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Related Experiment Video

Updated: Jan 19, 2026

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MLC-LSTM: Exploiting the Spatiotemporal Correlation between Multi-Level Weather Radar Echoes for Echo Sequence

Jinrui Jing1, Qian Li2, Xuan Peng3

  • 1College of Meteorology and Oceanography, National University of Defense Technology, 60 Shuanglong Road, Nanjing 211101, China. jingjinrui18@nudt.edu.cn.

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|September 22, 2019
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Summary
This summary is machine-generated.

This study introduces a new deep learning model for weather radar echo extrapolation, improving short-term weather forecasting accuracy and detail. The model enhances prediction by considering weather system evolution and adversarial training for sharper, realistic echo forecasts.

Keywords:
adversarial trainingdeep learningevolutionlong short-term memoryradar echo extrapolationspatiotemporal correlationweather forecastingweather radar sensor

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Area of Science:

  • Meteorology
  • Artificial Intelligence
  • Data Science

Background:

  • Weather radar echo extrapolation is crucial for short-term weather forecasting and disaster warnings.
  • Existing deep learning methods struggle with modeling weather system evolution and produce blurry predictions.

Purpose of the Study:

  • To address limitations in current deep learning weather radar echo extrapolation methods.
  • To improve the accuracy and realism of extrapolated weather echoes.

Main Methods:

  • Proposed a Multi-Level Correlation Long Short-Term Memory (MLC-LSTM) model.
  • Integrated adversarial training to enhance echo sharpness and realism.
  • Utilized a real-life multi-level weather radar echoes dataset for training and testing.

Main Results:

  • The MLC-LSTM model accurately forecasts echo motion and evolution.
  • Predicted echoes are realistic and fine-grained, overcoming blurriness.
  • Achieved superior performance on key metrics (POD, FAR, CSI, HSS) compared to state-of-the-art methods.

Conclusions:

  • The proposed MLC-LSTM with adversarial training effectively improves weather radar echo extrapolation.
  • The model shows significant potential for application in operational weather forecasting practices.