Benedikt Knüsel
Biographic Data
| ID | 5993642 |
|---|---|
| NAME | Benedikt Knüsel |
| GIVEN NAMES | Benedikt |
| FAMILY NAME | Knüsel |
| SIGNATURE | KNÜSEL B |
| AFFILIATIONS | ETH Zurich |
| ORCID | 0000-0003-0292-5982 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 8 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 2 |
Understanding and assessing uncertainty of observational climate datasets for model evaluation using ensembles
In climate science, observational gridded climate datasets that are based on in situ measurements serve as evidence for scientific claims and they are used to both calibrate and evaluate models. However, datasets only represent selected aspects of the real world, so when they are used for a specific purpose they can be a source of uncertainty. Here, we present a framework for understanding this uncertainty of observational datasets which distingu…
Understanding climate phenomena with data-driven models
In climate science, climate models are one of the main tools for understanding phenomena. Here, we develop a framework to assess the fitness of a climate model for providing understanding. The framework is based on three dimensions: representational accuracy, representational depth, and graspability. We show that this framework does justice to the intuition that classical process-based climate models give understanding of phenomena. While simple …
Understanding climate phenomena with data-driven models
In climate science, climate models are one of the main tools for understanding phenomena. Here, we develop a framework to assess the fitness of a climate model for providing understanding. The framework is based on three dimensions: representational accuracy, representational depth, and graspability. We show that this framework does justice to the intuition that classical process-based climate models give understanding of phenomena. While simple …
Understanding and assessing uncertainty of observational climate datasets for model evaluation using ensembles
In climate science, observational gridded climate datasets that are based on in situ measurements serve as evidence for scientific claims and they are used to both calibrate and evaluate models. However, datasets only represent selected aspects of the real world, so when they are used for a specific purpose they can be a source of uncertainty. Here, we present a framework for understanding this uncertainty of observational datasets which distingu…
Understanding and assessing uncertainty of observational climate datasets for model evaluation using ensembles
In climate science, observational gridded climate datasets that are based on in situ measurements serve as evidence for scientific claims and they are used to both calibrate and evaluate models. However, datasets only represent selected aspects of the real world, so when they are used for a specific purpose they can be a source of uncertainty. Here, we present a framework for understanding this uncertainty of observational datasets which distingu…
Understanding climate phenomena with data-driven models
In climate science, climate models are one of the main tools for understanding phenomena. Here, we develop a framework to assess the fitness of a climate model for providing understanding. The framework is based on three dimensions: representational accuracy, representational depth, and graspability. We show that this framework does justice to the intuition that classical process-based climate models give understanding of phenomena. While simple …
Artificial Intelligence (2 works) · Climate change (2 works) · Climate model (2 works) · Climate science (2 works) · Computer Science (2 works) · Data science (2 works) · Ecology (2 works) · Machine learning (2 works) · Mathematics (2 works) · Atmospheric and Environmental Gas Dynamics (1 work)