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A method of radar echo extrapolation based on dilated convolution and attention convolution
Xiajiong Shen1,2, Kunying Meng1, Lei Zhang3,4
1School of Computer and Information Engineering, Henan University, Kaifeng, Henan, China.
Scientific Reports
|June 22, 2022
Summary
This study introduces ADC_Net, a novel neural network model for radar echo extrapolation. By using dilated and attention convolutions, it enhances extrapolation accuracy and better utilizes radar echo information.
Area of Science:
- Meteorology
- Artificial Intelligence
- Computer Vision
Background:
- Traditional methods for radar echo extrapolation have limitations in accuracy.
- Neural network approaches show promise but require further refinement for optimal radar echo extrapolation.
Purpose of the Study:
- To propose an improved radar echo extrapolation model, ADC_Net, enhancing accuracy and information utilization.
- To address the limitations of existing neural network applications in radar echo extrapolation.
Main Methods:
- Developed ADC_Net, a model integrating dilated convolution for downsampling and attention convolution for feature enhancement.
- Utilized dilated convolution to preserve feature matrix data structure and extract multi-scale spatial features.
- Incorporated attention convolution to boost sensitivity to target features and reduce interference.
Main Results:
- The ADC_Net model demonstrated effective improvement in radar echo extrapolation accuracy.
- Evaluated performance using extrapolated images and key indices (POD, CSI, FAR, HSS) over a 90-minute forecast period.
Conclusions:
- ADC_Net significantly enhances the accuracy of radar echo extrapolation compared to traditional methods.
- The model effectively improves the utilization of radar echo information for more precise forecasting.
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