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Experimental Astronomy

, Volume 36, Issue 1–2, pp 407–424 | Cite as

Weather forecasting and dynamic scheduling for a modern cm/mm wave radiotelescope

  • F. T. Nasir
  • C. Castiglia
  • F. Buffa
  • G. L. Deiana
  • A. Delitala
  • A. Tarchi
Original Article

Abstract

In order to maximize the productivity of a state of the art scientific instrument, concurrent projects that will be performed by the instrument itself should be scheduled and prioritized appropriately. In the case of radioastronomical facilities, the precedences are set on the basis of many parameters amongst which meteo-climatic variables play a fundamental role. The Sardinia Radio Telescope (SRT) is nearly completed, and soon it will start performing radioastronomical observations in the 0.3–100 GHz frequency range. It is known that centimeter and especially millimeter wavelengths are affected by the atmospheric water vapor content and weather conditions. In order to increase the performances of observations at higher frequencies, detailed knowledge of the climatology of the telescope site is needed, moreover, accurate weather predictions that range several days ahead are particularly useful. Such information is mandatory for the implementation of a dynamic scheduling system through which the observations are scheduled according to the oncoming weather conditions. In this paper we compare the forecasts produced by a numerical weather prediction (NWP) model, provided by the local weather service, to the measurements conducted at the SRT site with ground-based instruments. The comparisons are used to validate the NWPs in order to understand if they could be used for scheduling radioastronomical observations.Our analysis, clearly indicates that the model predictions are representative for the radiotelescope site. Indeed, the forecasts of water vapor and brightness temperatures, made up to 36 hours ahead, are consistent with the on-site measurements; wind forecasts are also correlated to the ground level measurements. In the score analysis we have deducted that the NWP model, on average, is consistent with the measurements more than 65 % of the time, implying that the majority of the radioastronomical observations would be performed in weather conditions which are appropriate to the frequency that is being used. By using the available NWP model and the on-site instrumentation, it is feasible to implement a dynamic scheduling system for the SRT.

Keywords

Radiotelescope Dynamic scheduling system Water vapor Opacity Forecast Numerical weather prediction model 

Notes

Acknowledgments

This project has been supported by the regional Sardinian government law L.R. 7–2007. We would like to thank Alex Kraus and Ron Maddalena for useful discussions.

