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MLAM: Multi-Layer Attention Module for Radar Extrapolation Based on Spatiotemporal Sequence Neural Network.

Shengchun Wang¹, Tianyang Wang¹, Sihong Wang¹

¹College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China.

Sensors (Basel, Switzerland)

|October 14, 2023

Summary

This study introduces a multi-layer attention module (MLAM) to improve precipitation nowcasting using convolutional recurrent neural networks (ConvRNNs). The enhanced ConvRNNs better predict high-intensity echoes and long-term dependencies for more accurate radar extrapolation.

Keywords:

attention mechanism convolutional recurrent neural networks precipitation nowcasting radar echo extrapolation

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Area of Science:

Meteorology
Artificial Intelligence
Computer Vision

Background:

Precipitation nowcasting commonly uses radar echo extrapolation.
Convolutional recurrent neural networks (ConvRNNs) show promise but struggle with predicting high-intensity echoes and long-term feature dependencies.

Purpose of the Study:

To address limitations in ConvRNNs for radar echo extrapolation.
To improve the accuracy and temporal consistency of precipitation nowcasting.

Main Methods:

An embedded multi-layer attention module (MLAM) was developed.
MLAM enhances attention to critical echo regions and long-term spatiotemporal features.
Experiments were conducted on the HKO-7 and HMB radar datasets.

Main Results:

ConvRNNs integrated with MLAM demonstrated superior performance compared to standard ConvRNNs.
The proposed method showed improved prediction of echoes with varying intensities.
Enhanced ability to capture long-term feature dependencies was observed.

Conclusions:

The embedded multi-layer attention module (MLAM) effectively enhances ConvRNNs for precipitation nowcasting.
MLAM integration leads to more accurate and reliable radar echo extrapolation.
This approach offers a significant advancement in meteorological forecasting technology.