Automated Segmentation of tropical cyclone clouds in geostationary infrared images

Joshua May, Liang Hu, Elizabeth A. Ritchie, Mehrtash Harandi, J. Scott Tyo

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

We demonstrate that a convolutional neural network (CNN) based on the U-Net architecture can be used to create a cloud mask data set that accurately identifies the clouds associated with tropical cyclones (TCs). The CNN can be trained using a single year of cloud masks produced by an earlier first-principles algorithm, and the results are insensitive to the specific year of training data used. These masks were originally created in order to compute the upwelling radiation due to TC clouds, and we show that the predicted masks result in both pixel areas and radiation calculations that are nearly identical to those computed using the earlier masks.

Original languageEnglish
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume21
DOIs
Publication statusPublished - 25 Jan 2024

Keywords

  • Clouds
  • Convolutional neural networks
  • Earth
  • Meteorology
  • Satellite Remote Sensing
  • Supervised Segmentation
  • Testing
  • Training
  • Tropical cyclones
  • Tropical Cyclones

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