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This document presents a study on using variational recurrent neural networks (RNNs) for probabilistic prediction of cyclone trajectory and intensity with uncertainty quantification. The study utilizes simple RNNs and long short-term memory (LSTM) RNNs with variational inference to jointly predict tropical cyclone location (latitude and longitude) and wind intensity as a time-series prediction problem across four different regions. The results show that variational RNNs provide probabilistic predictions wit

Author

Miqueias Pedro

Language

EN