Modeling transport effects on ground-level ozone using a non-stationary space-time model

Environmetrics

Volumn 15, Issue 3, 2004, Pages 251-268

  Huang, Hsin Cheng ^a   Hsu, Nan Jung ^b  

^a INSTITUTE OF STATISTICAL SCIENCE   (Taiwan)

^b NATIONAL TSING HUA UNIVERSITY   (Taiwan)

Author keywords

Empirical orthogonal function; Kalman filter; Kriging; Non separable spatio temporal covariance function; Wind direction; Wind speed

Indexed keywords

GROUND-BASED MEASUREMENT; KALMAN FILTER; KRIGING; OZONE; WIND DIRECTION; WIND VELOCITY;

EID: 1942454839     PISSN: 11804009     EISSN: None     Source Type: Journal    
DOI: 10.1002/env.639     Document Type: Article

References (19)

1. Brown PE, Kåresen KF, Robert GO, Tonellato S. 2000. Blur-generated nonseparable space-time models. Journal of the Royal Statistical Society, Series B 62: 847-860.
2. Buell CE. 1972. Integral equation representation for factor analysis. Journal of Atmospheric Science 28: 1502-1505.
3. Carroll RJ, Chen R, George EI, Li TH, Newton HJ, Schmiediche H, Wang N. 1997. Ozone exposure and population density in Harris county, Texas. Journal of the American Statistical Association 92: 392-415.
4. Cohen A, Jones RH. 1969. Regression on a random field. Journal of the American Statistical Association 64: 1172-1182.
5. Cressie N. 1985. Fitting variogram models by weighted least squares. Journal of the International Association for Mathematical Geology 17: 563-583.
6. Cressie N, Huang H-C. 1999. Classes of nonseparable, spatio-temporal stationary covariance functions. Journal of the American Statistical Association 94: 1330-1340.
7. Gneiting T. 2002. Nonseparable, stationary covariance functions for space-time data. Journal of the American Statistical Association 97: 590-600.
8. Guttorp P, Meiring W, Sampson PD. 1994. A space-time analysis of ground-level ozone data. Environmetrics 5: 241-254.
9. Guttorp P, Meiring W, Sampson PD. 1997. Space-time estimation of grid-cell hourly ozone levels for assessment of a deterministic model. Technical Report Series No. 7, National Research Center of Statistics and the Environments, Seattle, Washington.
10. Harvey AC. 1989. Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press: Cambridge.
11. Holland D, Saltzman N, Cox LH, Nychka D. 1999. Spatial prediction of sulfur dioxide in the eastern United States. In geoENV II - Geostatistics for Environmental Applications, Gómez-Hernández J, Soares A, Froidevaux R (eds). Kluwer: Dordrecht; 65-76.
12. Huang H-C, Cressie N. 1996. Spatio-temporal prediction of snow water equivalent using the Kalman filter. Computational Statistics and Data Analysis 22: 159-175.
13. Huerta G, Sanso B, Stroud JR. 2000. Space-time analysis of Mexico city ozone levels. Technical Report 2000-13, Sentro de Estadística y Software Matemático, Caracas, Venezuela.
14. Karl TR, Koscielny AJ, Diaz HF. 1982. Potential errors in the application of principal component (eigenvector) analysis to geophysical data. Journal of Applied Meteorology 21: 1183-1186.
15. Mardia KV, Goodall C, Redfern EJ, Alonso FJ. 1998. The kriged Kalman filter. Test 7: 217-285.
16. Nychka D, Saltzman N. 1998. Design of air quality networks. In Case Studies in Environmental Statistics, Nychka D, Piegorsch W, Cox LH (eds). Lecture Notes in Statistics, 132, Springer Verlag: New York; 51-76.
17. Thomson ML, Reynolds J, Cox LH, Guttorp P, Sampson PD. 1999. A review of statistical methods for the meteorological adjustment of tropospheric ozone. Technical Report Series No. 26, National Research Center of Statistics and the Environments, Seattle, Washington.
18. Wikle CK, Cressie N. 1999. A dimension reduction approach to space-time Kalman filtering. Biometrika 86: 815-829.
19. Wikle CK, Cressie N. 2000. Space-time statistical modeling of environmental data. Quantifying Spatial Uncertainty in Natural Resources, Ann Arbor Press; 213-235.