Resumen
There are several methods to forecast precipitation, but none of them is accurate enough since predicting precipitation is very complicated and influenced by many factors. Data assimilation systems (DAS) aim to increase the prediction result by processing data from different sources in a general way, such as a weighted average, but have not been used for precipitation prediction until now. A DAS that makes use of mathematical tools is complex and hard to carry out. In our paper, machine learning techniques are introduced into a precipitation data assimilation system. After summarizing the theoretical construction of this method, we take some practical weather forecasting experiments and the results show that the new system is effective and promising.