References

  1. 1.
    Acme Systems: Home page. http://www.acmesystems.it/ (2011). Accessed 1 Apr 2011
  2. 2.
    ARPAS: Agenzia Regionale per L’ambiente della Sardegna. http://www.sardegnaambiente.it/index.php. Accessed 19 Jul 2011
  3. 3.
    Balser, D.S., Bignell, C., Braatz, J., Clark, M., Condon, J., Harnett, J., O’Neil K., Maddalena, R., Marganian, P., McCarty, M., Sessoms, E., Shelton, A.: GBT dynamical scheduling system: algorithm, metrics and simulations. ASP Conf. Ser. 411, 330 (2009)ADSGoogle Scholar
  4. 4.
    Buzzi, A., Fantini, M., Malguzzi, P., Nerozzi, F.: Validation of a limited area model in cases of mediterranean cyclogenesis: surface fields and precipitation scores. Meteor. Atmos. Phys. 53, 137 (1994)CrossRefGoogle Scholar
  5. 5.
    Malguzzi, P., Tartaglione, N.: An economical second order advection scheme for numerical weather prediction. Q. J. R. Meteorol. Soc. 125, 2291 (1999)ADSCrossRefGoogle Scholar
  6. 6.
    Buehler, S.A., Eriksson, P., Kuhn, T., Von Eneln., A., Verdes, C.: ARTS, the atmospheric radiative transfer simultor. J. Quant. Spectrosc. Radiat. Transf. 91(1), 65 (2005)ADSCrossRefGoogle Scholar
  7. 7.
    Condon, J.J., Balser, D.S.: Dynamic scheduling algorithms, metrics, and simulations. GBT Archive: DS005.3 (2010)Google Scholar
  8. 8.
    D’Amico, N.: The Sardinia Radio Telescope and the local context. Mem. Soc. Astron. Ital. 10, 25 (2006)Google Scholar
  9. 9.
    Hartman, J.C.: Engineering Economy and the Decision-Making Process. Prentice Hall, New Jersey (2006)Google Scholar
  10. 10.
    Holton, J.R.: An Introduction to Dynamic Meteorology, 4th edn., pp. 4,38. Elsevier Academic Press, Printed in the United States of America (2005)Google Scholar
  11. 11.
    ECMWF: European Center For Medium-Range Forecasts, home page. http://www.ecmwf.int/. Accessed 22 Jun 2011
  12. 12.
    Nasir, F.T., Buffa, F., Deiana, G.L.: Characterization of the atmosphere above a site for millimeter wave astronomy. Exp. Astron. 29(3), 207 (2011)ADSCrossRefGoogle Scholar
  13. 13.
    Olmi, L., Grueff, G.: SRT: Design and Technical Specifications. Mem. Soc. Astron. Ital. 10, 19 (2006)Google Scholar
  14. 14.
    Orfei, A.: Performance Estimation at 90 GHz (3.3 mm) of the Medicina and Noto Sites. IRA Internal Report, IRA 418/08 (2008)Google Scholar
  15. 15.
    Prandoni, I., Felli, M.: The working group “Science with the SRT”: tasks, activities, and results. Mem. Soc. Astron. Ital. 10, 205 (2006)Google Scholar
  16. 16.
    SardegnaArpa : Dipartimento Specialistico Sardo Idroclimatico. http://www.sar.sardegna.it/documentazione/meteo/modelli.asp. Accessed 22 Jul 2011
  17. 17.
    Radiometrics corporation: MP-3000A Temperature, Humidity and Liquid Profiler. http://www.radiometrics.com/ (2009). Accessed 1 Apr 2011
  18. 18.
    Max-Planck-Institut fur Radioastronomie: Receivers for the Effelsberg 100-m Telescope. http://www.mpifr.de/div/effelsberg/receivers/receiver.html#receivers. Accessed 1 Apr 2011
  19. 19.
    Rodgers, C.D.: Inverse Methods for Atmospheric Sounding: Theory and Practice, pp. 14. World Scientific Publishing Company, London (2000)CrossRefGoogle Scholar
  20. 20.
    Schwartz, R., Kraus, A., Zensus, A.: Evaluation and selection of radio astronomy programs: the case of the 100m Radio Telescope at Effelsberg. Organ. Strateg. Astron. 6, 125 (2006)ADSGoogle Scholar
  21. 21.
    Sensirion, the sensor company: Digital humidity and temperature sensors (RH&T) - overview. http://www.sensirion.com/en/01_humidity_sensors/ (2012). Accessed 6 May 2012
  22. 22.
    The Pool Observation Database System (ODS) and the Data Reduction Pipeline at the IRAM 30m Telescope. http://www.iram.fr/IRAMFR/ARN/may04/node5.html#SECTION00054000000000000000. Accessed 15 Dec 2012
  23. 23.
    Wilson, T.L., Rohlfs, K., Huttemeister, S.: Tools of Radio Astronomy, 5th edn., pp. 180. Astronomy and Astrophysics Library. Springer, Berlin (2009)Google Scholar
  24. 24.
    National Center for Atmospheric Research: Technical Notes. http://nldr.library.ucar.edu/collections/technotes/asset-000-000-000-845.pdf. Accessed 22 Jul 2011
  25. 25.
    Wu, S.: Optimum frequencies of a passive microwave radiometer for tropospheric path-length correction. IEEE Trans. Antennas Propag. 27(2), 233 (1979)ADSCrossRefGoogle Scholar
  26. 26.
    Young Company : Meteorological surface station equipment, products page. http://www.youngusa.com/products/ (2008). Accessed 1 Apr 2011
  27. 27.

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • F. T. Nasir
    • 1
  • C. Castiglia
    • 2
  • F. Buffa
    • 1
  • G. L. Deiana
    • 1
  • A. Delitala
    • 2
  • A. Tarchi
    • 1
  1. 1.Osservatorio Astronomico di CagliariCapoterraItaly
  2. 2.Dipartimento IdroMeteoClimaticoArpasSassariItaly

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