284 citations · 806 across the 87 of their papers we have counts for
15 papers · 1 filter
Quantile-based causal inference for spatio-temporal processes: Assessing the impacts of wildfires on US air quality
Zipei Geng, Jordan Richards, Raphael Huser +1
Wildfires pose an increasingly severe threat to air quality, yet quantifying their causal impact remains challenging due to unmeasured meteorological and geographic confounders. Mo…
Online stochastic generators using Slepian bases for regional bivariate wind speed ensembles from ERA5
Yan Song, Zubair Khalid, Marc G. Genton
Reanalysis data, such as ERA5, provide a comprehensive and detailed representation of the Earth's system by assimilating observations into climate models. While crucial for climate…
Efficient stochastic generators with spherical harmonic transformation for high-resolution global climate simulations from CESM2-LENS2
Yan Song, Zubair Khalid, Marc G. Genton
Earth system models (ESMs) are fundamental for understanding Earth's complex climate system. However, the computational demands and storage requirements of ESM simulations limit th…
Sensitivity Analysis of Wind Energy Resources with Bayesian non-Gaussian and nonstationary Functional ANOVA
Jiachen Zhang, Paola Crippa, Marc G. Genton +1
The transition from non-renewable to renewable energies represents a global societal challenge, and developing a sustainable energy portfolio is an especially daunting task for dev…
Forecasting High-Frequency Spatio-Temporal Wind Power with Dimensionally Reduced Echo State Networks
Huang Huang, Stefano Castruccio, Marc G. Genton
Fast and accurate hourly forecasts of wind speed and power are crucial in quantifying and planning the energy budget in the electric grid. Modeling wind at a high resolution brings…
Assessing the Reliability of Wind Power Operations under a Changing Climate with a Non-Gaussian Bias Correction
Jiachen Zhang, Paola Crippa, Marc G. Genton +1
Facing increasing societal and economic pressure, many countries have established strategies to develop renewable energy portfolios, whose penetration in the market can alleviate t…
Improving Bayesian Local Spatial Models in Large Data Sets
Amanda Lenzi, Stefano Castruccio, Haavard Rue +1
Environmental processes resolved at a sufficiently small scale in space and time will inevitably display non-stationary behavior. Such processes are both challenging to model and c…
Bayesian Modeling of Air Pollution Extremes Using Nested Multivariate Max-Stable Processes
Sabrina Vettori, Raphaël Huser, Marc G. Genton
Capturing the potentially strong dependence among the peak concentrations of multiple air pollutants across a spatial region is crucial for assessing the related public health risk…
A Stochastic Generator of Global Monthly Wind Energy with Tukey -and- Autoregressive Processes
Jaehong Jeong, Yuan Yan, Stefano Castruccio +1
Quantifying the uncertainty of wind energy potential from climate models is a very time-consuming task and requires a considerable amount of computational resources. A statistical…
Reducing Storage of Global Wind Ensembles with Stochastic Generators
Jaehong Jeong, Stefano Castruccio, Paola Crippa +1
Wind has the potential to make a significant contribution to future energy resources. Locating the sources of this renewable energy on a global scale is however extremely challengi…
A Copula Model for Non-Gaussian Multivariate Spatial Data
Pavel Krupskii, Marc G. Genton
We propose a new copula model for replicated multivariate spatial data. Unlike classical models that assume multivariate normality of the data, the proposed copula is based on the…
A Multi-Resolution Spatio-Temporal Model for Brain Activation and Connectivity in fMRI Data
Stefano Castruccio, Hernando Ombao, Marc G. Genton
Functional Magnetic Resonance Imaging (fMRI) is a primary modality for studying brain activity. Modeling spatial dependence of imaging data at different scales is one of the main c…
Bayesian linear regression with skew-symmetric error distributions with applications to survival analysis
Francisco J. Rubio, Marc G. Genton
We study Bayesian linear regression models with skew-symmetric scale mixtures of normal error distributions. These kinds of models can be used to capture departures from the usual…
Factor Copula Models for Replicated Spatial Data
Pavel Krupskii, Raphael Huser, Marc G. Genton
We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common fac…
Incorporating geostrophic wind information for improved space-time short-term wind speed forecasting
Xinxin Zhu, Kenneth P. Bowman, Marc G. Genton
Accurate short-term wind speed forecasting is needed for the rapid development and efficient operation of wind energy resources. This is, however, a very challenging problem. Altho…