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Using Deep Learning for Severe Weather Detection from Radar Images

This research explores the application of Deep Convolutional Neural Networks (DCNNs) for detecting severe weather events using radar reflectivity images. Supervised by Professors Fuqing Zhang and Jia Li, the study addresses challenges in interpreting human-designed visual features and the reliance on expert experience in severe weather detection. By training a Regional Convolutional Neural Network (RCNN), the framework extracts relevant visual features and identifies regions of interest. The study demonstrates a novel approach for accurately predicting severe weather, with promising results for future classification tasks.

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Using Deep Learning for Severe Weather Detection from Radar Images

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  1. Application of Deep Convolutional Neural Networks in Detecting Severe Weather Events from Radar Reflectivity Images Rachel Under the supervision of Professor Fuqing Zhang and Professor Jia Li May 10, 2019

  2. High-level Visual Signals in Weather Images Hook Echo, Velocity Couplet, The V-Notch, or “Flying Eagle”, The Debris Ball, etc. images credit to: https://www.ustornadoes.com/2013/02/14/understanding-basic-tornadic-radar-signatures/

  3. Motivation 1. Some of the human-designed visual features are hard to interpret. 2. Severe weather detection relies much on experience. 3. Deep learning has the ability to explore the underneath pattern of the images. Solution: Train a Deep Neural Network to detect severe weather events.

  4. Regional Convolutional Neural Network (RCNN)

  5. Part 1: Convolutional Neural Network (CNN) Function: output the visual features of the images. Method: Fix the first 10/24/30 layers of CNN and retrain with radar reflectivity images. Output: feature map

  6. Part 2: Region Proposal Network (RPN) Function: select regions from the region candidates generate from the anchors and image features. Adjust anchor positions. Output: about 2000 positive+negative regions.

  7. Part 3: Region of Interest (ROI) Pooling Function: further select regions of interest on region candidates. Adjust positions of the regions. Output: 128 positive+negative regions. Loss: sum of the cross entropy in RPN and ROI pooling stage.

  8. Dataset 1. The historical radar reflection images from 2008 to 2017. 2. The severe weather records. 3. Model simulations of the year 2017. Targeting regions: 24-50N, 80-106W.

  9. Positive and Negative Region Selection storm score: #(event in the bbox) reflectivity score: average radar reflectivity per pixel Positive regions: 1. Include severe weather events inside. (storm score > 0) 2. For the same event, we label the regions with higher radar reflectivity values. (reflectivity score > thres0) Negative regions: 1. Far from severe weather events. (storm score = 0) 2. Not having very high radar reflectivity (reflectivity score < thres1)

  10. Example Red dot: event Green: Positive Blue: Negative Yellow: Neither

  11. Result: Training

  12. Result: Recall-Precision Curve Left: Fix 24 layers in VGG16 and retrain the next. Right: Fix first 10 layers TITAN Baseline: Recall 0.43, Precision 0.21

  13. Application: Severe Weather Prediction with Simulations

  14. Conclusion We Propose a novel deep learning and computer vision framework to detect the severe weather events. The design of positive and negative regions for training the network automatically uses the large severe weather label dataset. Future work: validate on the simulation dataset, severe weather classification tasks.

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