Strong convective systems and the associated heavy rainfall events can trig-ger floods and landslides with severe detrimental consequences.These events have a high spatio-temporal variability,being difficult to predic...Strong convective systems and the associated heavy rainfall events can trig-ger floods and landslides with severe detrimental consequences.These events have a high spatio-temporal variability,being difficult to predict by standard meteorological numerical models.This work proposes the M5Images method for performing the very short-term prediction(nowcasting)of heavy convective rainfall using weather radar data by means of a convolutional recurrent neural network.The recurrent part of it is a Long Short-Term Memory(LSTM)neural network.Prediction tests were performed for the city and surroundings of Campinas,located in the Southeastern Brazil.The convolutional recurrent neural network was trained using time series of rainfall rate images derived from weather radar data for a selected set of heavy rainfall events.The attained pre-diction performance was better than that given by the persistence forecasting method for different prediction times.展开更多
文摘Strong convective systems and the associated heavy rainfall events can trig-ger floods and landslides with severe detrimental consequences.These events have a high spatio-temporal variability,being difficult to predict by standard meteorological numerical models.This work proposes the M5Images method for performing the very short-term prediction(nowcasting)of heavy convective rainfall using weather radar data by means of a convolutional recurrent neural network.The recurrent part of it is a Long Short-Term Memory(LSTM)neural network.Prediction tests were performed for the city and surroundings of Campinas,located in the Southeastern Brazil.The convolutional recurrent neural network was trained using time series of rainfall rate images derived from weather radar data for a selected set of heavy rainfall events.The attained pre-diction performance was better than that given by the persistence forecasting method for different prediction times.
