A dynamic nonstationary spatio-temporal model for short term prediction of precipitation

DOI10.1214/12-AOAS564zbMath1257.62121arXiv1102.4210OpenAlexW3098501491MaRDI QIDQ1939996

Werner A. Stahel, Fabio Sigrist, Hans R. Künsch

Publication date: 5 March 2013

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

Full work available at URL: https://arxiv.org/abs/1102.4210

zbMATH Keywords

censoringMarkov chain Monte Carlo (MCMC)space-time modelhierarchical Bayesian modelGaussian random fieldsrainfall modeling

Mathematics Subject Classification ID

Related Items (10)

A spatio-temporal modeling framework for weather radar image data in tropical southeast Asia ⋮ Interpolation of daily rainfall data using censored Bayesian spatially varying model ⋮ Modeling Nonstationarity in Space and Time ⋮ Section on the special year for Mathematics of Planet Earth (MPE 2013) ⋮ High-frequency analysis of parabolic stochastic PDEs ⋮ Ensemble Kalman Methods for High-Dimensional Hierarchical Dynamic Space-Time Models ⋮ Spatial modeling of rainfall accumulated over short periods of time ⋮ Computationally efficient multivariate spatio-temporal models for high-dimensional count-valued data (with discussion) ⋮ Hierarchical spatially varying coefficient and temporal dynamic process models usingspTDyn ⋮ A stochastic space-time model for intermittent precipitation occurrences

Uses Software

• GMRFLib
• spate

Cites Work

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• An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
• Making and Evaluating Point Forecasts
• Estimation of Relationships for Limited Dependent Variables
• A general science-based framework for dynamical spatio-temporal models
• Bayesian forecasting and dynamic models.
• Uniform asymptotic optimality of linear predictions of a random field using an incorrect second-order structure
• Spatial-temporal mesoscale modeling of rainfall intensity using gage and radar data
• Probabilistic quantitative precipitation field forecasting using a two-stage spatial model
• Families of spatio-temporal stationary covariance models.
• Time Trend Estimation for a Geographic Region
• A Latent Gaussian Markov Random-Field Model for Spatiotemporal Rainfall Disaggregation
• Sampling-Based Approaches to Calculating Marginal Densities
• Handbook of Spatial Statistics
• Gaussian Predictive Process Models for Large Spatial Data Sets
• Problems with likelihood estimation of covariance functions of spatial Gaussian processes
• Scoring Rules for Continuous Probability Distributions
• Model-Based Geostatistics
• A Non-Homogeneous Hidden Markov Model for Precipitation Occurrence
• Venezuelan Rainfall Data Analysed by Using a Bayesian Space–time Model
• DATA AUGMENTATION AND DYNAMIC LINEAR MODELS
• On Gibbs sampling for state space models
• Objective Bayesian Analysis of Spatially Correlated Data
• Nonseparable, Stationary Covariance Functions for Space–Time Data
• Blur-generated non-separable space–time models
• Classes of Nonseparable, Spatio-Temporal Stationary Covariance Functions
• A Nonstationary Multisite Model for Rainfall
• Gaussian Markov Random Fields
• Q-ary cascaded GMW sequences
• A dimension-reduced approach to space-time Kalman filtering
• Strictly Proper Scoring Rules, Prediction, and Estimation
• Probabilistic Forecasts, Calibration and Sharpness
• Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics
• Monte Carlo sampling methods using Markov chains and their applications
• Space–Time Covariance Functions
• A Kernel-Based Spatio-Temporal Dynamical Model for Nowcasting Weather Radar Reflectivities
• Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center
• Geostatistical space-time models: a review

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