Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting

• URL: http://arxiv.org/abs/2403.16612v2
• Date: Thu, 4 Apr 2024 12:35:33 GMT
• Title: Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting
• Authors: Busra Asan, Abdullah Akgül, Alper Unal, Melih Kandemir, Gozde Unal,
• Abstract summary: 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. We show that with a slight trade-off between prediction error and calibration error, it is possible to get more reliable and sharper forecasts.
• Score: 10.412055701639682
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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