Abstract
This paper describes a new approach to the analysis of weather radar data for short-range rainfall forecasting based on a neural network model. This approach consists in extracting synthetic information from radar images using the approximation capabilities of multilayer neural networks. Each image in a sequence is approximated using a modified radial basis function network trained by a competitive mechanism. Prediction of the rain field evolution is performed by analysing and extrapolating the time series of weight values. This method has been compared to the conventional cross-correlation technique and the persistence method for three different rainfall events, showing significant improvement in 30 and 60 min ahead forecast accuracy.
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Battan LJ. Radar Observation of the Atmosphere. University of Chicago Press, 1973
Clift GA. Use of radar in meteorology. Technical Report 181, World Meteorological Organization, 1985
Einfalt T, Denœux T, Jacquet G. A radar rainfall forecasting method designed for hydrological purposes. J Hydrology 1990; 114: 229–244
Denœux T, Einfalt T, Jacquet G. Determination in real time of the reliability of radar rainfall forecasts. J Hydrology 1991; 122: 353–371
Austin GL, Bellon A. The use of digital weather radar records for short-term precipitation forecasting. Quart J Roy Meteorological Soc 1974; 100: 658–664
Neumann A. Introduction d'outils de l'Intelligence Artificielle dans la prévision de pluie par radar (In French). PhD thesis, Ecole Nationale des Ponts et Chaussées, Paris, 1991
Ding X, Denœux T, Helloco F. Tracking rain cells in radar images using multilayer neural networks. In S Gielen and B Kappen, editors, Proc ICANN'93, pp 962–967. Springer-Verlag, London, 1993
French MN, Krajewski WF, Cuykendall RR. Rainfall forecasting in space and time using a neural network. J Hydrology 1992; 137: 1–31
Lee S. Supervised learning with gaussian potentials. In B Kosko, editor, Neural Networks for Signal Processing, Prentice-Hall, Englewood Cliffs, NJ, 1992 pp 189–227
Platt JC. Learning by combining memorization and gradient descent. In RP Lippman, JE Moody, DS Touretzky, editors, Neural Information Processing 3, Morgan Kaufmann, San Mateo, CA, 1991, pp. 714–720
Silva FM, Almeida LB. Speeding up back-propagation. In R. Eckmiller, editor, Advanced Neural Computers, North-Holland, Amsterdam, 1990, 151–158
Lopez REet al. Population characteristics, development processes and structure of radar echoes in south Florida. Monthly Weather Rev 1984; 112: 56–75
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Université de Technologie de Compiègne, U.R.A. C.N.R.S., 817 Heudiasyc, BP 649, F-60206, Compiègne cedex, France
T. Denœux
Lyonnaise des Eaux Dumez (LIAC), France
T. Denœux
RHEA SA, Nanterre, France
P. Rizand
- T. Denœux
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- P. Rizand
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Denœux, T., Rizand, P. Analysis of radar images for rainfall forecasting using neural networks.Neural Comput & Applic3, 50–61 (1995). https://doi.org/10.1007/BF01414176
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