An Objective Method to Modify Numerical Model Forecasts with Newly Given Weather Data Using an Artificial Neural Network

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Abstract
An objective method of forecasting precipitation coverage with a neural network is presented. This method uses as predictors all available data at local weather stations including both numerical model results and weather data obtained later than the model initial time, which sometimes contradict each other and hence have to be handled subjectively by well-experienced forecasters. Since the method gives an objective and also realistic forecast of areal precipitation coverage, its skill scores are better than those of the persistence forecast (after 3 h), the linear regression forecasts, and numerical model precipitation prediction.

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