Oliver Reitebuch

The Aeolus mission demonstrated the significant value of global Doppler wind lidar observations for numerical weather prediction (NWP) and atmospheric research by providing the first spaceborne measurements of wind profiles on a global scale. In particular, Aeolus Mie-cloudy winds showed a strong positive impact on weather forecasting despite their comparatively limited spatial coverage. However, the operational Level-2B processing was deliberately optimized for robust retrievals from strong cloud returns, resulting in the rejection of many potentially useful wind observations under weak backscatter conditions such as aerosol layers, optically thin cirrus clouds, and polar stratospheric clouds. Recovering additional winds in these regions is scientifically important because they are closely linked to aerosol transport and upper-tropospheric and lower-stratospheric dynamics. To address this limitation, a median-filter-based quality-control scheme was developed to identify physically meaningful wind observations among the large fraction of Mie-cloudy winds flagged as invalid by the operational processing. The method compares each wind result with neighbouring observations in along-track distance and altitude space using a computationally efficient k-d tree approach. Invalid winds are recovered only if they are dynamically consistent with the surrounding wind field and satisfy additional plausibility criteria designed to suppress gross retrieval artefacts. The performance of the algorithm was evaluated for representative Aeolus orbits throughout the mission and for month-long periods in September 2022 and April 2023. The method substantially increases the number of useful Mie-cloudy wind observations while maintaining acceptable error characteristics. Depending on the selected filter configuration, the number of retrieved winds increases typically by 25–60%, whereas the associated increase in random error remains below 1 m/s. Particularly strong improvements are achieved in the upper troposphere and lower stratosphere, including during measurements of the 2022 Hunga Tonga stratospheric volcanic aerosol plume, when the coverage of wind observations with departures from the model background of less than 2.5 m/s increased by up to a factor of six in the lower stratosphere. The developed approach therefore significantly enhances the scientific and meteorological value of the Aeolus dataset and provides a promising approach for future spaceborne Doppler wind lidar missions such as Aeolus-2.

This feasibility study demonstrates, through modelling and simulation, that a Versatile Atmospheric Laser Doppler Instrument (VALDI), based on a single Fizeau interferometer and imaging detector, has the potential for precise, high-sensitivity, simultaneous measurements of wind speeds, temperature and atmospheric scattering ratio (ASR). In simple principle, the linear frequency dispersion of the Fizeau interferometer creates in the fringe plane an extended spectrum of atmospheric scattering. This is made up of broadband molecular scattering resulting in a Rayleigh-Brillouin spectral component of ≈4 GHz full width at half maximum (at 355 nm wavelength), and a narrow band spectral component originating from aerosol (Mie) scattering with spectral characteristics defined by the laser source and Fizeau design parameters. For the present investigation, a 16 channel detector, similar to those employed in ESA’s Aeolus spaceborne lidar is considered, together with an adapted Fizeau interferometer of specifications (plate separation, wedge angle, finesse, etc.) well within current practice. Extensive modelling and simulation demonstrates that the 16 detector channels of spectral information provide versatile, high sensitivity operation, for widely varying atmospheric scattering and attenuation conditions throughout the troposphere and lower stratosphere. Drawing on operational data and experience of the ESA Aeolus spaceborne lidar systems, simulations indicate that a spaceborne VALDI could achieve wind speed uncertainties σvHLOS in the range of ±1.4 m s−1 to ±3.2 m s−1, from the aerosol signal in central detector channels (with ASR ≈ 2 and 12 km observational path). The outer channels, dominated by molecular signal, should give wind speed uncertainties between ±2.4 m s−1 and ±3.4 m s−1 (for 87 km observation path length). It is further shown that fast analytical ratio techniques provide Doppler frequency analysis with accuracy approaching the Cramér-Rao ultimate quantum limit within 10 % to 20 %. Finally, questions of practical design, construction and operation are considered. In this regard the single interferometer and detector, with single optical delivery train, offers considerable advantages of optical simplicity, high signal throughput efficiency, robustness and good long term alignment control and stability, plus wide frequency/wind speed capability extending well above 100 m s−1.

Mineral dust is a key component of the globally-emitted aerosol mass. Although, mineral dust mixes with anthropogenic pollution during its atmospheric lifetime, data on polluted mineral dust layers have been scarce.In April 2017, the A-LIFE aircraft field experiment (www.a-life.at) was carried out in the Eastern Mediterranean. A-LIFE combined ground-based, airborne, satellite, and modelling efforts to characterize mineral dust mixtures with unprecedented detail. In 22 research flights (~80 flight hours), outbreaks of Saharan and Arabian dust, as well as pollution, biomass burning, and dust-impacted clouds were studied, and a unique aerosol and cloud data set was collected. Aerosol source apportionment was achieved with the Lagrangian transport and dispersion model FLEXPART version 8.2. Based on FLEXPART model results and aerosol measurements, the observations were classified into 12 aerosol types consisting of four main aerosol types (Saharan dust, Arabian dust, mixtures with and without coarse mode). Each of the four main aerosol types was further separated into three sub-classes (clean, moderately-polluted and polluted). For each of the 12 aerosol classes, microphysical and optical aerosol properties were derived.For the first time, the effect of pollution on the microphysical and optical properties of Saharan and Arabian dust was investigated systematically, revealing significant changes as a function of pollution content. The particle size distribution changes as a function of pollution content with effective diameters systematically decreasing for increasing pollution content. The collected data also provide new insights into the impact of Saharan and Arabian dust on cloud evolution processes, atmospheric radiation budget, and local meteorology. One outstanding finding of A-LIFE is that scattering properties of polluted dust mixtures do not show the typical dust signature, but rather show a wavelength-dependency of the scattering coefficient which is typical for pollution. This means that optical properties of mineral mixtures are frequently dominated by the pollution.In this presentation, we will show the results of the A-LIFE project including its mission objectives, experimental design, and meteorological conditions; highlight major A-LIFE findings; and feature the available data products on the optical, microphysical, and hygroscopic properties of pure and polluted mineral dust.

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 averaged 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.

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.

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.

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.

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.

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.

This paper presents an extensive investigation of the signal fringe profile for the Fizeau interferometer used in the first spaceborne wind lidar Aeolus and considers the fundamental implications for the wind measurement accuracy in Aeolus and future systems. The early Aeolus design phase considered that the basic fringe would be made up of a Fizeau instrumental component of ≈100 MHz (full width at half maximum, FWHM), folded with the laser pulse spectral width of ≈50 MHz (FWHM), both of Lorentzian form. Fringe anomalies observed before the mission and related to surface defects in the interferometer plates triggered the development of wave-optic methods for analysis of the fringe formation. These methods, herein described in an instructional appendix, were subsequently found to be essential for rigorous modelling of complex fringes for different physical and optical arrangements. Initial signal returns from Aeolus suggested that the Fizeau fringe profile was in fact broadened with a large Gaussian component. The laser pulse was subsequently shown to have a profile close to Gaussian of ≈45 MHz (FWHM) and thus provided a partial contribution. However, detailed examination of experimental Aeolus fringes constructed from ground return signals showed a large Gaussian component up to ≈130 MHz (FWHM). Wave-optic modelling established that Fizeau “aperture broadening”, of this form and magnitude, would be generated for the input signal beam of 500 µrad field of view (FOV) set at a large angle of incidence (AOI) of 300 µrad. These findings have strong implications for fringe shift and wind measurement accuracy, as given in the quantum-limited Cramér–Rao expression and the paramount importance of minimizing line width. Extensive modelling and simulation for the broadened profiles calculated above shows good agreement with measured Aeolus global wind measurement accuracies and indicates that loss of signal could be due to beam clipping at the field stop for such a large AOI. It is established that optimization of the present Aeolus Fizeau parameters could lead to a factor of 2.5 improvement in wind measurement precision. Future upgrades of the Fizeau interferometer and the laser within reasonable parameters suggest the potential for an factor of 7.6 improvement on the in-orbit performance.