An approach to statistical spatial-temporal modeling of meteorological fields

Journal of the American Statistical Association

Volumn 89, Issue 426, 1994, Pages 368-378

  Handcock, Mark S ^a   Wallis, James R ^b  

^a NEW YORK UNIVERSITY   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

Bayesian statistics; Climatic change; Gaussian random fields

Indexed keywords

EID: 84950427374     PISSN: 01621459     EISSN: 1537274X     Source Type: Journal    
DOI: 10.1080/01621459.1994.10476754     Document Type: Article

References (41)

1. Abramowitz, M., and Stegun, I. A. (1965), Handbook of Mathematical Functions, New York: Dover.
2. Cody, W. J. (1987), “SPECFUN—A Portable Special Function Package,” in New Computing Environments: Microcomputers in Large-Scale Scientific Computing, ed. A. Wouk, Philadelphia; SIAM, pp. 1–12.
3. Cramér, H., and Leadbetter, M. R. (1967), Stationary and Related Stochastic Processes, New York: John Wiley.
4. Cressie, N. A. C. (1991), Statistics for Spatial Data, New York: John Wiley.
5. Creutin, J. D., and Obled, C. (1982), “Objective Analyses and Mapping Techniques for Rainfall Fields: An Objective Comparison,” Water Resources Research, 18, 413–431.
6. Diaz, H. F., and Quayle, R. G. (1978), “The 1976–77 Winter in the Contiguous United States in Comparison With Past Records,” Monthly Weather Review, 106, 1393–1421.
7. —— (1980), “The Climate of the United States Since 1895: Spatial and Temporal Changes,” Monthly Weather Review, 108, 249–266.
8. Dongarra, J. J., and Du Croz, J. (1985), “Distribution of Mathematical Software Via Electronic Mail,” Technical Report MCS-TM-48, Argonne National Laboratory, Mathematics and Computer Science Division.
9. Gandin, L. S. (1963; 1965), Objective Analysis of Meteorological Fields, Leningrad: GIMIZ. Translated from Russian by R. Hardin. Israel program for Scientific Translations, Jerusalem.
10. Handcock, M. S. (1989), “Inference for Spatial Gaussian Random Fields When the Objective is Prediction,” unpublished Ph.D. thesis, University of Chicago, Dept. of Statistics.
11. Handcock, M. S., and Stein, M. L. (1993), “A Bayesian Analysis of Kriging,” Technometrics, 35, 4, 403–410.
12. Handcock, M. S., and Wallis, J. R. (1990), “An Approach to Statistical Spatial–Temporal Modeling of Meteorological Fields,” paper presented at the European Geophysical Society XV General Assembly, Copenhagen. Denmark, April 23–27, 1990.
13. Haslett, J., and Raftery, A. E. (1989), Space–Time Modeling With LongMemory Dependence: Assessing Ireland’s Wind Power Resource” (with discussion), Journal of the Royal Statistical Society, Ser. C, 38, 1–50.
14. Hastie, T. J., and Tibshirani, R. J. (1990), Generalized Additive Models, London: Chapman and Hall.
15. Hoeksema, R. J., and Kitanidis, P. K. (1985), “Analysis of the Spatial Structure of Properties of Selected Aquifers,” Water Resources Research, 21, 563–572.
16. Hosking, J. R. M. (1981), “Fractional Differencing,” Biometrika, 68, 165–176.
17. —— (1984), “Modeling Persistence In Hydrological Time Series Using Fractional Differencing,” Water Resources Research, 12, 1898–1908.
18. Intergovernmental Panel on Climate Change (IPCC) (1990), Climate Change: The IPCC Scientific Assessment, New York: Cambridge University Press.
19. Jones, R. H. (1989), “Fitting a Stochastic Partial Differential Equation to Aquifer Head Data,” Stochastic Hydrology and Hydraulics, 3, 100–105.
20. Jones, P. D., Raper, S. C. B., Bradley, R. S., Diaz, H. F., Kelly, P. M., and Wigley, T. M. L. (1986), “Northern Hemisphere Surface Air Temperature Variations, 1851–1984,” Journal of Climate and Applied Meteorology, 25, 161–179.
