7 papers
Surface temperature extremes produced by huge machine learning hindcasts of summer 2023
Mark Risser, Ankur Mahesh, Joshua North +6
The summer of 2023 brought record-breaking heat extremes across the globe. In this work, we investigate how much worse these extremes might have become, given identical large-scale…
gp2Scale: A Class of Compactly Supported Non-Stationary Kernels and Distributed Computing for Exact Gaussian Processes on 10 Million Data Points
Marcus M. Noack, Mark D. Risser, Hengrui Luo +2
Despite a large corpus of recent work on scaling up Gaussian processes, a stubborn trade-off between computational speed, prediction and uncertainty quantification accuracy, and cu…
Spatial scale-aware tail dependence modeling for high-dimensional spatial extremes
Muyang Shi, Likun Zhang, Mark D. Risser +1
Extreme events over large spatial domains may exhibit highly heterogeneous tail dependence characteristics, yet most existing spatial extremes models yield only one dependence clas…
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data
Mark D. Risser, Marcus M. Noack, Hengrui Luo +1
The Gaussian process (GP) is a widely used probabilistic machine learning method with implicit uncertainty characterization for stochastic function approximation, stochastic modeli…
Granger causal inference for climate change attribution
Mark D. Risser, Mohammed Ombadi, Michael F. Wehner
Climate change detection and attribution (D&A) is concerned with determining the extent to which anthropogenic activities have influenced specific aspects of the global climate sys…
Data-driven upper bounds and event attribution for unprecedented heatwaves
Mark D. Risser, Likun Zhang, Michael F. Wehner
The last decade has seen numerous record-shattering heatwaves in all corners of the globe. In the aftermath of these devastating events, there is interest in identifying worst-case…