Validation of Aeolus wind products above the Atlantic Ocean

In August 2018, the first Doppler wind lidar in space called ALADIN was launched on-board the satellite Aeolus by the European Space Agency ESA. Aeolus measures horizontal wind profiles :::::: profiles :: of ::: one ::::::::: horizontal ::::: wind ::::::::: component :::: (i.e. :::::: mainly ::: the :::::::: west-east :::::::: direction) : in the troposphere and lower stratosphere on a global basis. Furthermore, profiles of aerosol and cloud properties can be retrieved via the high-spectral-resolution lidar (HSRL) technique. The Aeolus mission is supposed to improve the quality of weather forecasts and the understanding of atmospheric processes. 5 We used the chance of opportunity to perform a unique validation of the wind products of Aeolus by utilizing the RV Polarstern cruise PS116 from Bremerhaven to Cape Town in November/December 2018. Due to concerted course modifications, six direct intersections with the Aeolus ground track could be achieved on the Atlantic Ocean, west of the African continent. For the validation of the Aeolus wind products, we launched additional radiosondes and used the EARLINET/ACTRIS lidar Polly for atmospheric scene analysis. The six analyzed cases proof :::: prove : the concept of Aeolus to be able to measure hor10 izontal wind speeds in the nearly West-East direction. Good agreements with the radiosonde observation :::::::::: observations could be achieved for both Aeolus wind products the winds observed in clean atmospheric regions called Rayleigh winds and the winds obtained in cloud layers called Mie winds according to the responsible scattering regime. Systematic and statistical errors of the Rayleigh winds were less than 1.5 m/s and 3.3 m/s, respectively, when comparing to radiosonde values averaged to the Aeolus vertical resolution. For the Mie winds, a systematic and random error of about 1 m/s was obtained from the 15 six comparisons in different climate zones. However, it is also shown that the coarse vertical resolution of 2 km in the upper troposphere which was set in this early mission phase two months after launch led to an underestimation of the maximum wind speed in the jet stream regions. Summarizing, promising first results of the first wind lidar space mission are shown and proof the concept of Aeolus for global wind observations.

 line 434: you could add: "in fact, Rayleigh-clear winds have proven more beneficial for NWP than Mie-cloudy winds". Added. Thanks!  line 436: "..... and random error of 3.3 m/s for the Rayleigh ...." This is very misleading as this value does not represent the usual STD but MAD, see comment above. So please translate this value to STD. Thanks for the hint! Done!  line 438: "Some known instrumental effects and calibrations have not yet been implemented in the retrieval algorithms" Rephrase to: "In the meantime discovered instrumental and calibration imperfections were not yet implemented in the retrieval algorithms used for the 2018 autumn data set" Done!  In this context, do you have plans to use reprocessed, unbiased, Aeolus data with the same radiosonde data set presented here? I would very much encourage the authors to write a followup paper, once the reprocessed data for the autumn 2018 period become available. If so, please mention in section 5. Yes, this is a good idea. We mentioned it now: "Once a final reprocessing has been taken place it could be worth to use the existent RV Polarstern data set to quantify the improvements of the algorithm updates." Minor comments / typos =======================  line 110: the data must be available within 3 hours=> the data must be available within 3 hours after measurement time (timeliness). By the way, this is not true for ECMWF who wait about 5 hours before they start there analysis run. This is valid for medium-range forecasts. Mesoscale meteo centers need the data within 3 hours for operational use. , thanks, good to know!  line 116: parameter => parameters , done  line129: The currently applied method by ESA is the use of the scattering ratio => The currently applied method by ESA is the use of the scattering ratio, which is determined as part of the L1B processing (ref) and used as input for the L2B processing. , done We thank reviewers 2 for his/her time and the valuable comments and suggestions. Please find below the response from us indicated in italic.

