校准用于次季节预报的贝叶斯 UNet++
Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting
AI总结：
本文针对次季节温度预报，校准基于 UNet++ 的贝叶斯架构，以较小预测误差代价换取更低校准误差和更可靠、锐利的预报。
摘要：
在检测由气候变化引起的极端高温和低温时，季节预报是一项至关重要的任务。由于一年中气温的小幅上升就会对世界产生巨大影响，因此对预测的置信度应当可靠。神经网络校准提供了一种确保我们对预测具有置信度的方法。然而，校准回归模型是一个研究不足的课题，尤其是在预报器中。我们校准了一种基于 UNet++ 的架构，该架构已被证明在温度异常方面优于基于物理的模型。我们表明，通过在预测误差与校准误差之间进行轻微权衡，可以获得更可靠且更锐利的预报。我们认为，校准应成为天气预报器等安全关键型机器学习应用的重要组成部分。
英文摘要：
Seasonal forecasting is a crucial task when it comes to detecting the extreme heat and colds that occur due to climate change. Confidence in the predictions should be reliable since a small increase in the temperatures in a year has a big impact on the world. Calibration of the neural networks provides a way to ensure our confidence in the predictions. However, calibrating regression models is an under-researched topic, especially in forecasters. We calibrate a UNet++ based architecture, which was shown to outperform physics-based models in temperature anomalies. We show that with a slight trade-off between prediction error and calibration error, it is possible to get more reliable and sharper forecasts. We believe that calibration should be an important part of safety-critical machine learning applications such as weather forecasters.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
