Space-time-time calibration of radar-rainfall data.

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Space-time-time calibration of radar-rainfall data. / Brown, Patrick E.; Diggle, Peter J.; Lord, Martin E. et al.
In: Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 50, No. 2, 2002, p. 221-241.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Brown, PE, Diggle, PJ, Lord, ME & Young, PC 2002, 'Space-time-time calibration of radar-rainfall data.', Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 50, no. 2, pp. 221-241. https://doi.org/10.1111/1467-9876.00230

APA

Brown, P. E., Diggle, P. J., Lord, M. E., & Young, P. C. (2002). Space-time-time calibration of radar-rainfall data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 50(2), 221-241. https://doi.org/10.1111/1467-9876.00230

Vancouver

Brown PE, Diggle PJ, Lord ME, Young PC. Space-time-time calibration of radar-rainfall data. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2002;50(2):221-241. doi: 10.1111/1467-9876.00230

Author

Brown, Patrick E. ; Diggle, Peter J. ; Lord, Martin E. et al. / Space-time-time calibration of radar-rainfall data. In: Journal of the Royal Statistical Society: Series C (Applied Statistics). 2002 ; Vol. 50, No. 2. pp. 221-241.

Bibtex

@article{f0e31c9bb73c474e90167ee6d7bdf16f,

title = "Space-time-time calibration of radar-rainfall data.",

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.",

keywords = "Dynamic linear model • Environmental monitoring • Kalman filter",

author = "Brown, {Patrick E.} and Diggle, {Peter J.} and Lord, {Martin E.} and Young, {Peter C.}",

year = "2002",

doi = "10.1111/1467-9876.00230",

language = "English",

volume = "50",

pages = "221--241",

journal = "Journal of the Royal Statistical Society: Series C (Applied Statistics)",

issn = "0035-9254",

publisher = "Wiley-Blackwell",

number = "2",

}

RIS

TY - JOUR

T1 - Space-time-time calibration of radar-rainfall data.

AU - Brown, Patrick E.

AU - Diggle, Peter J.

AU - Lord, Martin E.

AU - Young, Peter C.

PY - 2002

Y1 - 2002

N2 - 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.

AB - 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.

KW - Dynamic linear model • Environmental monitoring • Kalman filter

U2 - 10.1111/1467-9876.00230

DO - 10.1111/1467-9876.00230

M3 - Journal article

VL - 50

SP - 221

EP - 241

JO - Journal of the Royal Statistical Society: Series C (Applied Statistics)

JF - Journal of the Royal Statistical Society: Series C (Applied Statistics)

SN - 0035-9254

IS - 2

ER -

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