Interpolating fields of carbon monoxide data using a hybrid statistical-physical model

Annals of Applied Statistics

Volumn 2, Issue 4, 2008, Pages 1231-1248

  Malmberg, Anders ^a   Arellano, Avelino ^a   Edwards, David P ^a   Flyer, Natasha ^a   Nychka, Doug ^a   Wikle, Christopher ^a  

^a NONE

Author keywords

Bayesian hierarchical models; Carbon monoxide; Data assimilation; Interpolation; Satellite data

Indexed keywords

EID: 77956372069     PISSN: 19326157     EISSN: 19417330     Source Type: Journal    
DOI: 10.1214/08-AOAS168     Document Type: Article

References (29)

1. Arellano, A. F. et al. (2007). Evaluating model performance of an ensemble-based chemical data assimilation system during INTEX-B field mission. Atmos. Chem. Phys. 7 5695-5710.
2. Banerjee, S., Gelfand, A. E. and Carlin, B. P. (2004). Hierarchical Modeling and Analysis for Spatial Data. Chapman and Hall/CRC, Boca Raton, FL.
3. Barrie, L. A., Borrell, P. and Langen, J., eds. (2004). The changing atmosphere, an integrated global atmospheric chemistry observation (IGACO) theme for the IGOS partnership global atmospheric watch Report 159 (World Meteorological TD No. 1235). European Space Agency, Noordwijk, The Netherlands.
4. Berliner, L. M. (1996). Hierarchical Bayesian time series models. In Maximum Entropy and Bayesian Methods (Santa Fe, NM, 1995) 79 15-22. Kluwer Acad. Publ., Dordrecht.
5. Chai T. et al. (2006). Chemical data assimilation of transport and chemical evolution over the Pacific (TRACE-P) aircraft measurements. J. Geophys. Res. 111 D02301. DOI: 10.1029/2005JD005883.
6. Cicerone, R. J. (1988). How has the atmospheric concentration of CO changed? In The Changing Atmosphere (F. S. Rowland and I. S. A. Isaksen, eds.) 49-61. Wiley, New York.
7. Collins, W. D., Rasch, P. J., Boville, B. A., Hack, J. J., McCaa, J. R., Williamson, D. L. and Briegleb, B. P. (2006). The formulation and atmospheric simulation of the community atmosphere model version 3 (CAM3). J. Climate 19 2144-2161.
8. Cressie, N. (1993). Statistics for Spatial Data, rev. ed. Wiley, New York.
9. Deeter, M. N. et al. (2004). Vertical resolution and information content of CO profiles retrieved by MOPITT. Geophys. Res. Lett. 31 L15112. DOI: 10.1029/2004GL020235.
10. Eben, K. et al. (2005). An ensemble Kalman filter for short-term forecasting of tropospheric ozone concentrations. Q. J. R. Meteorol. Soc. 131 3313-3322.
11. Edwards, D. P. et al. (2004). Observations of carbon monoxide and aerosol from the terra satellite: Northern hemisphere variability. J. Geophys. Res. 109 D24202. DOI: 10.1029/2004JD0047272004.
12. Edwards, D. P. (2006). Air quality remote sensing from space. EOS Transactions AGU 87 327.
13. Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C. and Eder, B. (2005). Fully coupled online chemistry within the WRF model. Atmospheric Environment 39 6957-6975.
14. Haberman, R. (1987). Elementary Applied Partial Differential Equations, 2nd ed. Prentice Hall, Englewood Cliffs, NJ.
15. Hanea, R. G., Velders, G. J. M. and Heemink, A. (2004). Data assimilation of ground-level ozone in Europe with a Kalman filter and chemistry transport model. J. Geophys. Res. 109 D10302. DOI: 10.1029/2003JD004283.
16. Hotlon, J. R. (2004). An Introduction to Dynamic Meteorology. North-Holland, Amsterdam.
17. Kalman, R. E. and Bucy, R. S. (1961). New results in linear filtering and prediction theory. Trans. ASME Ser. D-J. Basic Engrg. 83 95-108.
18. Kalnay, E. (2003). Atmospheric Modeling, Data Assimilation and Predictability. Cambridge Univ. Press.
19. Khare, S. P., Anderson, J. L., Hoar, T. J. and Nychka, D. (2008). An investigation into the application of an ensemble Kalman smoother to high-dimensional geophysical systems. Tellus A. To appear.
20. Lamarque, J.-F. et al. (2004). Application of a bias estimator for the improved assimilation of Measurements of Pollution in the Troposphere (MOPITT) carbon monoxide retrievals. J. Geophys. Res. 109 D16304. DOI: 10.1029/2003JD004466.
21. Lary, D. J. (1999). Data assimilation: A powerful tool for atmospheric chemistry. Phil. Trans. R. Soc. Ser. A. Math. Phys. 357 3445-3457.
22. Malmberg, A., Arellano, A., Edwards, D. P., Flyer, N., Nychka, D. and Wikle, C. (2008). Supplement to "Interpolating fields of carbon monoxide data using a hybrid statistical-physical model." DOI: 10.1214/08-AOAS168SUPP.
23. Pan, L., Gille, J. C., Edwards, D. P. and Bailey, P. L. (1998). Retrieval of tropospheric carbon monoxide for the MOPITT experiment. J. Geophysical Research 103 32,277-32,290.
24. Robert, C. P. and Casella, G. (1999). Monte Carlo Statistical Methods. Springer, New York.
25. Sandu, A. et al. (2005). Ensemble-based data assimilation for atmospheric chemical transport models. Lecture Notes in Comput. Sci. 3515 648-655. Springer, Berlin.
26. Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and Its Applications. Springer, New York.
27. Wikle, C. K., Milliff, R. F., Nychka, D. and Berliner, L. M. (2001). Spatiotemporal hierarchical Bayesian modeling: Tropical ocean surface winds. J. Amer. Statist. Assoc. 96 382-397.
28. Wikle, C. K. (2003). Hierarchical Bayesian models for predicting the spread of ecological processes. Ecology 84 1382-1394.
29. Wikle, C. K., Berliner, L. M. and Milliff, R. F. (2003). Hierarchical Bayesian approach to boundary value problems with stochastic boundary conditions. Monthly Weather Review 131 1051-1062.