Thanks -changed!
 Line 34: It should be noted that Atmospheric Motion Vectors (AMVs) can also be computed by tracking features in the water vapor field. Thanks for this information, we added it to the text and added a new reference (Bormann, N., S. Saarinen, G. Kelly, and J. Thépaut, 2003: The Spatial Structure of Observation Errors in Atmospheric Motion Vectors from Geostationary Satellite Data. Mon. Wea. Rev.,131,[706][707][708][709][710][711][712][713][714][715][716][717][718])  Line 153: It isn't obvious to me that the Rayleigh_cloudy wind product is without value. I realize that the presence of aerosols complicates the wind retrieval in the Rayleigh channel, but I'm not aware that the Rayleigh_cloudy product is deemed totally useless. If the Aeolus project team has stated this then the authors should provide a reference. You are right as reviewer 1 has also mentioned and I have also used this wind type for investigations. We changed the text accordingly: "Two out of this four wind products, namely the Rayleigh_clear and the Mie_cloudy winds, are the main target for the operational use of Aeolus data in NWP"… "The Rayleigh_cloudy products may also deliver usable wind measurements, but contamination of Mie scattering need to be corrected first which is yet at an experimental stage. Thus, we will use only Rayleighclear and the Miecloudy product for our analysis"  Lines 157-162: Although references are provided, as a reader it would be nice to have a few sentences describing in general terms how the error threshold and validity flags are computed. We added some few sentences concerning that. But we think, the full explanation of the validity flag is not needed when the reference is given: "…These thresholds are chosen subjectively, based on the compromise between the number of observations that pass the quality control and the overall quality of the dataset ." …"The validity flag (de Kloe et al., 2016) considers the validity of the products. Several different technical, instrumental and retrieving checks account for this flag. ."  Line 200: Perhaps I missed it, but it would be useful to state in the text that because the Aeolus lidar beam is not nadir-pointing, the horizontal distance from the radiosonde to the Aeolus measurement volumes changes as a function of height as well as radiosonde movement. It's a simple and obvious point, but it can't hurt to note it. We have added this information accordingly: "As Aeolus is not pointing nadir but is taking measurements 35° off-nadir, the horizontal distance of the Aeolus observations to RV Polarstern is different for the different heights in the Aeolus wind profile. Also the radiosonde drifts along the wind direction, thus the distance to between the Aeolus measurements and the radiosonde changes during the ascent. The effect of both is illustrated…" Thanks for this advise. We reshaped all corresponding figures accordingly so that colors are not needed anymore and we hope that they are now more clearly readable.
 Line 247: The inability of Aeolus to characterize the maximum wind under strong shear conditions near the tropopause is useful to point out. However, it should probably be noted that this isn't an error in the Aeolus measurement, but rather an averaging effect that obscures an important parameter. You are right, we've added: "This is in principle no measurement error of Aeolus."  Line 253: Changing the range bins on Aeolus to 1 km has potentially negative consequences on the measurement in that it reduces the number of photons available, thus increasing the random error. The authors might want to comment on whether the Aeolus team chose to accept this increase in random error or compensate for it by, e.g., reducing the horizontal resolution. This is a good point. But as we are "only" a Cal/Val team and not any decision-making body, we would not like to comment too much on these issues. Nevertheless, we've added: "…but accepting the drawback of an increased random error."  Figure 5: it would be nice to provide a N-S reference on the plots.
Thanks, we have added this to the plots!  Line 353: A sentence explaining why the authors prefer to use MAD as the statistic to represent the random error would be useful. As also raised by reviewer 1, we meanwhile provide the scaled MAD as an indicator for the random error. This is explained in the text and also that the MAD and thus also the scaled MAD is less sensitive to outliers in contrast to the standard deviation: "The median absolute deviation (MAD) of the distribution is used to calculate the random error of the Aeolus wind observations Witschas et al., 2020) Table 2 Caption: The caption seems to be defining medium absolute deviation (MAD) as MADrandom error, which doesn't make sense. You are right. Reviewer 1 also raised this point. Thus, we have added a column for the scaled MAD which is representative for the random error and rephrased the caption accordingly. mainly ::: the :::::::: west-east :::::::: direction) : in the troposphere and lower stratosphere on a global basis. Furthermore, profiles of aerosol and cloud properties can be retrieved via the high-spectral-resolution lidar (HSRL) technique. The Aeolus mission is supposed to improve the quality of weather forecasts and the understanding of atmospheric processes. 5 We used the chance of opportunity to perform a unique validation of the wind products of Aeolus by utilizing the RV Polarstern cruise PS116 from Bremerhaven to Cape Town in November/December 2018. Due to concerted course modifications, six direct intersections with the Aeolus ground track could be achieved on the Atlantic Ocean, west of the African continent.
