标题： 贝叶斯深度学习用于对流触发的短期预报不确定性估计 显示英文标题

标题： Bayesian Deep Learning for Convective Initiation Nowcasting Uncertainty Estimation

摘要： 本研究评估了五种最近提出的贝叶斯深度学习方法相对于确定性残差神经网络（ResNet）基线在0-1小时对流触发（CI）预报中的概率和不确定性预测，使用的是GOES-16卫星红外观测数据。 不确定性是通过概率预测的校准程度以及不确定性区分大误差和小误差预测的能力来评估的。 大多数贝叶斯深度学习方法产生的概率预测优于确定性ResNet，其中一种方法，初始权重集合+蒙特卡洛（MC）丢弃，即一个具有不同初始权重开始训练的确定性ResNet集合，并在推理过程中激活丢弃，产生了最有技能且校准最好的预测。 初始权重集合+MC丢弃通过生成多个解决方案，更全面地采样假设空间而受益。 贝叶斯ResNet集合是唯一在较长预报时间表现比确定性ResNet差的集合，这可能是由于优化更多参数的挑战。 为了解决这个问题，采用了贝叶斯-MOPED（使用深度神经网络的经验贝叶斯模型先验）ResNet集合，并通过将假设搜索限制在确定性ResNet假设附近提高了预报技能。 所有贝叶斯方法都表现出良好的不确定性校准，并有效地区分了大误差和小误差的情况。 在案例研究中，初始权重集合+MC丢弃在晴空区域选定的CI事件中表现出比贝叶斯-MOPED集合和确定性ResNet更好的预报技能。 然而，与确定性ResNet和贝叶斯-MOPED集合相比，初始权重集合+MC丢弃在没有CI发生的晴空和积云区域表现出较差的泛化能力。 显示英文摘要

摘要： This study evaluated the probability and uncertainty forecasts of five recently proposed Bayesian deep learning methods relative to a deterministic residual neural network (ResNet) baseline for 0-1 h convective initiation (CI) nowcasting using GOES-16 satellite infrared observations. Uncertainty was assessed by how well probabilistic forecasts were calibrated and how well uncertainty separated forecasts with large and small errors. Most of the Bayesian deep learning methods produced probabilistic forecasts that outperformed the deterministic ResNet, with one, the initial-weights ensemble + Monte Carlo (MC) dropout, an ensemble of deterministic ResNets with different initial weights to start training and dropout activated during inference, producing the most skillful and well-calibrated forecasts. The initial-weights ensemble + MC dropout benefited from generating multiple solutions that more thoroughly sampled the hypothesis space. The Bayesian ResNet ensemble was the only one that performed worse than the deterministic ResNet at longer lead times, likely due to the challenge of optimizing a larger number of parameters. To address this issue, the Bayesian-MOPED (MOdel Priors with Empirical Bayes using Deep neural network) ResNet ensemble was adopted, and it enhanced forecast skill by constraining the hypothesis search near the deterministic ResNet hypothesis. All Bayesian methods demonstrated well-calibrated uncertainty and effectively separated cases with large and small errors. In case studies, the initial-weights ensemble + MC dropout demonstrated better forecast skill than the Bayesian-MOPED ensemble and the deterministic ResNet on selected CI events in clear-sky regions. However, the initial-weights ensemble + MC dropout exhibited poorer generalization in clear-sky and anvil cloud regions without CI occurrence compared to the deterministic ResNet and Bayesian-MOPED ensemble.