Benedikt Knüsel

TL;DR: It is shown that in climate research, intermediate categories exist between classical domain science and big data, and that big-data elements have also been applied without the possibility of repeated evaluation.

TL;DR: In this article, an approach to model urban temperature using the quantile regression forest algorithm and CWS, open government and remote sensing data is presented, which can accurately map urban heat at high spatial and temporal resolution without additional measurement infrastructure.

TL;DR: A framework for understanding uncertainty of observational datasets is presented and four different types of dataset ensembles are identified as tools to understand and assess uncertainties arising from the use of datasets for a specific purpose to allow for a more reliable uncertainty assessment in the context of model evaluation.

TL;DR: It is shown that data-driven models can be subject to substantial second-order uncertainty, i.e., uncertainty in the assessment of the predictive uncertainty, because they are often applied to ill-understood problems.

TL;DR: A framework to assess the fitness of a climate model for providing understanding is developed based on three dimensions: representational accuracy, representational depth, and graspability, and it is shown that this framework does justice to the intuition that classical process-based climate models give understanding of phenomena.