For the validation of the Aeolus wind products, we launched additional radiosondes and used the EARLINET/ACTRIS lidar Polly XT for atmospheric scene analysis. The six analyzed cases proof :::: prove : the concept of Aeolus to be able to measure hor-10 izontal wind speeds in the nearly West-East direction. Good agreements with the radiosonde observation :::::::::: observations could be achieved for both Aeolus wind products -the winds observed in clean atmospheric regions called Rayleigh winds and the winds obtained in cloud layers called Mie winds according to the responsible scattering regime. Systematic and statistical errors of the Rayleigh winds were less than 1.5 m/s and 3.3 m/s, respectively, when comparing to radiosonde values averaged to the Aeolus vertical resolution. For the Mie winds, a systematic and random error of about 1 m/s was obtained from the 15 six comparisons in different climate zones. However, it is also shown that the coarse vertical resolution of 2 km in the upper troposphere which was set in this early mission phase two months after launch led to an underestimation of the maximum wind speed in the jet stream regions. Summarizing, promising first results of the first wind lidar space mission are shown and proof the concept of Aeolus for global wind observations. And Ranging) instrument on a European satellite. It is also the first space-borne instrument capable of measuring vertical profiles of wind on a global basis. Next to wind measurements, aerosol properties can be obtained as a spin-off product (Ansmann et al., 2007;Flamant et al., 2008) via the High Spectral Resolution Lidar (HSRL) technique (Wandinger, 1998;Eloranta, 2005), which is a space-borne novelty as well. Thus, one of the mission goals is to proof the concept of the new technology in space.
For precise weather forecast, the numerical weather prediction (NWP) models rely on the data assimilation of worldwide 30 meteorological observations. But the global meteorological observing system does not provide equally distributed wind observations in time and space. The global and vertical direct wind observations that are assimilated at the European Centre for Medium-Range Weather Forecasts (ECMWF) in late 2016 (ECMWF, 2018) are mainly made by aircrafts, radiosondes, and Atmospheric Motion Vectors (AMV). AMV describe the method of observing the movement of objects (like clouds :: or ::::: water ::::: vapor :::: fields) from space and deriving the wind velocity from its movement . :::: (e.g., :::::::::::::::::: Bormann et al. (2003) :: ). But the coverage :: of 35 ::::: AMV in the lower stratosphere is poor as the AMV method only provides wind information in the cloudy troposphere for the uppermost cloud level and there are only few aircraft and radiosonde measurements in the lower stratosphere. Furthermore, the main input of aircraft measurements is obtained in Europe and the USA and is not globally distributed. The global meteorological observing system is therefore suffering from a shortage of observation ::::::::::: observations in specific regions, especially in the Southern Hemisphere, in the lower stratosphere, and over the Oceans. The aim of Aeolus is to fill these gaps by providing 40 global horizontal wind profiles in altitudes from 0 km to 30 km, ready for data assimilation in NWP models (Horányi et al., 2015a, b).
Within the German initiative EVAA (Experimental Validation and Assimilation of Aeolus observations, e.g., Baars et al. of EVAA is to validate the wind and aerosol products of Aeolus and to quantify the benefits of these new measurements for weather forecasting by assimilation experiments. As one part of these activities, the regular participation of TROPOS on RV Polarstern (Knust, 2017) cruises within the OCEANET project (Macke et al., 2010;Kanitz et al., 2013;Rittmeister et al., 2017;Bohlmann et al., 2018) offered the unique 50 opportunity to perform ground-based validation above the Atlantic Ocean where only few observational data is available. The Polarstern cruise PS116 from Bremerhaven, Germany to Cape Town, Republic of South Africa, took place from 10 November 2018 to 11 December 2018 (Hanfland and König, 2019) shortly after the launch of the satellite. Starting in the northern mid-latitudes and ending in the southern subtropical region at a latitude of -33.92 • , PS116 covered the northern mid-latitude region with frequent westerly winds, the subtropical jet stream region, the trade winds region, the Inter-Tropical Convergence Zone (ITCZ), and finally ended up in the subtropical region around Cape Town.
The wind validation could be realized using radiosonde launches provided by the German Meteorological Service DWD on RV Polarstern (Schmithüsen, 2019). We also utilized the multiwavelength-Raman-polarization lidar Polly XT Baars et al., 2016) in order to characterize the atmospheric state above RV Polarstern which is part of the European Infrastructure EARLINET/ACTRIS (European Aerosol Research Lidar Network/European Research Infrastructure for the 60 observation of Aerosol, Clouds and Trace Gases).

