Abstract
Short-time heavy rainfall is a kind of sudden strong and heavy precipitation weather, which seriously threatens people’s life and property safety. Accurate precipitation nowcasting is of great significance for the government to make disaster prevention and mitigation decisions in time. In order to make high-resolution forecasts of regional rainfall, this paper proposes a convolutional 3D GRU (Conv3D-GRU) model to predict the future rainfall intensity over a relatively short period of time from the machine learning perspective. Firstly, the spatial features of radar echo maps with different heights are extracted by 3D convolution, and then, the radar echo maps on time series are coded and decoded by using GRU. Finally, the trained model is used to predict the radar echo maps in the next 1-2 hours. The experimental results show that the algorithm can effectively extract the temporal and spatial features of radar echo maps, reduce the error between the predicted value and the real value of rainfall, and improve the accuracy of short-term rainfall prediction.
Highlights
Short-term heavy precipitation is a kind of weather process with sudden heavy rainfall, short precipitation time, and large precipitation
When evaluated on the radar echo dataset that our Conv3D-Gated recurrent unit (GRU) model consistently outperforms both the Conv2D and the Conv2D-GRU, which can effectively improve the accuracy of short-term rainfall prediction
GRU has been proved to be able to deal with the long-range dependence problem well, the input of the network is a one-dimensional vector, and the radar echo maps used in this paper are three-dimensional images
Summary
Short-term heavy precipitation is a kind of weather process with sudden heavy rainfall, short precipitation time, and large precipitation. Rough the recognition and analysis of radar echoes maps, the single body centroid-based method can obtain the features of thunderstorm cells, such as the thunderstorm center, the thunderstorm volume, and the weight center of reflectivity factor and extrapolate features of these thunderstorm movements to make convective near prediction [2,3,4,5,6]. Machine learning-based method uses its self-learning ability to obtain some hidden features of echo changes and shows good memory and association ability [19, 20] It has been applied as classification model and numerical prediction model in weather forecast, showing the potential and broad prospects of applying neural network model to radar echo extrapolation [21, 22]. When evaluated on the radar echo dataset that our Conv3D-GRU model consistently outperforms both the Conv2D and the Conv2D-GRU, which can effectively improve the accuracy of short-term rainfall prediction
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