Inverse Modeling of the Ocean and Atmosphere

ANDREW BENNETT was awarded a Ph.D. in applied mathematics from Harvard University in 1971. He subsequently continued his research as a National Research Council Fellow at the University of Toronto, and as a Queen's Fellow in Marine Science at Monash University (Melbourne, Australia). Following 8 years as a lecturer and senior lecturer in the Department of Mathematics at Monash University, he became a research scientist at the Institute of Ocean Sciences, Sidney, B.C., Canada. He has been a professor at the College of Oceanic and Atmospheric Sciences at Oregon State University since 1987, where his research interests include ocean data assimilation, turbulence theory, and regional modeling. Professor Bennett has won refereeing awards from the Journal of Physical Oceanography (1986) and the Journal of Geophysical Research (1995), and is also the author of Inverse Methods in Physical Oceanography (Cambridge University Press, 1992).

"...introduces the reader to the representer method and to its geometrical and statistical interpretations. The book provides in-depth discussion of practical issues, especially concerning extensions of the representer method to large and to non-linear problems, and concerning the computation of error statistics. One of the most compelling sections of the book is the one that presents case studie dealing with the estimation of ocean tide, the prediction of tropical-cyclone tracks and intensitites, and the testing of an intermediate coupled model. This book also includes an excellent discussion of ill-posed forecasting problems and a brief review of various alternative approaches; for example, the adjoint method, the Kalman filter, and Monte Carlo approaches...the definitive reference on the application of representer methods to oceanographic and meteorological problems. The insightful and original treatment of this intricate subject ensures that inverse theory remains...

• Preamble; 1. Variational assimilation; 2. Interpretation; 3. Implementation; 4. The varieties of linear and nonlinear estimation; 5. The ocean and the atmosphere; 6. Ill-posed forecasting problems; References; Appendix A. Computing exercises; Appendix B. Euler-Lagrange equations for a numerical weather prediction model; Index.