Spatial-temporal mesoscale modeling of rainfall intensity using gage and radar data

DOI10.1214/08-AOAS166zbMath1168.62103arXiv0901.3478MaRDI QIDQ999645

Gyuwon Lee, Montserrat Fuentes, Brian J. Reich

Publication date: 10 February 2009

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

Full work available at URL: https://arxiv.org/abs/0901.3478

zbMATH Keywords

nonstationarityconditionally autoregressive modelsspatial logistic regressionfull symmetryrainfall modellingspatial-temporal models

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to environmental and related topics (62P12) Geostatistics (86A32) Meteorology and atmospheric physics (86A10)

Related Items (8)

A spatio-temporal modeling framework for weather radar image data in tropical southeast Asia ⋮ Bayesian analysis of nonnegative data using dependency-extended two-part models ⋮ Modelling categorized levels of precipitation ⋮ A dynamic nonstationary spatio-temporal model for short term prediction of precipitation ⋮ An adaptive spatial model for precipitation data from multiple satellites over large regions ⋮ Special section on statistics in the atmospheric sciences ⋮ Comments on: Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds ⋮ Statistical downscaling with spatial misalignment: application to wildland fire \(\mathrm{PM}_{2.5}\) concentration forecasting

Uses Software

• R

Cites Work

• Unnamed Item
• Testing lack of symmetry in spatial-temporal processes
• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• Testing for directional symmetry in spatial dependence using the periodogram
• Testing axial symmetry and separability of lattice processes
• Case studies in Bayesian statistics. Vol 6: Proceedings of the 6th workshop, Carnegie Mellon University, Pittsburgh, PA, USA, September 28--29, 2001
• Multiresolution models for nonstationary spatial covariance functions
• Nonseparable, Stationary Covariance Functions for Space–Time Data
• A Nonstationary Multisite Model for Rainfall
• Spectral methods for nonstationary spatial processes
• Bayesian Measures of Model Complexity and Fit
• Space–Time Covariance Functions

This page was built for publication: Spatial-temporal mesoscale modeling of rainfall intensity using gage and radar data