Influence of systematic error on least squares retrieval of upper atmospheric parameters from the ultraviolet airglow

This paper investigates the effect of simple systematic error, or bias (i.e., in the magnitude of data or an associated model), on physical parameters retrieved by least squares algorithms from observations that are indexed by an independent variable. This factor is now of critical interest with the advent of global, space-based ultraviolet remote sensing of thermospheric and ionospheric composition by experimental and operational systems. A finite bias between an observed intensity profile and the parametric physical model used to compute a least squares solution will contaminate the values of retrieved physical parameters. The simplest mitigation method is to retrieve an additional bias adjustment parameter (additive or multiplicative) as part of the solution. The result is a measurably superior fit. The utility of this approach can depend on the particular spectral feature of interest. The discussion includes derivation of relevant equations and diagnostic tests. An illustrative application concerns recent observations of the dayside O II 834 A airglow, which contains information on O+, the dominant ion in the ionospheric F region.