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
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Dissertation
Bayesian Inference for Dynamic Spatio-temporal Models
TL;DR: A Hierarchical Bayesian framework for high dimensional spatio-temporal data based upon DTSMs is proposed which attempts to resolve this issue allowing the basis to adapt to the observed data, and a wavelet decomposition for the spatial evolution is presented.
Calibration of remote sensing measurements from surface observations
TL;DR: In this paper, a range of modelling techniques that may be used to tackle the problem of calibrating remote sensing measurements from surface observations is presented, and the authors propose a method to solve the problem.
References
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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.