Validation of the Aeolus L2B wind product: A new, very fast algorithm for the Fizeau fringe analysis based on pixel intensity ratios

Abstract

The measurement of Aeolus Mie-cloudy winds is based on the fringe-imaging technique. It relies on determining the spatial location of a linear interference pattern (fringe) that is originated from multiple interference in a Fizeau spectrometer and vertically imaged onto the Mie-channel detector. The accuracy of Mie-cloudy winds thus depends on several pre- and post-detection factors. These include the optical quality of the Fizeau interferometer, its manner of illumination and any spurious background light, as well as the number of detector pixels and the analytic algorithm used for determining the fringe location on the detector. In the Aeolus Level 1 B (L1B) processor, the centroid location and the width of the Fizeau fringes are usually analyzed by the Mie-core 2 algorithm, which applies a downhill simplex fit routine of a Lorentzian peak function to the measurement data. Although this algorithm works accurately and reliably, recent investigations based on atmospheric ground return signals demonstrated, that the Mie fringe profile is better described by a Voigt profile and, thus, the application of a Voigt fit improves the frequency measurement and the accuracy of the retrieved scattering ratio. The Voigt-fit was implemented in the L1B processor in 2022 and will be tested in the future for Mie fringe centroid computation. Against this background, an alternative algorithm based on an intensity ratio of the inner 4 pixels was developed (R4) which is insensitive to uniform background illumination. Simulations also demonstrated that the R4 algorithm is rather insensitive to the spectral shape of the fringe profile and that it is potentially one to two orders of magnitude faster than the fit-based approaches. Besides simulations, the R4 algorithm was applied to data of the Aladin Airborne Demonstrator (A2D) and the results were compared to both, the Lorentzian and the Voigt fit analysis. In particular, the data set from the AVATAR-I (Aeolus VAlidation Through Airborne LidaRs in Iceland) campaign was used for this study. In this contribution, we introduce the R4 algorithm in detail and investigate differences to the existing Mie-core algorithms (Lorentzian and Voigt fits) based on A2D data acquired during the AVATAR-I campaign in Iceland in 2019.