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.

翻译：季节性预报对于检测由气候变化引发的极端高温和低温事件至关重要。由于年度气温的微小升高便会对全球产生重大影响，因此预测结果的置信度必须可靠。神经网络校准为确保预测置信度提供了有效途径。然而，回归模型的校准仍是一个研究不足的课题，尤其是在预报领域。我们对基于UNet++的架构进行了校准，该架构在温度异常预测中已被证明优于基于物理的模型。研究表明，在预测误差与校准误差之间进行适度权衡后，可以获得更可靠且更尖锐的预报结果。我们认为校准应成为天气预测等安全关键型机器学习应用的重要组成部分。