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Convection initiation (CI) nowcasting remains a challenging problem for bothnumerical weather prediction models and existing nowcasting algorithms. In thisstudy, object-based probabilistic deep learning models are developed to predictCI based on multichannel infrared GOES-R satellite observations. The data comefrom patches surrounding potential CI events identified in Multi-RadarMulti-Sensor Doppler weather radar products over the Great Plains region fromJune and July 2020 and June 2021. An objective radar-based approach is used toidentify these events. The deep learning models significantly outperform theclassical logistic model at lead times up to 1 hour, especially on the falsealarm ratio. Through case studies, the deep learning model exhibits thedependence on the characteristics of clouds and moisture at multiple levels.Model explanation further reveals the model's decision-making process withdifferent baselines. The explanation results highlight the importance ofmoisture and cloud features at different levels depending on the choice ofbaseline. Our study demonstrates the advantage of using different baselines infurther understanding model behavior and gaining scientific insights.

Physically Explainable Deep Learning for Convective Initiation Nowcasting Using GOES-16 Satellite Observations