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During the first three years of 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 (Nov/Dec 2018) and AVATAR-E (Aeolus Validation Through Airborne Lidars in Europe, May/Jun 2019) were conducted in Europe and provided first insights in 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 September 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 km and 11000 km along the Aeolus measurement track, respectively. Based on these collocated measurements, statistical comparisons of Aeolus data with the reference lidar (2-μm DWL) as well as with in-situ measurements by the Falcon were performed to determine the systematic and random error 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 0.7m s-1 for both Rayleigh-clear and Mie-cloudy winds. The random error is shown to vary between 5.5 m s-1 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-μm 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 to investigate wind retrieval algorithm improvements for reprocessed Aeolus data sets. [ABSTRACT FROM AUTHOR]

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 of the Rayleighclear 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. 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. The spatial and temporal variability of the Rayleigh and Mie EE values, together with the inconsistency in the QC approaches, hampers the comparability of the results across different validation studies. 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 assimilation of Aeolus winds and against wind data measured with the 2-μm heterodyne-detection Doppler wind lidar (DWL) onboard 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 gross errors 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 with a high degree of normality while retaining a large portion of wind results from the original dataset. The same is true for the Rayleigh-clear winds, although the errors are more homogeneously distributed. After utilization of the described QC approach, the systematic errors of the L2B Rayleigh-clear and Mie-cloudy winds are determined to be below 0.3 m∙s-1 with respect to both the ECMWF model background and the 2-μm DWL. Differences in the random errors relative to the two reference datasets (Mie vs. model: 5.3 m∙s-1, Mie vs. DWL: 4.1 m∙s-1; Rayleigh vs. model: 7.8 m∙s-1; Rayleigh vs. DWL: 8.2 m∙s-1) are elaborated in the text. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In August 2018, the first Doppler Wind Lidar, developed by the European Space Agency (ESA), was launched on board the Aeolus satellite into space. Providing atmospheric wind profiles on a global basis, the Earth Explorer mission is expected to demonstrate improvements in the quality of numerical weather prediction (NWP). For the use of Aeolus observations in NWP data assimilation, a detailed characterization of the quality and the minimization of systematic errors is crucial. This study performs a statistical validation of Aeolus observations, using collocated radiosonde measurements and NWP forecast equivalents from two different global models, the ICOsahedral Nonhydrostatic model (ICON) of Deutscher Wetterdienst (DWD) and the European Centre for Medium-Range Weather Forecast (ECMWF) Integrated Forecast System (IFS) model, as reference data. For the time period from the satellite's launch to the end of December 2019, comparisons for the northern hemisphere (23.5-65° N) show strong variations of the Aeolus winds bias and differences between the ascending and descending orbit phase. The mean absolute bias for the selected validation area is found to be in the range of 1.8-2.3 m s-1 (Rayleigh) and 1.3-1.9 m s-1 (Mie), showing good agreement between the independent reference data sets. Due to lower representativeness, the random differences are larger for the validation using radiosonde observations compared to the model equivalent statistics. To achieve an estimate for the Aeolus instrumental error, the representativeness errors for the comparisons are determined, besides the estimation of the model and radiosonde observational error. The resulting Aeolus errors estimates are in the range of 4.1-4.4 m s-1 (Rayleigh) and 1.9-3.0 m s-1 (Mie). Investigations of the Rayleigh wind bias on a global scale show that in addition to the satellite flight direction and seasonal differences, the systematic differences depend on latitude. A latitude based bias correction approach is able to reduce the bias, but a residual bias of 0.4-0.6 m s-1 with a temporal trend remains. Taking additional longitudinal differences into account, the bias can be reduced further by almost 50 %. Longitudinal variations are suggested to be linked to land-sea distribution and tropical convection that influences the thermal emission of the earth. Since 20 April 2020 a bias correction scheme has been applied operationally in the L2B processor, developed by the Aeolus Data Innovation and Science Cluster (DISC). [ABSTRACT FROM AUTHOR]

