Efficient Stochastic Generators with Spherical Harmonic Transformation for High-Resolution Global Climate Simulations from CESM2-LENS2
From MaRDI portal
Publication:6651353
DOI10.1080/01621459.2024.2360666MaRDI QIDQ6651353
Yan Song, Zubair Khalid, Marc G. Genton
Publication date: 10 December 2024
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Cites Work
- Gaussian Process Learning via Fisher Scoring of Vecchia's Approximation
- Global space-time models for climate ensembles
- Fast dimension-reduced climate model calibration and the effect of data aggregation
- Spatial variation of total column ozone on a global scale
- Nonstationary covariance models for global data
- A modeling approach for large spatial datasets
- Principles for statistical inference on big spatio-temporal data from climate models
- Reducing storage of global wind ensembles with stochastic generators
- Spherical process models for global spatial statistics
- Optimal transport for applied mathematicians. Calculus of variations, PDEs, and modeling
- Bayesian calibration of computer models. (With discussion)
- Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
- Fixed Rank Kriging for Very Large Spatial Data Sets
- Gaussian Predictive Process Models for Large Spatial Data Sets
- Deterministic Nonperiodic Flow
- Compression and Conditional Emulation of Climate Model Output
- Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach
- A Stochastic Generator of Global Monthly Wind Energy with Tukey g-and-h Autoregressive Processes
- On the Concept of Depth for Functional Data
- Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets
- Stochastic Processes on a Sphere
- Saving storage in climate ensembles: a model-based stochastic approach
- Covariance–Based Rational Approximations of Fractional SPDEs for Computationally Efficient Bayesian Inference
- Non-Gaussian autoregressive processes with Tukey \(g\)-and-\(h\) transformations
- A high-resolution bilevel skew-\(t\) stochastic generator for assessing Saudi Arabia's wind energy resources
- An evolutionary spectrum approach to incorporate large-scale geographical descriptors on global processes
This page was built for publication: Efficient Stochastic Generators with Spherical Harmonic Transformation for High-Resolution Global Climate Simulations from CESM2-LENS2
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6651353)