Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting

arXiv:2403.16612v1 Announce Type: new
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 …

abstract arxiv bayesian big change climate climate change confidence cs.cv cs.lg extreme heat forecasting heat impact predictions small type unet world