Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting
<|reference_start|>Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting: 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.<|reference_end|>
@article{asan2024calibrating,
    title={Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting},
    author={Busra Asan, Abdullah Akg"ul, Alper Unal, Melih Kandemir, Gozde Unal},
    journal={arXiv preprint arXiv:2403.16612},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16612},
    primaryClass={cs.LG cs.CV}
}