Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting
This work addresses the need for reliable confidence in predictions for safety-critical applications like weather forecasting, though it is incremental as it applies calibration to an existing model.
The paper tackled the problem of calibrating regression models for sub-seasonal temperature forecasting, showing that calibrating a Bayesian UNet++ architecture yields more reliable and sharper forecasts with a slight trade-off between prediction and calibration errors.
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.