Abstract
The development of coastal ocean modeling in the recent years has allowed an improved representation of the associated complex physics. Such models have become more realistic, to the point that they can now be used to design observation networks in coastal areas, with the idea that a “good” network is a network that controls model state error. To test this ability without performing data assimilation, we set up a technique called Representer Matrix Spectra (RMS) technique that combines the model state and observation error covariance matrices into a single scaled representer matrix. Examination of the spectrum and the eigenvectors of that matrix informs us on which model state error modes a network can detect and constrain amidst the observation error background. We applied our technique to a 3D coastal model in the Bay of Biscay, with a focus on mesoscale activity, and tested the performance of various altimetry networks and an in situ array deployment strategy. It appears that a single nadir altimeter is not efficient enough at capturing coastal mesoscale physics, while a wide swath altimeter would do a much better job. Testing various local in situ array configurations confirms that adding a current meter to a vertical temperature measurement array improves the detection of secondary variability modes, while shifting the array higher on the shelf break would obviously enhance the model constraint along the coast. The RMS technique is easily set up and used as a “black box,” but the utility of its results is maximized by previous knowledge of model state error physics. The technique provides both quantitative (eigenvalues) and qualitative (eigenvectors) tools to study and compare various network options. The qualitative approach is essential to discard possibly inconsistent modes.
Highlights
The development of complex ocean models, thanks to the increase in computational facilities along with the increasing number of oceanic observations, has helped to get a better representation of the ocean physics and variability
The idea of model state error control is the base of many studies about array design in recent years, most of them accompanying the development of data assimilation in oceanography
A relatively simple methodology to assess the performance of any observational network at detecting model state errors without having to perform data assimilation has been developed and tested on realistic cases
Summary
The development of complex ocean models, thanks to the increase in computational facilities along with the increasing number of oceanic observations, has helped to get a better representation of the ocean physics and variability. The goal of observation networks design is nowadays not so much about discovering unknown features of the ocean physics but rather about correctly detecting some specific aspects of it, such as large-scale currents, thermocline depths, eddies, etc These relevant observations would in return help models better represent the associated physics, leading to a more precise analysis of the ocean state. Using simplified data assimilation schemes or interpolation techniques to perform array design is relevant to study open ocean processes of relatively simple variability but is probably not ideal to study highly nonlinear, non-isotropic physics, for which the model error budget is much more complex. We would certainly be interested by an approach assessing the relative performances of various observation networks on a complex coastal zone at a relatively low computational cost, while keeping the idea that a “good” network must be able to detect and provide an efficient constraint of model state error.
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