3 papers
stat.AP2026
Probabilistic Deep Learning for Drought Forecasting: Role of Internal Climate Variability
Henri Funk, Cornelia Gruber, Göran Kauermann +2
Predicting drought risk is essential for anticipating impacts on water resources, agriculture, ecosystems, and climate adaptation planning. Yet drought forecasts remain uncertain b…
stat.AP2026
Capturing Aleatoric Uncertainty in Climate Models
Cornelia Gruber, Henri Funk, Magdalena Mittermeier +2
Internal climate variability arises from the climate system's inherently chaotic dynamics. Quantifying it is essential for climate science, as it enables risk-based decision-making…
stat.AP2025
Deriving Duration Time from Occupancy Data -- A case study in the length of stay in Intensive Care Units for COVID-19 patients
Martje Rave, Göran Kauermann
This paper focuses on drawing information on underlying processes, which are not directly observed in the data. In particular, we work with data in which only the total count of un…