Statistical Science

Statistics in Atmospheric Science

Andrew R. Solow

Full-text: Open access

Abstract

This paper reviews the use of statistical methods in atmospheric science. The applications covered include the development, assessment and use of numerical physical models of the atmosphere and more empirical analysis unconnected to physical models.

Article information

Source
Statist. Sci., Volume 18, Number 4 (2003), 422-429.

Dates
First available in Project Euclid: 8 April 2004

Permanent link to this document
https://projecteuclid.org/euclid.ss/1081443226

Digital Object Identifier
doi:10.1214/ss/1081443226

Mathematical Reviews number (MathSciNet)
MR2109370

Zentralblatt MATH identifier
1055.62135

Keywords
Data assimilation general circulation model model assessment parameterization of physical processes spatial time series

Citation

Solow, Andrew R. Statistics in Atmospheric Science. Statist. Sci. 18 (2003), no. 4, 422--429. doi:10.1214/ss/1081443226. https://projecteuclid.org/euclid.ss/1081443226


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References

  • Bailey, B. A., Berliner, L. M., Collins, W., Nychka, D. W. and Kiehl, J. T. (2000). Neural networks: Cloud parameterizations. In Studies in the Atmospheric Sciences. Lecture Notes in Statist. 144 97–116. Springer, New York.
  • Bellone, E., Hughes, J. P. and Guttorp, P. (2000). A hidden Markov model for downscaling synoptic atmospheric patterns to precipitation amounts. Climate Res. 15 1–12.
  • Berliner, L. M. (2001). Monte Carlo based ensemble forecasting. Stat. Comput. 11 269–275.
  • Berliner, L. M. (2003). Physical–statistical modeling in geophysics. J. Geophys. Res.–-Atmospheres. To appear.
  • Berliner, L. M., Levine, R. A. and Shea, D. J. (2000). Bayesian climate change assessment. J. Climate 13 3805–3820.
  • Berliner, L. M., Nychka, D. and Hoar, T., eds. (2000). Studies in the Atmospheric Sciences. Lecture Notes in Statist. 144. Springer, New York.
  • Berliner, L. M., Royle, J. A., Wikle, C. K. and Milliff, R. F. (1999). Bayesian methods in the atmospheric sciences. In Bayesian Statistics 6 (J. M. Bernardo, J. O. Berger, A. P. Dawid and A. F. M. Smith, eds.) 83–100. Oxford Univ. Press.
  • Berliner, L. M., Wikle, C. K. and Cressie, N. (2000). Long-lead prediction of Pacific SSTs via Bayesian dynamic modeling. J. Climate 13 3953–3968.
  • Brown, L., Casella, G. and Hwang, J. T. G. (1995). Optimal confidence sets, bioequivalence, and the limaçon of Pascal. J. Amer. Statist. Assoc. 90 880–889.
  • Cohn, S. E. (1997). An introduction to estimation theory. J. Meteorol. Soc. Japan 75 257–288.
  • Daley, R. (1997). Atmospheric data assimilation. J. Meteorol. Soc. Japan 75 319–329.
  • Errico, R. M., Fillion, L., Nychka, D. and Lu, Z.-Q. (2000). Some statistical considerations associated with the data assimilation of precipitation observations. Quart. J. Roy. Meteorol. Soc. Ser. A 126 339–360.
  • Evensen, G. and van Leeuwen, P. J. (2000). An ensemble Kalman smoother for nonlinear dynamics. Monthly Weather Review 128 1852–1867.
  • Fisher, M. and Courtier, P. (1995). Estimating the covariance matrices of analysis and forecast error in variational data assimilation. Technical Memorandum 220, European Center for Medium-Range Weather Forecasting, Reading, UK.
  • Hasselmann, K. F. (1988). PIPS and POPS: The reduction of complex dynamical systems using Principal Interaction and Oscillation Patterns. J. Geophys. Res. Atmospheres 93 11,015–11,021.
  • Jolliffe, I. T. (1986). Principal Component Analysis. Springer, New York.
  • Kennedy, M. C. and O'Hagan, A. (2001). Bayesian calibration of computer models. J. R. Stat. Soc. Ser. B Stat. Methodol. 63 425–464.
  • Kitagawa, G. (1996). Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. J. Comput. Graph. Statist. 5 1–25.
  • Kooperberg, C. and O'Sullivan, F. (1996). Predictive oscillation patterns: A synthesis of methods for spatial-temporal decomposition of random fields. J. Amer. Statist. Assoc. 91 1485–1496.
  • Kuligowski, R. J. and Barros, A. P. (1998). Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks. Weather and Forecasting 13 1194–1204.
  • Levine, R. A. and Berliner, L. M. (1999). Statistical principles for climate change studies. J. Climate 12 564–574.
  • Lorenz, E. N. (1963). Deterministic nonperiodic flow. J. Atmospheric Sci. 20 130–148.
  • Lu, Z. Q., Berliner, L. M. and Snyder, C. (2000). Experimental design for spatial and adaptive observations. In Studies in the Atmospheric Sciences. Lecture Notes in Statist. 144 65–78. Springer, New York.
  • Sacks, J., Welch, W. J., Mitchell, T. J. and Wynn, H. P. (1989). Design and analysis of computer experiments (with discussion). Statist. Sci. 4 409–435.
  • Salby, M. L. (1996). Fundamentals of Atmospheric Physics. Academic Press, San Diego, CA.
  • Sivillo, J. K., Ahlquist, J. E. and Toth, Z. (1997). An ensemble forecasting primer. Weather and Forecasting 12 809–818.
  • Sneddon, G. (2000). A statistical perspective on data assimilation in numerical models. In Studies in the Atmospheric Sciences. Lecture Notes in Statist. 144 7–21. Springer, New York.
  • Solow, A. R. and Moore, L. (2000). Testing for a trend in a partially incomplete hurricane record. J. Climate 13 3696–3699.
  • Solow, A. R. and Moore, L. (2002). Testing for trend in North Atlantic hurricane activity, 1900–98. J. Climate 15 3111–3114.
  • Trenberth, K. E., ed. (1992). Climate System Modeling. Cambridge Univ. Press.
  • Vislocky, R. L. and Fritsch, J. M. (1995). Generalized additive models versus linear regression in generating probabilistic MOS forecasts of aviation weather parameters. Weather and Forecasting 10 669–680.
  • von Storch, H. and Zwiers, F. W. (1999). Statistical Analysis in Climate Research. Cambridge Univ. Press.
  • West, M. and Harrison, J. (1997). Bayesian Forecasting and Dynamic Models. Springer, New York.
  • Wikle, C. K., Milliff, R. F., Nychka, D. and Berliner, L. M. (2001). Spatio-temporal hierarchical Bayesian modeling: Tropical ocean surface winds. J. Amer. Statist. Assoc. 96 382–397.
  • Xu, K.-M. and Randall, D. A. (1996). A semiempirical cloudiness parameterization for use in climate models. J. Atmospheric Sci. 53 3084–3102.