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A method of radar echo extrapolation based on dilated convolution and attention convolution.

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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.

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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.