Locate family members with the last name KLEINDESSNER.

MAT,,HAEUS KLEINDESSNER is an academic who published a paper in 2021 entitled Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and. MAT,,HAEUS KLEINDESSNER published this work as part of a team: Huang, Yiyi. Here is a description of this work: The Arctic sea ice has retreated rapidly in the past few decades, which is believed to be driven by various dynamic and thermodynamic processes in the atmosphere. The newly open water resulted from sea ice decline in turn exerts large influence on the atmosphere. Therefore, this study aims to investigate the causality between multiple atmospheric processes and sea ice variations using three distinct data-driven causality approaches that have been proposed recently: Temporal Causality Discovery Framework Noncombinatorial Optimization via Trace Exponential and Augmented lagrangian for Structure learning (NOTEARS) and Directed Acyclic Graph-Graph Neural Networks (DAG-GNN). We apply these three algorithms to 39 years of historical time-series data sets, which include 11 atmospheric variables from ERA-5 reanalysis product and passive microwave satellite ret.

MAT,,H¨AUS KLEINDESSNER is an academic who published a paper in 2020 entitled Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and. MAT,,H¨AUS KLEINDESSNER published this work as part of a team: Huang, Yiyi. Here is a description of this work: The Arctic sea ice has retreated rapidly in the past few decades, which is believed to be driven by various dynamic and thermodynamic processes in the atmosphere. The newly open water resulted from sea ice decline in turn exerts large influence on the atmosphere. Therefore, this study aims to investigate the causality between multiple atmospheric processes and sea ice variations using three distinct data-driven causality approaches: TCDF, NOTEARS and DAGGNN. We find that the static graphs produced by NOTEARS and DAG-GNN are relatively reasonable. The results from NOTEARS indicate that relative humidity and precipitation dominate sea ice changes among all variables, while the results from DAG-GNN suggest that the horizontal wind fields are more important for driving sea ice variations. However, both of them produce some unrealistic edges. In comparison,.

Matthäus Kleindessner is an academic who published a paper in 2021-08-24 entitled Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere. The paper was published in a journal entitled Frontiers in Big Data.