Application of Artificial Neural Network Forecasts to Predict Fog at Canberra International Airport

Dustin Fabbian Department of Physical Geography, Macquarie University, North Ryde, New South Wales, Australia

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Richard de Dear Department of Physical Geography, Macquarie University, North Ryde, New South Wales, Australia

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Stephen Lellyett New South Wales Regional Office, Australian Bureau of Meteorology, Darlinghurst, New South Wales, Australia

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Abstract

The occurrence of fog can significantly impact air transport operations, and plays an important role in aviation safety. The economic value of aviation forecasts for Sydney Airport alone in 1993 was estimated at $6.8 million (Australian dollars) for Quantas Airways. The prediction of fog remains difficult despite improvements in numerical weather prediction guidance and models of the fog phenomenon. This paper assesses the ability of artificial neural networks (ANNs) to provide accurate forecasts of such events at Canberra International Airport (YSCB). Unlike conventional statistical techniques, ANNs are well suited to problems involving complex nonlinear interactions and therefore have potential in application to fog prediction. A 44-yr database of standard meteorological observations obtained from the Australian Bureau of Meteorology was used to develop, train, test, and validate ANNs designed to predict fog occurrence. Fog forecasting aids were developed for 3-, 6-, 12-, and 18-h lead times from 0600 local standard time. The forecasting skill of various ANN architectures was assessed through analysis of relative operating characteristic curves. Results indicate that ANNs are able to offer good discrimination ability at all four lead times. The results were robust to error perturbation for various input parameters. It is recommended that such models be included when preparing forecasts for YSCB, and that the technique should be extended in its application to cover other similarly fog-prone aviation locations.

Corresponding author address: Richard de Dear, Dept. of Physical Geography, Division of Environmental and Life Sciences, Macquarie University, North Ryde, NSW 1209, Australia. Email: rdedear@laurel.ocs.mq.edu.au

Abstract

The occurrence of fog can significantly impact air transport operations, and plays an important role in aviation safety. The economic value of aviation forecasts for Sydney Airport alone in 1993 was estimated at $6.8 million (Australian dollars) for Quantas Airways. The prediction of fog remains difficult despite improvements in numerical weather prediction guidance and models of the fog phenomenon. This paper assesses the ability of artificial neural networks (ANNs) to provide accurate forecasts of such events at Canberra International Airport (YSCB). Unlike conventional statistical techniques, ANNs are well suited to problems involving complex nonlinear interactions and therefore have potential in application to fog prediction. A 44-yr database of standard meteorological observations obtained from the Australian Bureau of Meteorology was used to develop, train, test, and validate ANNs designed to predict fog occurrence. Fog forecasting aids were developed for 3-, 6-, 12-, and 18-h lead times from 0600 local standard time. The forecasting skill of various ANN architectures was assessed through analysis of relative operating characteristic curves. Results indicate that ANNs are able to offer good discrimination ability at all four lead times. The results were robust to error perturbation for various input parameters. It is recommended that such models be included when preparing forecasts for YSCB, and that the technique should be extended in its application to cover other similarly fog-prone aviation locations.

Corresponding author address: Richard de Dear, Dept. of Physical Geography, Division of Environmental and Life Sciences, Macquarie University, North Ryde, NSW 1209, Australia. Email: rdedear@laurel.ocs.mq.edu.au

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  • Bonné, J., cited. 2005: Airlines still struggle with paths to profit. [Available online at http://www.msnbc.msn.com/id/3679292/.].

  • Demuth, H., and Beale M. , 2001: Neural Network Toolbox: For Use with MATLAB. 4th ed. The MathWorks, 844 pp.

  • Edwards, P. J., and Murray A. F. , 2000: A study of early stopping and model selection applied to the papermaking industry. Int. J. Neural Syst., 10 , 918.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gardner, M. W., and Dorling S. R. , 1998: Artificial neural networks (the multilayer perceptron)—A review of applications in the atmospheric sciences. Atmos. Environ., 32 , 26272636.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hagan, M. T., and Menhaj M. B. , 1994: Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Networks, 5 , 989993.

  • Hall, T., Brooks H. E. , and Doswell C. A. III, 1999: Precipitation forecasting using a neural network. Wea. Forecasting, 14 , 338345.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hewitson, B. C., and Crane R. G. , 1994: Neural Nets: Applications in Geography. GeoJournal Library, Vol. 29, Kluwer Academic, 194 pp.

  • Hsieh, W. W., and Tang B. , 1998: Applying neural network models to prediction and data analysis in meteorology and oceanography. Bull. Amer. Meteor. Soc., 79 , 18551871.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, X., Mills G. , Weymouth G. , Potts R. , and Keenan T. , 2002: Using high-resolution LAPS model to predict fog. Extended Abstracts, 14th Modelling Workshop on Modelling and Predicting Extreme Weather Events, BMRC Research Rep. 90, Melbourne, Australia, Bureau of Meteorology, 25–28.