Shortly after the successful launch of ESA's wind mission Aeolus, carried out by the European Space Agency, collocated airborne wind lidar observations were performed in Central Europe, employing the prototype of the satellite instrument, the ALADIN Airborne Demonstrator (A2D). Like the direct-detection Doppler wind lidar on-board Aeolus, the A2D is composed of a frequency-stabilised ultra-violet laser, a Cassegrain telescope and a dual-channel receiver to measure line-of-sight (LOS) wind speeds by analysing both Mie and Rayleigh backscatter signals. In the frame of the first airborne validation campaign after the launch still during the commissioning phase of the mission, four coordinated flights along the satellite swath were conducted in late autumn of 2018, yielding wind data in the troposphere with high coverage of the Rayleigh channel. Owing to the different measurement grids and viewing directions of the satellite and airborne instrument, intercomparison with the Aeolus wind product requires adequate averaging as well as conversion of the measured A2D LOS wind speeds to the satellite LOS. The statistical comparison of the two instruments with model wind data from the ECMWF shows biases of the A2D and Aeolus LOS wind speeds of -10.9 m s-1 and +1.6 m s-1, respectively, while the random errors are around 2.5 m s-1. The paper also discusses the influence of different threshold parameters implemented in the comparison algorithm as well as optimization of the A2D vertical sampling to be used in forthcoming validation campaigns. [ABSTRACT FROM AUTHOR]

Soon after the launch of Aeolus on 22 August 2018, the first ever wind lidar in space developed by the European Space Agency (ESA) has been providing profiles of the component of the wind vector along the instrument's line-of-sight (LOS) on a global scale. In order to validate the quality of Aeolus wind observations, the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt e.V., DLR) recently performed two airborne campaigns over Central Europe deploying two different Doppler wind lidars (DWL) on-board the DLR Falcon aircraft. The first campaign - WindVal III - was conducted from 5 November 2018 until 5 December 2018 and thus, still within the commissioning phase of the Aeolus mission. The second campaign - AVATARE (Aeolus Validation Through Airborne Lidars in Europe) - was performed from 6 May 2019 until 6 June 2019. Both campaigns were flown out of the DLR site in Oberpfaffenhofen, Germany. All together, 10 satellite underflights with 19 flight legs covering more than 7500 km of Aeolus swaths were performed and used to validate the early stage wind data product of Aeolus by means of collocated airborne wind lidar observations for the first time. For both campaign data sets, the statistical comparison of Aeolus data and the data of the reference lidar (2-µm DWL) on-board the Falcon aircraft shows enhanced systematic and random errors compared with the bias and precision requirements defined for Aeolus. In particular, the systematic errors are determined to be 2.1 m/s (Rayleigh) and 2.3 m/s (Mie) for WindVal III and -4.6 m/s (Rayleigh) and -0.2 m/s (Mie) for AVATARE. The corresponding random errors are determined to be 4.0 m/s (Rayleigh) and 2.2 m/s (Mie) for WindVal III, and 4.4 m/s (Rayleigh) and 2.2 m/s (Mie) for AVATARE. Potential reasons for those errors are analyzed and discussed. [ABSTRACT FROM AUTHOR]

The mixing layer height (MLH) is a measure for the vertical turbulent exchange within the boundary layer, which is one of the controlling factors for the dilution of pollutants emitted near the ground. Based on continuous MLH measurements with a Vaisala CL51 ceilometer and measurements from an air quality network, the relationship between MLH and near surface pollutant concentrations have been investigated. In this context the uncertainty of the MLH retrievals and the representativeness of ground-based in-situ measurements are crucial. We have investigated this topic by using data from the BAERLIN2014 campaign in Berlin, Germany, conducted during June and August 2014. To derive the MLH three versions of the proprietary software BL-VIEW and a novel approach COBOLT were compared. It was found that the overall agreement is reasonable if mean diurnal cycles are considered. The main advantage of COBOLT is the continuous detection of the MLH with a temporal resolution of 10 minutes and a lower number of cases when the residual layer is misinterpreted as mixing layer. We have calculated correlations between MLH as derived from the different retrievals and concentrations of pollutants (PM10, O3 and NOx) for different locations in the metropolitan area of Berlin. It was found that the correlations with PM10 are quite different for different sites without showing a clear pattern, whereas the correlation with NOx seems to depend on the vicinity of emission sources in main roads. In case of ozone as a secondary pollutant a clear correlation was found. We conclude that the effects of the heterogeneity of the emission sources, chemical processing and mixing during transport exceed the differences due to different MLH retrievals. Moreover, it seems to be unrealistic to find correlations between MLH and near surface pollutant concentrations representative for a city like Berlin, in particular when traffic emissions are dominant. Nevertheless it is worthwhile to use advanced MLH retrievals for ceilometer data, e.g. for the validation of chemical transport models. [ABSTRACT FROM AUTHOR]