53 citations · 188 across the 20 of their papers we have counts for
21 papers
Surface temperature extremes produced by huge machine learning hindcasts of summer 2023
Mark Risser, Ankur Mahesh, William D. Collins +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…
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes
Marcus M. Noack, Hengrui Luo, Mark D. Risser
The Gaussian process (GP) is a popular statistical technique for stochastic function approximation and uncertainty quantification from data. GPs have been adopted into the realm of…
Explaining the unexplainable: leveraging extremal dependence to characterize the 2021 Pacific Northwest heatwave
Likun Zhang, Mark D. Risser, Michael F. Wehner +1
In late June, 2021, a devastating heatwave affected the US Pacific Northwest and western Canada, breaking numerous all-time temperature records by large margins and directly causin…
A flexible class of priors for orthonormal matrices with basis function-specific structure
Joshua S. North, Mark D. Risser, F. Jay Breidt
Statistical modeling of high-dimensional matrix-valued data motivates the use of a low-rank representation that simultaneously summarizes key characteristics of the data and enable…
Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels
Marcus M. Noack, Harinarayan Krishnan, Mark D. Risser +1
A Gaussian Process (GP) is a prominent mathematical framework for stochastic function approximation in science and engineering applications. This success is largely attributed to t…
Nonstationary Bayesian modeling for a large data set of derived surface temperature return values
Mark Risser
Heat waves resulting from prolonged extreme temperatures pose a significant risk to human health globally. Given the limitations of observations of extreme temperature, climate mod…
The effect of geographic sampling on evaluation of extreme precipitation in high resolution climate models
Mark D. Risser, Michael F. Wehner
Traditional approaches for comparing global climate models and observational data products typically fail to account for the geographic location of the underlying weather station d…
Bayesian inference for high-dimensional nonstationary Gaussian processes
Mark D. Risser, Daniel Turek
In spite of the diverse literature on nonstationary spatial modeling and approximate Gaussian process (GP) methods, there are no general approaches for conducting fully Bayesian in…
Detected changes in precipitation extremes at their native scales derived from in situ measurements
Mark D. Risser, Christopher J. Paciorek, Travis A. O'Brien +2
The gridding of daily accumulated precipitation -- especially extremes -- from ground-based station observations is problematic due to the fractal nature of precipitation, and ther…
A probabilistic gridded product for daily precipitation extremes over the United States
Mark D. Risser, Christopher J. Paciorek, Michael F. Wehner +2
Gridded data products, for example interpolated daily measurements of precipitation from weather stations, are commonly used as a convenient substitute for direct observations beca…
Spatially-Dependent Multiple Testing Under Model Misspecification, with Application to Detection of Anthropogenic Influence on Extreme Climate Events
Mark D. Risser, Christopher J. Paciorek, Daithi Stone
The Weather Risk Attribution Forecast (WRAF) is a forecasting tool that uses output from global climate models to make simultaneous attribution statements about whether and how gre…
Review: Nonstationary Spatial Modeling, with Emphasis on Process Convolution and Covariate-Driven Approaches
Mark D. Risser
In many environmental applications involving spatially-referenced data, limitations on the number and locations of observations motivate the need for practical and efficient models…
Nonstationary Spatial Prediction of Soil Organic Carbon: Implications for Stock Assessment Decision Making
Mark D. Risser, Catherine A. Calder, Veronica J. Berrocal +1
The Rapid Carbon Assessment (RaCA) project was conducted by the US Department of Agriculture's National Resources Conservation Service between 2010-2012 in order to provide contemp…
Quantifying the effect of interannual ocean variability on the attribution of extreme climate events to human influence
Mark D. Risser, Daithi A. Stone, Christopher J. Paciorek +2
In recent years, the climate change research community has become highly interested in describing the anthropogenic influence on extreme weather events, commonly termed "event attr…
Local likelihood estimation for covariance functions with spatially-varying parameters: the convoSPAT package for R
Mark D. Risser, Catherine A. Calder
In spite of the interest in and appeal of convolution-based approaches for nonstationary spatial modeling, off-the-shelf software for model fitting does not as of yet exist. Convol…
Regression-based covariance functions for nonstationary spatial modeling
Mark D. Risser, Catherine A. Calder
In many environmental applications involving spatially-referenced data, limitations on the number and locations of observations motivate the need for practical and efficient models…