3 citations · 9 across the 6 of their papers we have counts for
7 papers
Skillful Data-Driven Subseasonal Soil Moisture Forecasting: Prospects and Limits for Flash Drought Prediction
Noelia Otero, Atahan Özer, Miguel-Ángel Fernández-Torres +1
Despite substantial progress in short-to-medium-range weather forecasting, predicting high-impact events such as flash droughts remains a key challenge for both early warning opera…
On the Predictive Skill of Artificial Intelligence-based Weather Models for Extreme Events using Uncertainty Quantification
Rodrigo Almeida, Noelia Otero, Miguel-Ángel Fernández-Torres +1
Accurate prediction of extreme weather events remains a major challenge for artificial intelligence-based weather prediction systems. While deterministic models such as FuXi, Graph…
Explainable Earth Surface Forecasting under Extreme Events
Oscar J. Pellicer-Valero, Miguel-Ángel Fernández-Torres, Chaonan Ji +2
With climate change-related extreme events on the rise, high dimensional Earth observation data presents a unique opportunity for forecasting and understanding impacts on ecosystem…
AI for Extreme Event Modeling and Understanding: Methodologies and Challenges
Gustau Camps-Valls, Miguel-Ángel Fernández-Torres, Kai-Hendrik Cohrs +22
In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences. Here, AI improved weather forecasting, model emulation, parameter…
DeepExtremeCubes: Integrating Earth system spatio-temporal data for impact assessment of climate extremes
Chaonan Ji, Tonio Fincke, Vitus Benson +12
With climate extremes' rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show prom…
Hierarchical Representations for Spatio-Temporal Visual Attention Modeling and Understanding
Miguel-Ángel Fernández-Torres
This PhD. Thesis concerns the study and development of hierarchical representations for spatio-temporal visual attention modeling and understanding in video sequences. More specifi…
Deep Learning Methods for Daily Wildfire Danger Forecasting
Ioannis Prapas, Spyros Kondylatos, Ioannis Papoutsis +5
Wildfire forecasting is of paramount importance for disaster risk reduction and environmental sustainability. We approach daily fire danger prediction as a machine learning task, u…