An Adaptive Inverse Method for Model Tuning and Testing,
Abstract
To determine the value of the adjustable parameters of an ocean model that are required to optimally fit the observations, an adaptive inverse method is developed and applied toa sea surface temperature (SST) model of the tropical Atlantic. The best-fit calculation is performed by minimizing the misfit between observed and simulated data, which depends on the observational and the modelization errors. An adaptive procedure is designed where the model that is being tuned is also to construct a sample estimate of the observational error covariance matrix. Assuming idealized modelization errors, the procedure is applied to the SST model of Blumenthal and Cane (1989), yielding improved estimates for several model and heat flux parameters. The tuned model provides a better simulation of the mean annual SST, but the model's ability to represent the seasonal and the interannual variability is not improved, and and the model-observed discrepancies remain too large. The existence of larger model deficiencies than was originally assumed in the model errors is confirmed by a statistical test of the correctness of the assumption in the inverse calculation
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 01, 1993
- Accession Number
- ADP008742
Entities
People
- Claude Frankignoul
- Nathalie Scoffier
Organizations
- Pierre and Marie Curie University