Local prediction of precipitation based on neural network

Abstract

The paper is focused on analysis of local neural network model of precipitation. We use basic multilayer perceptron neural network with the time-window on input data to predict the precipitation. We predict the precipitation in the next day from the local meteorological data from past days. Data from the past 60 years were used to train the predictor. Obtained prediction model is specific for given area of Košice City in Slovakia, as the prediction is based on the statistics of the weather in given area. This precipitation predictor is multiple-input-single-output architecture with a single value per day resolution on output. Obtained results show that good local temperature prediction accuracy is possible with chosen setup, but it is worse for the precipitation prediction. Also the training requirements of precipitation predictor seem to be significantly higher then for the temperature predictor. Obtained prediction results can be used for applications based on local meteorological station data, although they are not as accurate as the state of art agency predictions based on satellite data. In the paper we will analyze design of the precipitation predictor based on existing design of the temperature predictor and provide the reader with recommended setup of such predictor for application with his/her local precipitation data.