Recommended citation: Gamboa-Villafruela, C.J.;Fernández-Alvarez, J.C.; Márquez-Mijares, M.; Pérez-Alarcón, A.; Batista-Leyva, A.J. Convolutional LSTM Architecture for Precipitation Nowcasting Using Satellite Data. Environ. Sci. Proc., 8, 33. https://doi.org/10.3390/ecas2021-10340

Abstact

The short-term prediction of precipitation is a difficult spatio-temporal task due to the non-uniform characterization of meteorological structures over time. Currently, neural networks such as convolutional LSTM have shown ability for the spatio-temporal prediction of complex problems. In this research, we propose an LSTM convolutional neural network (CNN-LSTM) architecture for immediate prediction of various short-term precipitation events using satellite data. The CNN-LSTM is trained with NASA Global Precipitation Measurement (GPM) precipitation data sets, each at 30-min intervals. The trained neural network model is used to predict the sixteenth precipitation data of the corresponding fifteen precipitation sequence and up to a time interval of 180 min. The results show that the increase in the number of layers, as well as in the amount of data in the training data set, improves the quality of the forecast.