21. Karl, T. R. (1985), “Perspective on Climate Change in North America During the Twentieth Century,” Physical Geography, 6, 207–229.
22. Karl, T. R., Heim, R. R., and Quayle, R. G. (1991), “The Greenhouse Effect in Central North America: If Not Now, When?,” Science, 251, 1058–1061.
23. Karl, T. R., Livezey, R. E., and Epstein, E. S. (1984), “Recent Unusual Mean Winter Temperatures Across the Contiguous United States,” Bulletin of the American Meteorology Society, 65, 1302–1309.
24. Karl, T. R., Williams, C. N., Jr., Quinlan, F. T., and Boden, T. A. (1990), “United States Historical Climatology Network (HCN) Serial Temperature and Precipitation Data,” NDP-019/R1. Oak Ridge National Laboratory, Carbon Dioxide Information Analysis Center.
25. Karl, T. R., Williams, C. M., Young, P. J., and Wendland, W. M. (1986), “A Model to Estimate the Time of Observation Bias Associated With Monthly Mean Maximum, Minimum and Mean Temperatures in the United States,” Journal of Climate and Applied Meteorology, 25, 145–160.
26. Kitanidis, P. K. (1983), “Statistical Estimation of Polynomial Generalized Covariance Functions and Hydrologic Applications,” Water Resources Research, 19, 909–921.
27. Kitanidis, P. K., and Lane, R. W. (1985), “Maximum Likelihood Parameter Estimation of Hydrologic Spatial Processes by the Gauss–Newton Method,” Journal of Hydrology, 17, 31–56.
28. Lettenmaier, D. P., Wood, E. F., and Wallis, J. R. (1993), “Hydrological Trends in the Continental United States,” submitted to Journal of Climate.
29. Mardia, K. V. (1989), Discussion of “Space–Time Modeling With LongMemory Dependence: Assessing Ireland’s Wind Power Resource,” by J. Haslett and A. E. Raftery, Journal of the Royal Statistical Society, Ser. C, 38, 36–37.
30. Mardia, K. V., and Marshall, R. J. (1984), “Maximum Likelihood Estimation of Models for Residual Covariance in Spatial Regression,” Biometrika, 71, 135–146.
31. Matérn, B. (1986), Spatial Variation (2nd ed.), Lecture Notes in Statistics, 36, Berlin: Springer-Verlag.
32. Matheron, G. (1965), Les Variables Régionalisées et leur Estimation, Paris: Masson.
33. Mejía, J. M., and Rodríguez-Iturbe, I. (1974), “On the Synthesis of Random Field Sampling From the Spectrum: An Application to the Generation of Hydrologie Spatial Processes,” Water Resources Research, 10, 705–711.
34. Mitchell, J. F. B. (1989), “The ‘Greenhouse Effect’ and Climate Change,” Reviews of Geophysics, 27, 115–139.
35. Omre, G. M., and Halvarsen, D. F. (1989), “A Bayesian Approach to Kriging,” in Proceedings of the Third International Geostatistics Congress I, ed. M. Armstrong, Dordrecht: Academic Publishers, pp. 49–68.
36. Quinlan, F. T., Karl, T. R., and Williams, C. N., Jr. (1987), “United States Historical Climatology Network (HCN) Serial Temperature and Precipitation Data,” NDP-019, Carbon Dioxide Information Analysis Center, Oak Ridge Laboratory.
37. Ripley, B. D. (1981), Spatial Statistics, New York: John Wiley.
38. Wallis, J. R., Lettenmaier, D. P., and Wood, E. F. (1991), “A Daily Hydroclimatological Data Set for the Continental United States,” Water Resources Research, 27, 1657–1663.
39. Whittle, P. (1954), “On Stationary Processes in the Plane,” Biometrika, 41, 434–449.
40. Zimmerman, M. S., and Cressie, N. (1992), “Mean Squared Prediction Error in the Spatial Linear Model With Estimated Covariance Parameters,” Annals of the Institute of Statistical Mathematics, 44, 27–43.
41. Zimmerman, M. S., and Zimmerman, M. L. (1991), “A Comparison of Spatial Semivariogram Estimators and Corresponding Ordinary Kriging Predictors,” Technometrics, 33, 77–91.