Wind lidar mission Aeolus
In 1999, ESA has chosen ::::::: selected the Atmospheric Dynamics Mission (ADM, Stoffelen et al. (2005)) as the 2 nd Earth Explorer Core mission. The name Aeolus was inspired by the keeper of the wind in Greek mythology (Ingmann and Straume, 2016).
ALADIN, the instrument on board, is a High Spectral Resolution (HSR) elastic backscatter lidar with a Nd-YAG laser operating at a wavelength of around 355 nm Reitebuch, 2012;Ingmann and Straume, 2016;Lux et al., 2020;Witschas et al., 2020). The laser pulses are circularly polarized and are emitted with a frequency of 50.5 Hz. The wind profiles are obtained from backscattering processes of the laser light pulses at moving air molecules and particles (Stoffelen et al., 2006;Tan et al., 2008;Reitebuch, 2012;. The signals are separately detected by two different receiver channels, the Rayleigh channel for backscattering from molecules and the Mie channel for backscattering from particles.

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As a consequence, two independent wind measurements can be obtained. Furthermore, it gives the possibility to subsequently perform aerosol measurements, providing the particle extinction and the particle backscatter coefficient independently (Flamant et al., 2017;Ansmann et al., 2007;Flamant et al., 2008;Martinet et al., 2018;Flament et al., 2019).
Aeolus has a weekly repeating, polar-sun-synchronous orbit with an inclination of 97 • and a mean altitude of 320 km (Kanitz et al., 2019a;. Besides in strong convection cases, the vertical wind velocity is small compared to the horizontal wind. Thus, the vertical 80 wind component is neglected for calculating the horizontal wind speed from the Aeolus LOS observations. Furthermore, the LOS is orthogonal to the flight direction in order to minimize the effect of the satellite velocity on the wind measurements. The orbit is aligned such that Aeolus flies along the day/night border facing towards the night side to minimize the solar background radiation (Kanitz et al., 2019a). Thus, the overpasses are either in the morning (descending orbit) at around 6 am or in the evening (ascending orbit) at around 6 pm local time. Passing from North to South in the morning, Aeolus' viewing direction 85 has an azimuth angle of around 100 • . This leads mainly to a measurement of the horizontal West-East wind component, having a positive sign for easterly winds along the HLOS. Consequently, the sign is vice versa for the Aeolus track from South to North, having an azimuth angle of around 260 • .
For the Aeolus mission, the accumulation of the return signal of 19 outgoing laser pulses is defined as one measurement and corresponds to a horizontal length of around ≈ 3 km. One observation is the average of several measurements and is aimed to be about 30 measurements ≈ 87 km horizontal resolution for Rayleigh wind observations. The number of measurements included in one observation can be modified, depending on the desired integration length. The receiver has 24 vertical range bins and wind profiles can be obtained between 0 km and 30 km with a vertical resolution between 250 m to 2 km (Reitebuch et al., 2014).
Aeolus, i.e., ALADIN, is able to retrieve wind retrievals from Doppler shift at particles, these are the so-called Mie winds, 95 but also in clean atmosphere due to the Doppler shift at molecules, these are the so-called Rayleigh winds. The technique onboard the satellite and the respective algorithms to retrieve the wind are described in Stoffelen et al. (2006); Andersson et al. Products of Aeolus are delivered at several data levels (Reitebuch et al., 2014;Ingmann and Straume, 2016;Flamant et al., 100 2017). For the end user, only Level 2 is of interest on which all necessary calibration and instrument correction ::::::::: corrections have been performed. The calibrated and fully processed HLOS wind is delivered in the Level 2B data Ingmann and Straume, 2016). This is the main wind product of Aeolus. There is also Level 2C data, which is vector wind data, resulted from ECMWF model analysis after the assimilation of Level 2B profiles. In Level 2A, the aerosol and cloud spin-off products (optical properties) are delivered (Ansmann et al., 2007;Flamant et al., 2008Flamant et al., , 2017Flament et al., 2019), which will 105 not be discussed in this paper.
The observational requirements (Ingmann and Straume, 2016) for the Aeolus Mission are that the vertical resolution shall achieve 500 m in the Planetary Boundary Layer (PBL), 1 km in the troposphere, and 2 km in the lower stratosphere. The requirements for the horizontal integration length per observation depends on the measurement type and altitude. The precision of the HLOS component is aimed to be 1 m/s within the PBL, 2.5 m/s for the troposphere, and 3 m/s for the lower stratosphere.

