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Using Deep Learning To Nowcast The Spatial Coverage of Convection From Himawari-8 Satellite Data


Predicting the timing and location of thunderstorms (“convection”) allows for preventive actions that can save both lives and property. We have applied U-nets, a deep-learning-based type of neural network, to forecast convection on a grid at lead times up to 120 minutes. The goal is to make skillful forecasts with only present and past satellite data as predictors. Specifically, predictors are multispectral brightness-temperature images from the Himawari-8 satellite, while targets (ground truth) are provided by weather radars in Taiwan. U-nets are becoming popular in atmospheric science due to their advantages for gridded prediction. Furthermore, we use three novel approaches to advance U-nets in atmospheric science. First, we compare three architectures – vanilla, temporal, and U-net++ – and find that vanilla U-nets are best for this task. Second, we train U-nets with the fractions skill score, which is spatially aware, as the loss function. Third, because we do not have adequate ground truth over the full Himawari-8 domain, we train the U-nets with small radar-centered patches, then apply trained U-nets to the full domain. Also, we find that the best predictions are given by U-nets trained with satellite data from multiple lag times, not only the present. We evaluate U-nets in detail – by time of day, month, and geographic location – and compare to persistence models. The U-nets outperform persistence at lead times ≥ 60 minutes, and at all lead times the U-nets provide a more realistic climatology than persistence. Our code is available publicly.

Article / Publication Data
Available Metadata
Early Online Release
September 27, 2021
Fiscal Year
Peer Reviewed
Publication Name
Monthly Weather Review
Published On
November 19, 2021
Publisher Name
American Meteorological Society
Print Volume
Submitted On
April 28, 2021


Authors who have authored or contributed to this publication.

  • Ryan Lagerquist - lead Gsl
    Under Contract to NOAA/Global Systems Laboratory
    325 Broadway, Boulder, Colorado
  • Jebb Q. Stewart - second Gsl
  • Christina E. Kumler - fourth Gsl
    Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder
    NOAA/Global Systems Laboratory