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October 3, 2025Open Access

Five years of Aeolus wind profiling: global coverage and data quality

Key Points

Aeolus provided unprecedented insights into wind profiles, enhancing forecast skill in numerical weather prediction.
Key parameters like signal-to-noise ratio and error estimate are evaluated over the mission's duration, revealing trends in data coverage.
This analysis interrelates Level-1B and Level-2B data to improve understanding of instrument performance and atmospheric phenomena.
Findings highlight how events like wildfires and volcanic eruptions influenced wind data collection and quality.

Abstract

Abstract. The European Space Agency's Aeolus mission (2018–2023) was the first satellite to deliver global wind profile observations using Doppler wind lidar technology. Aeolus significantly advanced numerical weather prediction (NWP) and atmospheric science, particularly in improving forecast skill and understanding global circulation and aerosol transport. With its successor mission Aeolus-2 now in development, a detailed assessment of Aeolus' long-term performance is essential to guide future system design and processing strategies. This study analyses the evolution and interrelation of key parameters from Aeolus Level-1B (L1B) and Level-2B (L2B) data products from processor baseline 16, including signal-to-noise ratio (SNR), error estimate (EE), and wind data coverage. For the first time, L1B instrument parameters are interpolated onto the L2B wind grid, enabling direct correlation with final product quality and tracking of performance changes across the mission. A major focus is placed on wind data coverage. Traditional metrics counted valid observations without considering variable horizontal and vertical bin sizes. Here, we assess the atmospheric area covered, accounting for the varying horizontal and vertical extent of wind bins from the Rayleigh and Mie channel across different mission phases. This reveals important changes in data yield and the influence of events like wildfires (2019) and the Hunga Tonga eruption (2022). We also evaluate how well the EE represents actual wind uncertainty using observation minus short-range NWP forecast differences. Under low-SNR conditions, the Rayleigh-clear EE slightly overestimates random error, whereas in the high-SNR regime, the Mie-cloudy EE tends to underestimate uncertainty. These findings provide critical input for optimising Aeolus-2 instrument design and data processing and offer a valuable framework for future Doppler wind lidar missions by improving data evaluation, quality control, and assimilation readiness.

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