Journal ArticleDOI
Space-time calibration of radar rainfall data
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TLDR
A space–time model for use in environmental monitoring applications is developed as a high dimensional multivariate state space time series model, in which the cross-covariance structure is derived from the spatial context of the component series, in such a way that its interpretation is essentially independent of the particular set of spatial locations at which the data are recorded.Abstract:
Motivated by a specific problem concerning the relationship between radar reflectance and rainfall intensity, the paper develops a space–time model for use in environmental monitoring applications. The model is cast as a high dimensional multivariate state space time series model, in which the cross-covariance structure is derived from the spatial context of the component series, in such a way that its interpretation is essentially independent of the particular set of spatial locations at which the data are recorded. We develop algorithms for estimating the parameters of the model by maximum likelihood, and for making spatial predictions of the radar calibration parameters by using realtime computations. We apply the model to data from a weather radar station in Lancashire, England, and demonstrate through empirical validation the predictive performance of the model.read more
Citations
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Estimating daily nitrogen dioxide level: exploring traffic effects
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Power-law correlations and other models with long-range dependence on a lattice
TL;DR: In this article, the authors introduced long-range dependence for a stationary random field on a plane lattice, and derived an exact power-law correlation model and other models with long-term dependence on the lattice.
Book ChapterDOI
Deriving Space-Time Variograms from Space-Time Autoregressive (STAR) Model Specifications
TL;DR: STAR specifications that parallel geostatistical model specifications commonly used to describe space–time variation are summarized, with the goal of establishing synergies between these two modeling approaches.
Journal ArticleDOI
Interpolation performance of a spatio-temporal model with spatially varying coefficients: application to PM10 concentrations in Rio de Janeiro
TL;DR: This work describes for this model how to make inference about the regression coefficients and response processes under two scenarios: when the explanatory processes are known throughout the study region, and when they are known only at the sampling locations.
Journal ArticleDOI
Bias-correction of Kalman filter estimators associated to a linear state space model with estimated parameters
Marco Costa,Magda Monteiro +1 more
TL;DR: In this article, the impact of the bias in the invariant state space models with estimated parameters is discussed and an adaptive correction procedure based on any parameters estimation method (for instance, maximum likelihood or distribution-free estimators).
References
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Book ChapterDOI
A New Approach to Linear Filtering and Prediction Problems
TL;DR: In this paper, the clssical filleting and prediclion problem is re-examined using the Bode-Shannon representation of random processes and the?stat-tran-sition? method of analysis of dynamic systems.
Book
Forecasting, Structural Time Series Models and the Kalman Filter
TL;DR: In this article, the Kalman filter and state space models were used for univariate structural time series models to estimate, predict, and smoothen the univariate time series model.
Posted Content
Forecasting, Structural Time Series Models and the Kalman Filter
TL;DR: In this paper, the authors provide a unified and comprehensive theory of structural time series models, including a detailed treatment of the Kalman filter for modeling economic and social time series, and address the special problems which the treatment of such series poses.
Journal ArticleDOI
Some Models for Rainfall Based on Stochastic Point Processes
TL;DR: In this paper, the variation of rainfall intensity at a fixed point in space is discussed for the variation in rainfall intensity over a fixed period of time and the main properties of these models are determined analytically.