 Euros x
Car rental - Tenerife observations (~15 nights) 1200 Euros x
Lodging - Tenerife observations (~15 nights) 1500 Euros x
Meals - Tenerife observations (~15 nights) 650 Euros x
Meals and logding -Tenerife observatory (2 nights) - x
Car rental - La Palma observations (~15 nights) 1200 Euros x
Lodging - La Palma observations (~15 nights) 1500 Euros x
Meals - La Palma observations (~15 nights) 650 Euros x
Meals and logding - La Palma observatory (2 nights) - x
Gas 800 Euros x
Inter-island Transport (2 flights) 300 Euros x
Food and accomodation stipend (6 month) 6000 Euros x
SAND-3 utilization - x
Computing time - x x
Researcher salary 25000 Euros x
Researcher computer - x
Office w internet connection & Linux pc - x
TOTAL 25000 Euros 7500 Euros 7300 Euros

References

Aubé, M., Franchomme-Fosse, L., Robert-Staehler, P. and Houle, V. (2005). Light pollution modelling and detection in a heterogeneous environment : Toward a night time aerosol optical depth retrieval method. in Atmospheric and environmental remote sensing data processing and utilization : Numerical atmospheric prediction and environmental monitoring (Vol. 5890, p. 1-9), International Society for Optical Engineering, Bellingham WA, WA 98227-0010, United States.

Aubé , M. (2007). Light pollution modeling and detection in a heterogeneous environment. in e C. Marìn and J. Jafari (Eds.), Starlight 2007 (p. 119-126). European Council for an Energy Efficient Economy.

Aumann, C. A. (2007). A methodology for developing simulation models of complex systems. Ecological Modelling, vol. 202, no 3-4, p. 385-396.

Balci, O. (1994). Validation, verification, and testing techniques throughout the life cycle of a simulation study. Annals of Operations Research, vol. 53, no 1-4, p. 121-173.

Cinzano, P., Falchi, F., Elvidge, C. D. and Baugh, K. E. (2000). The artificial night sky brightness mapped from dmsp satellite operational linescan system measurements. Monthly Notices of the Royal Astronomical Society, vol. 318, no 3, p. 641-657.

Garstang, R. H. (1986). Model for artificial night-sky illumination. Publications of the Astronomical Society of the Pacific, vol. 98, no 601, p. 364-375.

Holben, B.N., D.Tanre, A.Smirnov, T.F.Eck, I.Slutsker, N.Abuhassan, W.W.Newcomb, J.Schafer, B.Chatenet, F.Lavenue, Y.J.Kaufman, J.Vande Castle, A.Setzer, B.Markham, D.Clark, R.Frouin, R.Halthore, A.Karnieli, N.T.O'Neill, C.Pietras, R.T.Pinker, K.Voss, and G.Zibordi, (2001). An emerging ground-based aerosol climatology: Aerosol Optical Depth from AERONET, J. Geophys. Res., 106, 12 067-12 097.

Kocifaj, M. (2007). Light-pollution model for cloudy and cloudless night skies with ground-based light sources. Applied Optics, vol. 46, no 15, p. 3013-3022.

Vermote, E. F., & Vermeulen, A. (1999). Atmospheric correction algorithm: Spectral reflectances (MOD09), ATBD version 4.0.

Walker, M. F. (1977). The effects of urban lighting on the brightness of the night sky. Publications of the Astronomical Society of the Pacific, vol. 89, p. 405-409.

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