317 citations · 490 across the 13 of their papers we have counts for
13 papers
Assessing socio-economic climate impacts from text data
Mariana Madruga de Brito, Brielen Madureira, Taís Maria Nunes Carvalho +15
Recent advances in natural language processing (NLP) and large language models (LLMs) have enabled the systematic use of large-scale textual data from news, social media, and repor…
Exploring climate change effects on concurrent floods and concurrent droughts via statistical deep learning
C. J. R. Murphy-Barltrop, J. Richards, B. Poschlod +2
Concurrent floods and concurrent droughts in nearby catchments pose challenges to risk assessment and water management. Climate change is affecting extremely high and low discharge…
Omega-blocks with spatially compounding extremes over Europe are highly sensitive to remote atmospheric drivers
Magdalena Mittermeier, Christian M. Grams, Urs Beyerle +5
Omega-blocks can trigger spatially compounding heat-precipitation extremes with severe societal impacts, as seen in September 2023 when a heatwave over France coincided with devast…
Numerical models outperform AI weather forecasts of record-breaking extremes
Zhongwei Zhang, Erich Fischer, Jakob Zscheischler +1
Artificial intelligence (AI)-based models are revolutionizing weather forecasting and have surpassed leading numerical weather prediction systems on various benchmark tasks. Howeve…
Regularization of ML models for Earth systems by using longer model timesteps
Raghul Parthipan, Mohit Anand, Hannah M Christensen +3
Regularization is a technique to improve generalization of machine learning (ML) models. A common form of regularization in the ML literature is to train on data where similar inpu…
Causal machine learning for sustainable agroecosystems
Vasileios Sitokonstantinou, Emiliano Díaz Salas Porras, Jordi Cerdà Bautista +8
In a changing climate, sustainable agriculture is essential for food security and environmental health. However, it is challenging to understand the complex interactions among its…
Validating Deep Learning Weather Forecast Models on Recent High-Impact Extreme Events
Olivier C. Pasche, Jonathan Wider, Zhongwei Zhang +2
The forecast accuracy of machine learning (ML) weather prediction models is improving rapidly, leading many to speak of a "second revolution in weather forecasting". With numerous…
Insights into the drivers and spatio-temporal trends of extreme Mediterranean wildfires with statistical deep-learning
Jordan Richards, Raphaël Huser, Emanuele Bevacqua +1
Extreme wildfires are a significant cause of human death and biodiversity destruction within countries that encompass the Mediterranean Basin. Recent worrying trends in wildfire ac…
Modelling and simulating spatial extremes by combining extreme value theory with generative adversarial networks
Younes Boulaguiem, Jakob Zscheischler, Edoardo Vignotto +2
Modelling dependencies between climate extremes is important for climate risk assessment, for instance when allocating emergency management funds. In statistics, multivariate extre…
Have precipitation extremes and annual totals been increasing in the world's dry regions over the last 60 years?
Sebastian Sippel, Jakob Zscheischler, Martin Heimann +5
Daily rainfall extremes and annual totals have increased in large parts of the global land area over the last decades. These observations are consistent with theoretical considerat…
Distinguishing cause from effect using observational data: methods and benchmarks
Joris M. Mooij, Jonas Peters, Dominik Janzing +2
The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide w…
Inferring deterministic causal relations
Povilas Daniusis, Dominik Janzing, Joris Mooij +4
We consider two variables that are related to each other by an invertible function. While it has previously been shown that the dependence structure of the noise can provide hints…
Testing whether linear equations are causal: A free probability theory approach
Jakob Zscheischler, Dominik Janzing, Kun Zhang
We propose a method that infers whether linear relations between two high-dimensional variables X and Y are due to a causal influence from X to Y or from Y to X. The earlier propos…