Compression of climate simulations with a nonstationary global spatiotemporal SPDE model

DOI10.1214/20-AOAS1340zbMath1446.62139MaRDI QIDQ2194444

Stefano Castruccio, Geir-Arne Fuglstad

Publication date: 26 August 2020

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aoas/1593449315

zbMATH Keywords

nonstationaryspace-time modelclimate modelglobal modelstochastic partial differential equation (SPDE)

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Computational methods for problems pertaining to statistics (62-08) Applications of statistics to environmental and related topics (62P12) Stochastic partial differential equations (aspects of stochastic analysis) (60H15) Statistical aspects of big data and data science (62R07) Climate science and climate modeling (86A08)

Related Items (2)

Improving Bayesian Local Spatial Models in Large Datasets ⋮ Efficient parameter estimation for parabolic SPDEs based on a log-linear model for realized volatilities

Uses Software

• GMRFLib

Cites Work

• An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
• Global space-time models for climate ensembles
• Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping
• Reducing storage of global wind ensembles with stochastic generators
• Exploring a New Class of Non-stationary Spatial Gaussian Random Fields with Varying Local Anisotropy
• MARKOV RANDOM FIELDS AND STOCHASTIC PARTIAL DIFFERENTIAL EQUATIONS
• Gaussian Markov Random Fields
• Compression and Conditional Emulation of Climate Model Output
• A Stochastic Generator of Global Monthly Wind Energy with Tukey g-and-h Autoregressive Processes
• Constructing Priors that Penalize the Complexity of Gaussian Random Fields
• Approximate Likelihood for Large Irregularly Spaced Spatial Data
• Statistical Methods for Regular Monitoring Data
• ON STATIONARY PROCESSES IN THE PLANE

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