Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.16612 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917630048206848 |
|---|---|
| author | Asan, Busra Akgül, Abdullah Unal, Alper Kandemir, Melih Unal, Gozde |
| author_facet | Asan, Busra Akgül, Abdullah Unal, Alper Kandemir, Melih Unal, Gozde |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16612 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting Asan, Busra Akgül, Abdullah Unal, Alper Kandemir, Melih Unal, Gozde Machine Learning Computer Vision and Pattern Recognition 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. |
| title | Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.16612 |