On GOES-16 data, a deep ensemble plus MC dropout yields the best calibrated probabilistic convective initiation nowcasts among five Bayesian deep learning methods.
Dotzek, 2008:The Spatial Distribution of Severe Convective Storms and an Analysis of their Secular Changes, 35–53
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Bayesian Deep Learning for Convective Initiation Nowcasting Uncertainty Estimation
On GOES-16 data, a deep ensemble plus MC dropout yields the best calibrated probabilistic convective initiation nowcasts among five Bayesian deep learning methods.