Artificial neural networks (ANN) like the multi-layer perceptron provide powerful solutions for these problems, but have been only marginally used in the operational weather centers. The novelty of the approach partly explains this situation. Several reasons can be found in some difficulties associated to the ANN approach as well. Among others, we may mention the remote link of a trained ANN with the physical laws, the difficulty of gathering an adequate (i.e. representative and correctly sampled) training dataset for high-dimension problem, the difficulty of having both the direct model and its derivatives right, or the difficulty of modifying the ANN once it has been trained.
The presentation will discuss the current strengths and limitations of ANN in the context of numerical weather prediction. It will be illustrated by several applications, including those developed by the author at the European Centre for Medium-range Weather Forecasts in the field of radiation modeling.