Remote effects spatial process models for modeling teleconnections

Abstract

While most spatial data can be modeled with the assumption that distant points are uncorrelated, some problems require dependence at both far and short distances. We introduce a model to directly incorporate dependence in phenomena that influence a distant response. Spatial climate problems often have such modeling needs as data are influenced by local factors in addition to remote phenomena, known as teleconnections. Teleconnections arise from complex interactions between the atmosphere and ocean, of which the El Niño–Southern Oscillation teleconnection is a well‐known example. Our model extends the standard geostatistical modeling framework to account for effects of covariates observed on a spatially remote domain. We frame our model as an extension of spatially varying coefficient models. Connections to existing methods are highlighted, and further modeling needs are addressed by additionally drawing on spatial basis functions and predictive processes. Notably, our approach allows users to model teleconnected data without prespecifying teleconnection indices, which other methods often require. We adopt a hierarchical Bayesian framework to conduct inference and make predictions. The method is demonstrated by predicting precipitation in Colorado while accounting for local factors and teleconnection effects with Pacific Ocean sea surface temperatures. We show how the proposed model improves upon standard methods for estimating teleconnection effects and discuss its utility for climate applications.

Basic Multivariate Statistical Methods for Environmental Monitoring Data Mining: Introductory Course for Master Students

“…In the references some major textbook are given and each one of them could be source of additional information on the theory and application of chemometrics and environmetrics for data interpretation and modelling. Additional small selection of applications of exploratory data analysis are offered for individual work and seminars [12][13][14][15][16][17].…”

Toward usable predictive climate information at decadal timescales

“…The remote effects spatial process (RESP) statistical model was developed as part of this project to identify and model relationships in observations between monthly and potentially predictable Pacific SSTs and monthly US precipitation. 39 The model improves on traditional approaches to estimating teleconnection effects by explicitly modeling both the spatial dependence in the SSTs and the spatial dependence in local precipitation. We demonstrated the RESP model for the case of winter precipitation in Colorado.…”

Approximate Bayesian Inference via Sparse grid Quadrature Evaluation for Hierarchical Models

“…While most spatial data can be modeled with the assumption that distant points are uncorrelated, large-scale atmospheric circulations can induce dependence between fields separated by large distances. The resulting climate phenomena, known as teleconnection, may be modeled using remote effects spatial process (RESP) models, which can improve teleconnection-based predictions of seasonal precipitation (Hewitt, Hoeting, Done and Towler, 2018). The RESP model is given by…”