2 citations · 2 across the 5 of their papers we have counts for
5 papers
Uncertainty-Aware End-to-End AI Weather Forecasting: Disentangling Observation and Model Contributions
Rodrigo Almeida, Noelia Otero, Jost Arndt +3
End-to-end weather forecasting systems produce skillful global gridded and station forecasts directly from raw Earth observations, replacing the numerical weather prediction pipeli…
MADE: A Living Benchmark for Multi-Label Text Classification with Uncertainty Quantification of Medical Device Adverse Events
Raunak Agarwal, Markus Wenzel, Simon Baur +3
Machine learning in high-stakes domains such as healthcare requires not only strong predictive performance but also reliable uncertainty quantification (UQ) to support human oversi…
On the Predictive Skill of Artificial Intelligence-based Weather Models for Extreme Events using Uncertainty Quantification
Rodrigo Almeida, Noelia Otero, Miguel-Ángel Fernández-Torres +1
Accurate prediction of extreme weather events remains a major challenge for artificial intelligence-based weather prediction systems. While deterministic models such as FuXi, Graph…
Spatially Resolved Meteorological and Ancillary Data in Central Europe for Rainfall Streamflow Modeling
Marc Aurel Vischer, Noelia Otero, Jackie Ma
We present a dataset for rainfall streamflow modeling that is fully spatially resolved with the aim of taking neural network-driven hydrological modeling beyond lumped catchments.…
DiffScale: Continuous Downscaling and Bias Correction of Subseasonal Wind Speed Forecasts using Diffusion Models
Maximilian Springenberg, Noelia Otero, Yuxin Xue +1
Renewable resources are strongly dependent on local and large-scale weather situations. Skillful subseasonal to seasonal (S2S) forecasts -- beyond two weeks and up to two months --…