Abstract

The realization of the European Space Agency’s Aeolus mission was supported by the long-standing development and field deployment of the ALADIN Airborne Demonstrator (A2D) which, since the launch of the Aeolus satellite in 2018, has been serving as a key instrument for the validation of the Atmospheric LAser Doppler INstrument (ALADIN), the first-ever Doppler wind lidar (DWL) in space. However, the validation capabilities of the A2D are compromised by deficiencies of the dual-channel receiver which, like its spaceborne counterpart, consists of a Rayleigh and a complementary Mie spectrometer for sensing the wind speed from both molecular and particulate backscatter signals, respectively. Whereas the accuracy and precision of the Rayleigh channel is limited by the spectrometer’s high alignment sensitivity, especially in the near field of the instrument, large systematic Mie wind errors are caused by aberrations of the interferometer in combination with the temporal overlap of adjacent range gates during signal readout. The two error sources are mitigated by modifications of the A2D wind retrieval algorithm. A novel quality control scheme was implemented which ensures that only backscatter return signals within a small angular range are further processed. Moreover, Mie wind results with large bias of opposing sign in adjacent range bins are vertically averaged. The resulting improvement of the A2D performance was evaluated in the context of two Aeolus airborne validation campaigns that were conducted between May and September 2019. Comparison of the A2D wind data against a high-accuracy, coherent Doppler wind lidar that was deployed in parallel on-board the same aircraft shows that the retrieval refinements considerably decrease the random errors of the A2D line-of-sight (LOS) Rayleigh and Mie winds from about 2.0 m∙s−1 to about 1.5 m∙s−1, demonstrating the capability of such a direct detection DWL. Moreover, the measurement range of the Rayleigh channel could be largely extended by up to 2 km in the instrument’s near field close to the aircraft. The Rayleigh and Mie systematic errors are below 0.5 m∙s−1 (LOS), hence allowing for an accurate assessment of the Aeolus wind errors during the September campaign. The latter revealed different biases of the L2B Rayleigh-clear and Mie-cloudy horizontal LOS (HLOS) for ascending and descending orbits as well as random errors of about 3 m∙s−1 (HLOS) for the Mie and close to 6 m∙s−1 (HLOS) for the Rayleigh winds, respectively. In addition to the Aeolus error evaluation, the present study discusses the applicability of the developed A2D algorithm modifications to the Aeolus processor, thereby offering prospects for improving the Aeolus wind data quality.

Highlights

  • Doppler wind lidar (DWL) represents a powerful means to acquire atmospheric wind profiles with high spatiotemporal 35 resolution and high accuracy, and it has been employed from ground as well as from air- and shipborne platforms for many years

  • Since the start of the mission, the quality of the Aeolus wind data product has been assessed by a number of calibration and validation (Cal/Val) activities based on model comparisons (Martin et al, 2021) and ground-based or suborbital instruments 55 (Bedka et al, 2021; Iwai et al, 2021; Fehr et al, 2020, 2021; Baars et al, 2020; Khaykin et al, 2020)

  • While the Rayleigh wind results are impaired by the imperfect transmit-receive co-alignment in combination with the incomplete telescope overlap in the instrument’s near field, large systematic manifests significant reduction of gross errors (Mie) wind errors are caused by imperfections of the Fizeau interferometer (FI) plates which results in a skewed 70 interference fringe

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Summary

Introduction

Doppler wind lidar (DWL) represents a powerful means to acquire atmospheric wind profiles with high spatiotemporal 35 resolution and high accuracy, and it has been employed from ground as well as from air- and shipborne platforms for many years. The Aeolus mission primarily aims at improving numerical weather prediction (NWP) by filling observational gaps in the global wind data coverage, over the oceans, poles, tropics, and the Southern Hemisphere (Andersson, 2018; Stoffelen et al, 2005, 2020; Straume et al, 2020) This goal was already achieved within the first half of the intended mission lifetime of three years with the successful assimilation of Aeolus winds into NWP models, after having identified and corrected for two large error sources that had diminished the wind data quality in the early phase of the mission (Kanitz et al, 45 2020; Reitebuch et al, 2020). While the Rayleigh wind results are impaired by the imperfect transmit-receive co-alignment in combination with the incomplete telescope overlap in the instrument’s near field, large systematic Mie wind errors are caused by imperfections of the Fizeau interferometer (FI) plates which results in a skewed 70 interference fringe These two error sources were tackled by several modifications of A2D wind retrieval which considerably improved the accuracy and precision of the wind measurement while increasing the measurement range. The article closes with a summary and conclusions drawn from the analyses (Sect. 5), including a discussion on the possible applicability of the developed correction schemes to the Aeolus wind retrieval

Overview of validation campaigns and datasets
AVATARE
The A2D wind retrieval
Mitigation of Rayleigh wind errors
A2D error assessment using the 2-μm DWL
AVATARI
Comparison with previous campaigns
730 5 Summary and conclusions
Findings
825 Acknowledgements
Full Text
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