LVP conditions at Mohamed V airport, Morocco: Local characteristics and prediction using neural networks


  • Driss BARI Direction de la Météorologie Nationale/ CNRM
  • Mohamed EL KHLIFI Université Hassan II de Casablanca/FST de Mohammedia





Low Visibility Procedure, Ceiling, Multi-Layer Perceptron, Neural Network, Resilient backpropagation.


Low visibility and/or ceiling conditions have a strong impact on airports' traffic and their prediction is still a challenge for meteorologists. In this paper, the local characteristics of Low Visibility Procedure (LVP) conditions are investigated and the artificial neural network (ANN) based on resilient backpropagation as supervised learning algorithm is used to predict such meteorological conditions at Mohamed V international airport, Casablanca, Morocco. This article aims to assess the ANN ability to provide accurate prediction of such events using the meteorological parameters from the Automated Weather Observation Station (AWOS) over the period from January 2009 to March 2015. First, LVP conditions were classified according to their classes (fog LVP and no fog LVP) and their sources (Runway Visual Range -RVR LVP-, Ceiling -HCB LVP- or both) for both runway end points (35R and 17L). It is found that most of LVP conditions are associated with fog and are often due to decreasing of RVR below 600m. Next, Eleven ANNs were developed to produce LVP prediction for consecutive hourly valid forecast times covering the night and early morning. The Multi-Layer Perceptron (MLP) architecture with one hidden layer is used in this study. Results show that ANNs are able to well predict the LVP conditions and are robust to errors in input parameters for a relative error below 10%. Furthermore, it is found that the ANN's skill is less sensitive to LVP type being predicted.


[1] Bergot, T., "Quality assessment of the Cobel-Isba numerical forecast system of fog and low clouds". Pure and Applied Geophysics, Vol. 164, No. 6-7, (2007), 1265-1282.

[2] ICAO, "European guidance material on All weather operations at aerodromes", European and North Atlantic office, Fourth edition (2012). "available online:"

[3] WMO, "International Meteorological Vocabulary", WMO: Geneva, Switzerland, (1992).

[4] M.W. Gardner, S.R. Dorling, "Artificial neural networks (the multilayer perceptron)- A review of applications in the atmospheric sciences", Atmospheric environment, Vol. 32, (1998), 2627-2636.

[5] W.W. Hsieh, B. Tang, "Applying neural network models to prediction and data analysis in meteorology and oceanography", Bulletin of the American Meteorological Society, Vol. 79, (1998), 1855-1870.

[6] A. Pasini, V. Pelino, S. Potesta, "A neural network model for visibility nowcasting from surface observations: Results and sensitivity to physical input variables", Journal of Geophysical Research, Vol. 106, (2001), 14951-14959.

[7] D. Fabian, R. De Dear, S. Lellyeit, "Application of artificial neural network forecasts to predict fog at Canberra international airport", Weather and Forecasting, Vol. 22, (2006), 372-381.

[8] J.B. Bremnes, S.C. Michaelides, "Probabilistic visibility forecasting using neural networks", Pure and Applied Geo- physics, Vol. 164, (2007), 1365-1381.

[9] C. Marzban, S.M Leyton, B. Colman, "Ceiling and visibility forecasting via neural networks", Weather and Forecasting, Vol. 22, (2006), 466-479.

[10] F. Gunther, S. Fritsch, "Neuralnet : Training of Neural Networks", The R Journal, Vol. 2, (2010), 30-38.

[11] G. Zhang, B. Eddy Patuwo, M.Y. Hu, "Forecasting with artificial neural networks : the state of the art", International Journal of Forecasting, Vol. 14, (1998), 35-62.

[12] D.E. Rumelhart, J.L. Mc Clellend, "Parallel distributed Processing : Explorations in the microstructure of cognition", MIT Press, Cambridge, (1986), 318-362.

[13] A.D. Anastasiadis, G.D. Magoulas, M.N. Vrahatis, "New globally convergent training scheme based on the resilient propagation algorithm", Neurocomputing, Vol. 64, (2005), 253-270.

[14] S. Knerr, L. Personnaz, G. Dreyfus, "Single-layer learning revisited: a stepwise procedure for building and training a neural network". In Neurocomputing. Springer Berlin Heidelberg. (1990), 41-50.

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

[16] D.B. Reusch, R.B. Alley. "Automatic weather stations and artificial neural networks: Improving the instrumental record in West Antarctica". Mon. Wea. Rev., Vol. 130, (2002), 3037-3053.

[17] D.S. Wilks, "Statistical Methods in the Atmospheric Sciences". Academic Press, (1995), 467pp.

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