Physically Explainable Deep Learning for Convective Initiation
  Nowcasting Using GOES-16 Satellite Observations
Da Fan Steven J. Greybush David John Gagne II Eugene E. Clothiaux
Convection initiation (CI) nowcasting remains a challenging problem for both
numerical weather prediction models and existing nowcasting algorithms. In this
study, object-based probabilistic deep learning models are developed to predict
CI based on multichannel infrared GOES-R satellite observations. The data come
from patches surrounding potential CI events identified in Multi-Radar
Multi-Sensor Doppler weather radar products over the Great Plains region from
June and July 2020 and June 2021. An objective radar-based approach is used to
identify these events. The deep learning models significantly outperform the
classical logistic model at lead times up to 1 hour, especially on the false
alarm ratio. Through case studies, the deep learning model exhibits the
dependence on the characteristics of clouds and moisture at multiple levels.
Model explanation further reveals the model's decision-making process with
different baselines. The explanation results highlight the importance of
moisture and cloud features at different levels depending on the choice of
baseline. Our study demonstrates the advantage of using different baselines in
further understanding model behavior and gaining scientific insights.