Search Results for author:
Found 2 papers, 0 papers with code
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.
Climate Model Driven Seasonal Forecasting Approach with Deep Learning
Three different setups (CMIP6; CMIP6 + elevation; CMIP6 + elevation + ERA5 finetuning) were used with both UNet and UNet++ algorithms resulting in six different models.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