Data set and Methodology
For the validation of the Aeolus wind products, the Level 2B is the product of choice for comparison to the radiosonde measurements. These are the fully calibrated and processed HLOS winds ready for data assimilation in NWP models. The 115 output of the product includes different classifications and quality parameters which need to be chosen correctly. The use of these parameter ::::::::: parameters is described in the following:

Atmospheric classification
The Level 2B product provides four separated wind profiles for one atmospheric scene according to the atmospheric classification performed in the processor chain . These four wind "types" are:

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-Rayleigh clear : Wind derived in atmospheric regions without any particle backscatter, thus in clear sky using the Rayleigh methodology, environment using the Rayleigh methodology, -Mie clear : Wind derived in atmospheric regions with zero particle backscatter using the Mie methodology. As in clear sky 125 condition no Mie wind should be detectable, this is only possible if the classification failed to detect particle backscatter, -Mie cloudy : Wind derived in atmospheric regions with non-zero particle backscatter using the Mie methodology.
For this, a predefined scattering ratio threshold value as a function of altitude is used. If the scattering ratio is higher than the threshold value, particle scattering is considered to be dominant. Below the threshold, molecular scattering only is assumed.
The range-bins assigned to the same classification type are accumulated within the corresponding observation. This accumu-135 lation of the measurements improves the signal-to-noise-ratio and provides a large-scale wind observation which is ready for the NWP data assimilation . If, for example, a cloud layer exists in the first 21 km of the observation ::::::::: observation, the Mie cloudy wind product considers only the measurements of these first 21 km. As this procedure is not only applied to the profiles but for each vertical range bin 145 individually, the coordinates of the Aeolus observations profiles given at a certain range bin can be different. While, e.g., at 4 km altitude a cloud is observed for the first 21 km, another one is observed at 7 km altitude in the last 30 km of the 87 km horizontal path. Then the coordinates given for the Rayleigh cloudy and Mie cloudy winds at 4 km altitude are the mean coordinates of the first 21 km, while for 7 km height, the mean coordinates of the last 30 km are used.
To make it even more difficult, in principle, the Mie and Rayleigh wind observations can have a different horizontal resolu-150 tion. In this work, however, we analyzed early-mission data obtained shortly after launch during the commissioning phase of Aeolus, and at this time the horizontal resolution for both, Rayleigh and Mie winds, was equal and about 87 km. As Mie cloudy winds benefit from strong backscatter at cloud particles, the horizontal resolution is meanwhile increased to 12 km due to the significantly higher signal-to-noise ratio of this "wind type". The Rayleigh horizontal resolution is, however, kept at 87 km.

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Hot pixel ::::: pixels During the commissioning phase of Aeolus, it was noticed that pixel ::::: pixels : with an increased dark current occurred in the memory zone of both ACCD (Accumulation Charge Coupled Device) in the detector unit of ALADIN Kanitz et al., 2019b). These pixel ::::: pixels are called hot pixel and their increase :::: pixels :::: and :::: their :::::::: increased : dark current can have a changing magnitude with time. As no correction procedure was available at the early mission period we focus on, we skipped 175 all height bins at which a hot pixel occurred, as they significantly bias the Aeolus wind and aerosol products. For our analyzed data period, these are range bins 2, 13, 16, and 24 of the Mie products and range bins 5, 11, and 15 of the Rayleigh products (note that according to ESA's nomenclature, range bin 1 is the highest and range bin 24 the lowest of the profile). It is worth noticing that meanwhile a hot pixel correction is in place for Aeolus data since 14 June 2019.

Observation geometry 180
As Aeolus provides only the wind along the HLOS, which is mainly the west-east wind component, the radiosonde measurements are projected to the HLOS of Aeolus using the following formula: v RS describes the horizontal wind velocity and ϕ RS the wind direction measured with the radiosonde. ϕ Aeolus is the azimuth angle of Aeolus, which is obtained from the Level 2B data and differs depending on range-bin and global position.

