510 citations · 3.3k across the 69 of their papers we have counts for
75 papers
TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting
Ioannis Prapas, Nikolaos Ioannis Bountos, Spyros Kondylatos +3
Wildfires are increasingly exacerbated as a result of climate change, necessitating advanced proactive measures for effective mitigation. It is important to forecast wildfires week…
Graphs in State-Space Models for Granger Causality in Climate Science
Víctor Elvira, Émilie Chouzenoux, Jordi Cerdà +1
Granger causality (GC) is often considered not an actual form of causality. Still, it is arguably the most widely used method to assess the predictability of a time series from ano…
Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean
Spyros Kondylatos, Ioannis Prapas, Gustau Camps-Valls +1
We introduce Mesogeos, a large-scale multi-purpose dataset for wildfire modeling in the Mediterranean. Mesogeos integrates variables representing wildfire drivers (meteorology, veg…
Understanding cirrus clouds using explainable machine learning
Kai Jeggle, David Neubauer, Gustau Camps-Valls +1
Cirrus clouds are key modulators of Earth's climate. Their dependencies on meteorological and aerosol conditions are among the largest uncertainties in global climate models. This…
Discovering Causal Relations and Equations from Data
Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad +7
Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the p…
The Kernelized Taylor Diagram
Kristoffer Wickstrøm, J. Emmanuel Johnson, Sigurd Løkse +4
This paper presents the kernelized Taylor diagram, a graphical framework for visualizing similarities between data populations. The kernelized Taylor diagram builds on the widely u…