Space-time calibration of radar rainfall data

Journal of the Royal Statistical Society. Series C: Applied Statistics

Volumn 50, Issue 2, 2001, Pages 221-241

  Brown, Patrick E ^a   Diggle, Peter J ^b   Lord, Martin E ^b   Young, Peter C ^b  

^a LANCASTER UNIVERSITY   (United Kingdom)

^b NONE

Author keywords

Dynamic linear model; Environmental monitoring; Kalman filter

Indexed keywords

EID: 0035649063     PISSN: 00359254     EISSN: None     Source Type: Journal    
DOI: 10.1111/1467-9876.00230     Document Type: Article

References (25)

1. Brown, P. E., Kåresen, K. F., Roberts, G. O. and Tonellato, S. (2000) Blur-generated non-separable space-time models. J. R. Statist. Soc. B, 62, 847-860.
2. Cliff, A. D. and Ord, J. K. (1975) Model building and the analysis of spatial pattern in human geography (with discussion). J. R. Statist. Soc. B, 37, 297-348.
3. Collinge, V. K. and Young, P. C. (1993) The calibration of weather radars using a time series approach. In Concise Encyclopaedia of Environmental Systems (ed. P. C. Young), pp. 706-710. Oxford: Pergamon.
4. Guttorp, P., Meiring, W. and Sampson, P. D. (1994) A space-time analysis of ground-level ozone data. Environmetrics, 3, 241-254.
5. -(1997) Ozone exposure and population density in Harris county, Texas - comment. J. Am. Statist. Ass., 92, 405-408.
6. Haas, T. C. (1995) Local prediction of a spatio-temporal process with an application to wet sulfate deposition. J. Am. Statist. Ass., 90, 1189-1199.
7. Handcock, M. S. and Wallis, J. R. (1994) An approach to statistical spatial-temporal modelling of meteorological fields. J. Am. Statist. Ass., 89, 368-378.
8. Harvey, A. C. (1989) Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press.
9. Haslett, J. and Raftery, A. E. (1989) Space-time modelling with long-memory dependence: assessing Ireland's wind power resource (with discussion). Appl. Statist., 38, 1-50.
10. Høst, G., Omre, H. and Switzer, P. (1995) Spatial interpolation errors for monitoring data. J. Am. Statist. Ass., 90 853-861.
11. Huang, H.-C. and Cressie, N. (1996) Spatio-temporal prediction of snow water equivalent using the Kalman filter. Comput. Statist. Data Anal., 22, 159-175.
12. Jones, R. H. and Zhang, Y. (1997) Models for continuous stationary space-time processes. Lect. Notes Statist., 122, 289-298.
13. Kalman, R. E. (1960) A new approach to linear filtering and prediction problems. ASME Trans. D, 82, 35-45.
14. Kyriakidis, P. C. and Journel, A. G. (1999) Geostatistical space-time models: a review. Math. Geol., 31, 651-684.
15. Lord, M. E. and Young, P. C. (1994) An improved adaptive calibration technique for weather radar. Technical Report 347. National Rivers Authority, Birmingham.
16. Mardia, K. V., Goodall, C., Redfern, E. J. and Alonso, F. J. (1998) The Kriged Kalman filter. TEST, 7, 217-252.
17. Matérn, B. (1960) Spatial variation. Medded. Stat. Skogsforskinst., 49, 1-144.
18. Meiring, W., Sampson, P. D. and Guttorp, P. (1998) Space-time estimation of grid-cell hourly ozone levels for assessment of a deterministic model. Environ. Ecol. Statist., 5, 197-222.
19. O'Hagan, A. (1998) A markov property for covariance structures. Unpublished.
20. Pfeifer, P. E. and Deutsch, S. J. (1980) A three state iterative procedure for space-time modeling. Technometrics, 22, 35-47.
21. Rodriguez Iturbe, I., Cox, D. R. and Isham, V. (1987) Some models for rainfall based on stochastic point-processes. Proc. R. Soc. Lond. A, 410, 269-288.
22. Stern, R. D. and Coe, R. (1984) A model fitting analysis of daily rainfall data (with discussion). J. R. Statist. Soc. A, 147, 1-34.
23. Wikle, C. K., Berliner, L. M. and Cressie, N. (1998) Hierarchical Bayesian space-time models. Environ. Ecol. Statist., 5, 117-154.
24. Wikle, C. K. and Cressie, N. (1999) A dimension-reduction approach to space-time Kalman filtering. Biometrika, 86, 815-829.
25. Young, P. C. (1999) Nonstationary time series analysis and forecasting. Prog. Environ. Sci., 1, 3-48.