  • Leigh, R. J., 1995: Economic benefits of Terminal Aerodrome Forecasts (TAFs) for Sydney Airport, Australia. Meteor. Appl., 2 , 239247.

    • Search Google Scholar
    • Export Citation
  • Leipper, D. F., 1995: Fog forecasting objectively in the Californian coastal area using LIBS. Wea. Forecasting, 10 , 741762.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lippmann, R. P., 1987: An introduction to computing with neural nets. IEEE Acoustics, Speech Signal Process. Mag., 4 , 422.

  • Malley, S., Miao Y. , Davis C. , and Forrest A. , 2003: A study of fog statistics and forecasting aids at Canberra Airport. Meteorological Note 219, Australian Bureau of Meteorology, 15 pp.

  • Marzban, C., 2004: The ROC curve and the area under it as a performance measure. Wea. Forecasting, 19 , 11061114.

  • Marzban, C., and Stumpf G. J. , 1996: A neural network for tornado prediction based on Doppler radar–derived attributes. J. Appl. Meteor., 35 , 617626.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matthews, C., Lawrence S. , Jardine R. , and Webb C. , 2002: Fog forecasting at Sydney Airport. Australian Bureau of Meteorology, 6 pp.

  • Mehrotra, K., Mohan C. K. , and Ranka S. , 1997: Elements of Artificial Neural Networks. The MIT Press, 334 pp.

  • Murphy, A. H., 1994: Assessing the economic value of weather forecasts: An overview of methods, results and issues. Meteor. Appl., 1 , 6973.

    • Search Google Scholar
    • Export Citation
  • Oke, T. R., 1987: Boundary Layer Climates. 2d ed. Muthen and Co., 435 pp.

  • Parikh, C. R., Pont M. J. , and Jones N. B. , 1999: Improving the performance of multi-layer perceptrons where limited training data are available for some classes. Proc. Ninth Int. Conf. on Artificial Neural Networks, Vol. 1, Edinburgh, United Kingdom, Institution of Electrical Engineers, 227–232.

    • Crossref
    • Export Citation
  • Pasini, A., Pelino V. , and Potestà S. , 2001: A neural network model for visibility nowcasting from surface observations: Results and sensitivity to physical input variables. J. Geophys. Res., 106 , 1495114959.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Puri, K., Dietachmayer G. S. , Mills G. A. , Davidson N. E. , Bowen R. A. , and Logan L. W. , 1998: The new BMRC Limited Area Prediction System, LAPS. Aust. Meteor. Mag., 47 , 203223.

    • Search Google Scholar
    • Export Citation
  • Regano, L., 1997: A fully automated forecasting aid for fog in Australia. Meteorological Note 212, Australian Bureau of Meteorology, 8 pp.

  • Reusch, D. B., and Alley R. B. , 2002: Automatic weather stations and artificial neural networks: Improving the instrumental record in West Antarctica. Mon. Wea. Rev., 130 , 30373053.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rummelhart, D. E., and McCelland J. L. , 1986: Parallel Distributed Processing. Vol. 1, The MIT Press, 517 pp.

  • Spining, M. T., Darsey J. A. , Sumpter B. G. , and Noid D. W. , 1994: Opening up the black box of artificial neural networks. J. Chem. Educ., 71 , 406411.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sturman, A., and Tapper N. , 1996: The Weather and Climate of Australia and New Zealand. Oxford University Press, 476 pp.

  • Tasadduq, I., Rehman S. , and Bubshait K. , 2002: Application of neural networks for the prediction of hourly mean surface temperature in Saudi Arabia. Renewable Energy, 25 , 545554.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tresp, V., Neuneier R. , and Ahmad S. , 1995: Efficient methods for dealing with missing data in supervised learning. Advances in Neural Information Processing Systems 7, G. Tesauro, D. S. Touretzky, and T. K. Leen, Eds., The MIT Press, 689–696.

    • Search Google Scholar
    • Export Citation
  • Turner, D. B., 1964: A diffusion model for an urban area. J. Appl. Meteor., 3 , 8391.

  • U.S. Environmental Protection Agency, 2000: Meteorological monitoring guidance for regulatory modelling applications. EPA-454/R-99-005, U.S. EPA, 171 pp.

  • Vamplew, P. W., Clark D. , Adams A. , and Muench J. P. , 1996: Techniques for dealing with missing values in feedforward networks. Proc. Seventh Australian Conf. on Neural Networks, Canberra, ACT, Australia, University of Tasmania, 250–254.

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