论文标题
带有贝叶斯神经网络的地球物理模型
Ensembling geophysical models with Bayesian Neural Networks
论文作者
论文摘要
地球物理模型的合奏提高了投影准确性并表达不确定性。我们开发了一种新型的数据驱动的结合策略，用于使用贝叶斯神经网络组合地球物理模型，该策略在观察结果中占据了空间瞬间变化的模型权重和偏差。这会产生更准确和不确定性感知的预测，而无需牺牲可解释性。应用于从15种化学气候模型的合奏中预测总臭氧的总预测，我们发现贝叶斯神经网络合奏（Baynne）的表现优于现有的结合方法，可减少49.4％的时间额定功能，而降低了67.4％的降低，而对于RMSE而言，与RMSE相比，相比之下。不确定性也是良好的，我们的外推验证数据集中有90.6％的数据点位于2个标准偏差范围内，而在3个标准偏差内的数据点98.5％。
Ensembles of geophysical models improve projection accuracy and express uncertainties. We develop a novel data-driven ensembling strategy for combining geophysical models using Bayesian Neural Networks, which infers spatiotemporally varying model weights and bias while accounting for heteroscedastic uncertainties in the observations. This produces more accurate and uncertainty-aware projections without sacrificing interpretability. Applied to the prediction of total column ozone from an ensemble of 15 chemistry-climate models, we find that the Bayesian neural network ensemble (BayNNE) outperforms existing ensembling methods, achieving a 49.4% reduction in RMSE for temporal extrapolation, and a 67.4% reduction in RMSE for polar data voids, compared to a weighted mean. Uncertainty is also well-characterized, with 90.6% of the data points in our extrapolation validation dataset lying within 2 standard deviations and 98.5% within 3 standard deviations.
