activity
20182021
most citedEarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task

2 citations · 2 across the 1 of their papers we have counts for

collaborators

5 papers

physics.ao-ph2021

Causal inference for process understanding in Earth sciences

Adam Massmann, Pierre Gentine, Jakob Runge

There is growing interest in the study of causal methods in the Earth sciences. However, most applications have focused on causal discovery, i.e. inferring the causal relationships…

cs.LG20212 cited

EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task

Christian Requena-Mesa, Vitus Benson, Markus Reichstein +2

Satellite images are snapshots of the Earth surface. We propose to forecast them. We frame Earth surface forecasting as the task of predicting satellite imagery conditioned on futu…

stat.ME2020

High-recall causal discovery for autocorrelated time series with latent confounders

Andreas Gerhardus, Jakob Runge

We present a new method for linear and nonlinear, lagged and contemporaneous constraint-based causal discovery from observational time series in the presence of latent confounders.…

stat.ME2020

Reconstructing regime-dependent causal relationships from observational time series

Elena Saggioro, Jana de Wiljes, Marlene Kretschmer +1

Inferring causal relations from observational time series data is a key problem across science and engineering whenever experimental interventions are infeasible or unethical. Incr…

physics.space-ph2018

Common solar wind drivers behind magnetic storm-magnetospheric substorm dependency

Jakob Runge, Georgios Balasis, Ioannis A. Daglis +2

The dynamical relationship between magnetic storms and magnetospheric substorms presents one of the most controversial problems of contemporary geospace research. Here, we tackle t…