Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data.

ABSTRACT: Nowcasting of precipitation is a difficult spatiotemporal task because of the non-uniform characterization of meteorological structures over time. Recently, convolutional LSTM has been shown to be successful in solving various complex spatiotemporal based problems. In this research, we propose a novel precipitation nowcasting architecture 'Convcast' to predict various short-term precipitation events using satellite data. We train Convcast with ten consecutive NASA's IMERG precipitation data sets each at intervals of 30 minutes. We use the trained neural network model to predict the eleventh precipitation data of the corresponding ten precipitation sequence. Subsequently, the predicted precipitation data are used iteratively for precipitation nowcasting of up to 150 minutes lead time. Convc

SUBMITTER: Kumar A 

PROVIDER: S-EPMC7065808 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature