用后期融合解决深度学习模型在长期气候预测中的不确定性问题

全球变暖导致极端气候的频率和强度增加，造成巨大的生命和财产损失。准确的远期气候预测可以为此类极端事件的准备和灾害风险管理提供更多时间。尽管机器学习方法在长程气候预测中表现出了很好的效果，但相关的模型不确定性可能会降低其可靠性。为了解决这个问题，我们提出了一种晚期融合方法，系统地结合来自多个模型的预测，以减少融合结果的预期误差。我们还提出了一种带有新颖的去规范化层的网络结构，以获得数据规范化的好处，而无需实际规范化数据。长距离2米温度预报的实验结果表明，该框架的性能优于30年气候常模，而且可以通过增加模型的数量来提高精度。

原文题目：Addressing Deep Learning Model Uncertainty in Long-Range Climate Forecasting with Late Fusion

原文：Global warming leads to the increase in frequency and intensity of climate extremes that cause tremendous loss of lives and property. Accurate long-range climate prediction allows more time for preparation and disaster risk management for such extreme events. Although machine learning approaches have shown promising results in long-range climate forecasting, the associated model uncertainties may reduce their reliability. To address this issue, we propose a late fusion approach that systematically combines the predictions from multiple models to reduce the expected errors of the fused results. We also propose a network architecture with the novel denormalization layer to gain the benefits of data normalization without actually normalizing the data. The experimental results on long-range 2m temperature forecasting show that the framework outperforms the 30-year climate normals, and the accuracy can be improved by increasing the number of models.