تقرير
Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting
| العنوان: | Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting |
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| المؤلفون: | Asan, Busra, Akgül, Abdullah, Unal, Alper, Kandemir, Melih, Unal, Gozde |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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. Comment: Accepted as a workshop paper at "ICLR 2024 Tackling Climate Change with Machine Learning" |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2403.16612 |
| رقم الأكسشن: | edsarx.2403.16612 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |