Since its launch in 2018, the European Space Agency'sEarth Explorer satellite Aeolus has provided global height resolvedmeasurements of horizontal wind in the troposphere and lower stratospherefor the first time. Novel datasets such as these provide an unprecedentedopportunity for the research of atmospheric dynamics and provide newinsights into the dynamics of the upper troposphere and lower stratosphere(UTLS) region. Aeolus measures the wind component along its horizontalline-of-sight, but for the analysis and interpretation of atmosphericdynamics, zonal and/or meridional wind components are most useful. In thispaper, we introduce and compare three different methods to derive zonal andmeridional wind components from the Aeolus wind measurements. We find thatthe most promising method involves combining Aeolus measurements duringascending and descending orbits. Using this method, we derive globalestimates of the zonal wind in the latitude range 79.7 ∘  S to84.5 ∘  N with errors of less than 5 m s −1 (at the 2 σ level). Due to the orbit geometry of Aeolus, the estimation of meridionalwind in the tropics and at midlatitudes is more challenging and the qualityis less reliable. However, we find that it is possible to derive meridionalwinds poleward of 70 ∘ latitude with absolute errors typicallybelow 5 m s −1 (at the 2 σ level). This further demonstrates the value of Aeolus wind measurements for applications in weather and climate research, in addition to their important role in numerical weatherprediction.

During the first 3 years of the European Space Agency's Aeolus mission, the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt, DLR) performed four airborne campaigns deploying two different Doppler wind lidars (DWL) on board the DLR Falcon aircraft, aiming to validate the quality of the recent Aeolus Level 2B (L2B) wind data product (processor baseline 11 and 12). The first two campaigns, WindVal III (November–December 2018) and AVATAR-E (Aeolus Validation Through Airborne Lidars in Europe, May and June 2019), were conducted in Europe and provided first insights into the data quality at the beginning of the mission phase. The two later campaigns, AVATAR-I (Aeolus Validation Through Airborne Lidars in Iceland) and AVATAR-T (Aeolus Validation Through Airborne Lidars in the Tropics), were performed in regions of particular interest for the Aeolus validation: AVATAR-I was conducted from Keflavik, Iceland, between 9 September and 1 October 2019 to sample the high wind speeds in the vicinity of the polar jet stream; AVATAR-T was carried out from Sal, Cape Verde, between 6 and 28 September 2021 to measure winds in the Saharan dust-laden African easterly jet. Altogether, 10 Aeolus underflights were performed during AVATAR-I and 11 underflights during AVATAR-T, covering about 8000 and 11 000 km along the Aeolus measurement track, respectively.Based on these collocated measurements, statistical comparisons of Aeolus data with the reference lidar (2  µ m DWL) as well as with in situ measurements by the Falcon were performed to determine the systematic and random errors of Rayleigh-clear and Mie-cloudy winds that are contained in the Aeolus L2B product. It is demonstrated that the systematic error almost fulfills the mission requirement of being below 0.7  m s −1 for both Rayleigh-clear and Mie-cloudy winds. The random error is shown to vary between 5.5 and 7.1  m s −1 for Rayleigh-clear winds and is thus larger than specified ( 2.5  m s −1 ), whereas it is close to the specifications for Mie-cloudy winds ( 2.7 to 2.9  m s −1 ). In addition, the dependency of the systematic and random errors on the actual wind speed, the geolocation, the scattering ratio, and the time difference between 2  µ m DWL observation and satellite overflight is investigated and discussed. Thus, this work contributes to the characterization of the Aeolus data quality in different meteorological situations and allows one to investigate wind retrieval algorithm improvements for reprocessed Aeolus data sets.

Since the start of the European Space Agency's Aeolus mission in 2018, various studies were dedicated to the evaluation of its wind data quality and particularly to the determination of the systematic and random errors in the Rayleigh-clear and Mie-cloudy wind results provided in the Aeolus Level-2B (L2B) product. The quality control (QC) schemes applied in the analyses mostly rely on the estimated error (EE), reported in the L2B data, using different and often subjectively chosen thresholds for rejecting data outliers, thus hampering the comparability of different validation studies. This work gives insight into the calculation of the EE for the two receiver channels and reveals its limitations as a measure of the actual wind error due to its spatial and temporal variability. It is demonstrated that a precise error assessment of the Aeolus winds necessitates a careful statistical analysis, including a rigorous screening for gross errors to be compliant with the error definitions formulated in the Aeolus mission requirements. To this end, the modified Z  score and normal quantile plots are shown to be useful statistical tools for effectively eliminating gross errors and for evaluating the normality of the wind error distribution in dependence on the applied QC scheme, respectively. The influence of different QC approaches and thresholds on key statistical parameters is discussed in the context of the Joint Aeolus Tropical Atlantic Campaign (JATAC), which was conducted in Cabo Verde in September 2021. Aeolus winds are compared against model background data from the European Centre for Medium-Range Weather Forecasts (ECMWF) before the assimilation of Aeolus winds and against wind data measured with the 2  µm heterodyne detection Doppler wind lidar (DWL) aboard the Falcon aircraft. The two studies make evident that the error distribution of the Mie-cloudy winds is strongly skewed with a preponderance of positively biased wind results distorting the statistics if not filtered out properly. Effective outlier removal is accomplished by applying a two-step QC based on the EE and the modified Z  score, thereby ensuring an error distribution witha high degree of normality while retaining a large portion of wind resultsfrom the original dataset. After the utilization of the described QC approach, the systematic errors in the L2B Rayleigh-clear and Mie-cloudy winds are determined to be below 0.3 m s −1 with respect to both theECMWF model background and the 2  µm DWL. Differences in the random errors relative to the two reference datasets (Mie vs. model is5.3 m s −1 , Mie vs. DWL is 4.1 m s −1 , Rayleigh vs. model is 7.8 m s −1 , and Rayleigh vs. DWL is 8.2 m s −1 ) are elaborated in the text.

After the successful launch of Aeolus, which is the firstspaceborne wind lidar developed by the European Space Agency (ESA), on 22 August 2018, we deployed several ground-based coherent Doppler wind lidars(CDLs) to verify the wind observations from Aeolus. By the simultaneous windmeasurements with CDLs at 17 stations over China, the Rayleigh-clear andMie-cloudy horizontal-line-of-sight (HLOS) wind velocities from Aeolus inthe atmospheric boundary layer and the lower troposphere are compared withthose from CDLs. To ensure the quality of the measurement data from CDLs andAeolus, strict quality controls are applied in this study. Overall, 52simultaneous Mie-cloudy comparison pairs and 387 Rayleigh-clear comparisonpairs from this campaign are acquired. All of the Aeolus-produced Level 2B (L2B) Mie-cloudy HLOS wind and Rayleigh-clear HLOS wind and CDL-produced HLOS wind are comparedindividually. For the inter-comparison result of Mie-cloudy HLOS wind andCDL-produced HLOS wind, the correlation coefficient, the standard deviation,the scaled mean absolute deviation (MAD) and the bias are 0.83, 3.15 m s −1 , 2.64 m s −1 and − 0.25 m s −1 , respectively, while the y = a x slope, the y = a x + b slope and the y = a x + b intercept are 0.93,0.92 and − 0.33 m s −1 . For the Rayleigh-clearHLOS wind, the correlation coefficient, the standard deviation, the scaledMAD and the bias are 0.62, 7.07 m s −1 , 5.77 m s −1 and − 1.15 m s −1 , respectively, while the y = a x slope, the y = a x + b slope and the y = a x + b intercept are 1.00, 0.96 and − 1.2 m s −1 . It is found that the standarddeviation, the scaled MAD and the bias on ascending tracks are lower thanthose on descending tracks. Moreover, to evaluate the accuracy of Aeolus HLOSwind measurements under different product baselines, the Aeolus L2BMie-cloudy HLOS wind data and L2B Rayleigh-clear HLOS wind data underBaselines 07 and 08, Baselines 09 and 10, and Baseline 11 are compared against theCDL-retrieved HLOS wind data separately. From the comparison results, marked misfits between the wind data from Aeolus Baselines 07 and 08 and wind data fromCDLs in the atmospheric boundary layer and the lower troposphere are found.With the continuous calibration and validation and product processorupdates, the performances of Aeolus wind measurements under Baselines 09 and 10and Baseline 11 are improved significantly. Considering the influence ofturbulence and convection in the atmospheric boundary layers and the lowertroposphere, higher values for the vertical velocity are common in thisregion. Hence, as a special note, the vertical velocity could impact theHLOS wind velocity retrieval from Aeolus.

The realization of the European Space Agency's Aeolus mission was supported by the long-standing development and field deployment of the Atmospheric LAser Doppler INstrument (ALADIN) Airborne Demonstrator (A2D) which, since the launch of theAeolus satellite in 2018, has been serving as a key instrument for thevalidation of 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 DWL that was deployed in parallel on board the same aircraft shows that the retrieval refinementsconsiderably decrease the random errors of the A2D line-of-sight (LOS)Rayleigh and Mie winds from about 2.0 to about 1.5 m s −1 , demonstrating the capability of such a direct detection DWL. Furthermore, 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 Level 2B (L2B) Rayleigh-clear and Mie-cloudy horizontal LOS (HLOS) winds 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.

In August 2018, the European Space Agency (ESA) launched the first Doppler wind lidar into space, which has since then been providing continuousprofiles of the horizontal line-of-sight wind component at a global scale. Aeolus data have been successfully assimilated into several numericalweather prediction (NWP) models and demonstrated a positive impact on the quality of the weather forecasts. To provide valuable input data for NWPmodels, a detailed characterization of the Aeolus instrumental performance as well as the realization and minimization of systematic error sourcesis crucial. In this paper, Aeolus interferometer spectral drifts and their potential as systematic error sources for the aerosol and wind productsare investigated by means of instrument spectral registration (ISR) measurements that are performed on a weekly basis. During these measurements,the laser frequency is scanned over a range of 11  GHz in steps of 25  MHz and thus spectrally resolves the transmission curves of theFizeau interferometer and the Fabry–Pérot interferometers (FPIs) used in Aeolus. Mathematical model functions are derived to analyze themeasured transmission curves by means of non-linear fit procedures. The obtained fit parameters are used to draw conclusions about the Aeolusinstrumental alignment and potentially ongoing drifts. The introduced instrumental functions and analysis tools may also be applied for upcomingmissions using similar spectrometers as for instance EarthCARE (ESA), which is based on the Aeolus FPI design.