A Deep-Learning Model for Automated Detection of Intense Midlatitude Convection Using Geostationary Satellite Images

Details

• Journal Title:
  Weather and Forecasting
• Personal Author:
  Cintineo, John L. ; Pavolonis, Michael J. ; Sieglaff, Justin M. ; Wimmers, Anthony ; Brunner, Jason ; Bellon, Willard
• NOAA Program & Office:
  CIMMS (Cooperative Institute for Mesoscale Meteorological Studies) ; NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research)
• Description:
  Intense thunderstorms threaten life and property, impact aviation, and are a challenging forecast problem, particularly without precipitation-sensing radar data. Trained forecasters often look for features in geostationary satellite images such as rapid cloud growth, strong and persistent overshooting tops, U- or V-shaped patterns in storm-top temperature (and associated above-anvil cirrus plumes), thermal couplets, intricate texturing in cloud albedo (e.g., “bubbling” cloud tops), cloud-top divergence, spatial and temporal trends in lightning, and other nuances to identify intense thunderstorms. In this paper, a machine-learning algorithm was employed to automatically learn and extract salient features and patterns in geostationary satellite data for the prediction of intense convection. Namely, a convolutional neural network (CNN) was trained on 0.64-μm reflectance and 10.35-μm brightness temperature from the Advanced Baseline Imager (ABI) and flash-extent density (FED) from the Geostationary Lightning Mapper (GLM) on board GOES-16. Using a training dataset consisting of over 220 000 human-labeled satellite images, the CNN learned pertinent features that are known to be associated with intense convection and skillfully discriminated between intense and ordinary convection. The CNN also learned a more nuanced feature associated with intense convection—strong infrared brightness temperature gradients near cloud edges in the vicinity of the main updraft. A successive-permutation test ranked the most important predictors as follows: 1) ABI 10.35-μm brightness temperature, 2) ABI GLM flash-extent density, and 3) ABI 0.64-μm reflectance. The CNN model can provide forecasters with quantitative information that often foreshadows the occurrence of severe weather, day or night, over the full range of instrument-scan modes.
• Keywords:
  • Deep convection
  • Satellite observations
  • Neural networks
  • Machine learning
• Source:
  Weather and Forecasting, 35(4), 2567-2588
• DOI:
  https://doi.org/10.1175/WAF-D-20-0028.1
• Document Type:
  Journal Article
• Rights Information:
  Other
• Compliance:
  Submitted
• Download URL:
  https://repository.library.noaa.gov/view/noaa/29519/noaa_29519_DS1.pdf
• File Type:
  [PDF - 5.59 MB]
• Collection(s):
  NOAA Cooperative Institutes ; National Environmental Satellite and Data Information Service (NESDIS)
• Main Document Checksum:
  urn:sha-512:b85896203d8722bdb59c270dafc70fd9ec8ef284740e6ab9c98d949a31ce1a2d2579b37b5e51194ddaf8c86bc857565177faa6cf2d9641c3ddd5808ed209cbee