Practical and Theoretical Aspect of Adjoint Parameter Estimation and Identifiability in Meteorology and Oceanography
I. M. Navon
Dedicated to Professor Richard Pfeffer
The present paper has two aims. One is to survey briefly the state of the art of parameter estimation in meteorology and oceanography in view of applications of 4-D variational data assimilation techniques to inverse parameter estimation problems, which bear promise of serious positive impact on improving model prediction. The other aim is to present crucial aspects of identifiability and stability essential for validating results of optimal parameter estimation and which have not been addressed so far in either the meteorological or the oceanographic literature.
As noted by Yeh(1986) in the context of ground water flow parameter estimation the inverse or parameter estimation problem is often ill-posed and beset by instability and nonuniqueness, particularly if one seeks parameters distributed in space-time domain. This approach will allow one to assess and rigorously validate results of parameter estimation, i.e., do they indeed represent a real identification of physical model parameters or just compensate model errors? A brief survey of other approaches for solving the problem of optimal parameter estimation in meteorology and oceanography is finally presented.