Aeolus Validation
The ship-borne validation took place during the RV Polarstern cruise PS116 (10 November 2018 to 11 December 2018) from Bremerhaven, Germany to Cape Town, Republic of South Africa (Hanfland and König, 2019). Figure   date, the location of RV Polarstern during the RS launch, the launch time, the time of the exact Aeolus overpass, as well as the distance between the radiosonde and the closest Aeolus wind profile is given. ::: The ::: last :::::: column ::::::: indicates :::::: whether :::::: Aeolus ::: had ::: an :::::::: ascending :::: node ::::: during :::: local :::::: evening, :: or :: an ::::::::: descending :::: node ::::: during :::: local ::::::: morning.  Also, on-board RV Polarstern was the portable multiwavelength-Raman-polarization lidar Polly XT of the OCEANET facility . With its setup, aerosol and cloud properties can be classified by shape, size, and absorption behaviour 210 . The observations with this EARLINET/ACTRIS lidar will be used to characterize the atmospheric state above RV Polarstern. Figure 2 shows the temporal evolution of the attenuated backscatter coefficient (calibrated rangecorrected signal) to get an impression of the atmospheric scenery for the time around overpass at 0630 UTC. A cloud layer at around 2 km was observed exactly during the Aeolus overpass (red rectangle). The lidar could not penetrate this optically thick cloud. Below this cloud, the marine boundary layer (BL) was located as indicated by moderate backscattering (green colours). the lidar. Having a look at the period after the Aeolus overpass without cloud occurrence (after ca. 0725 UTC), an aerosol layer up to around 4 km is visible (greenish-bluish colours).
This :::::: ascent. ::: The ::::: effect :: of :::: both : is illustrated for this case study in Fig. 3 for the two closest Rayleigh (green and blue) and Mie (purple and cyan) observation profiles. While the horizontal distance to the Mie cloudy profiles varies between 10 km and 55 km in the lower 5 km (remember the accumulation of measurements within one observation as discussed above), the distance to the Rayleigh clear profile has only minor changes and is on average as indicated in the legend of Fig. 3. The distance changes 225 are not only caused by the radiosonde drift, but in particular because of the Aeolus classification algorithm as discussed above. Figure 4 shows the HLOS wind velocity profiles measured by the radiosonde (red) launched for this overpass and the two closest Aeolus Rayleigh clear (green and blue) and Mie cloudy profiles (magenta and cyan). Figure 4a provides the radiosonde profile with its highest vertical resolution while in Fig. 4b, the vertical resolution of the radiosonde measurements is aggregated  wind velocity and 1 • for the wind direction (Dirksen et al., 2014). Even though this reference considers the Vaisala radiosonde type RS92 and not RS41, which was used on RV Polarstern, there is no significant difference in the uncertainty, as both 235 radiosonde types are based on the same technique to derive wind velocity and direction (Jensen et al., 2016).
Regarding the Rayleigh clear profiles, a good agreement was found for the winds between 4 km and 6 km while a positive bias 255 (systematic error) in the region between 7.5 km to 12 km was observed for the two closest observations. Above 12 km, a good agreement is found, considering also the extent of the range-bins at this altitude of about 2 km. Below 4 km, no measurements are available due to the low signal-to-noise ratio and the cloud layer at 2.5 km. Summarizing, the Rayleigh clear winds follow well the shape of the wind profile from 4 km to 20 km.
Nevertheless, as can be seen in Fig. 4a, the maximum wind velocity occurs just below the tropopause at around 15 km, 260 having an opposite direction (westerly winds) than in the lower troposphere (easterly winds). A maximum absolute wind velocity higher than 25 m/s was observed in this height region according to the high resolved radiosonde profile. However, the Rayleigh clear wind measurements of Aeolus are not able to detect such high wind speeds. :::: This : is :: in :::::::: principle ::: no ::::::::::: measurement :::: error :: of ::::::: Aeolus. The reason for that is the low :: the ::::::::::: disagreement :: is ::: the :::::: coarse range resolution of the Aeolus measurements in the higher troposphere/lower stratosphere at this time of the mission as can been seen in Fig. 4b. Here it gets obvious, that the 265 resolution is simply too low ::::: coarse : in order to recognize the strong wind velocity in a vertically narrow atmospheric layer. In this height region, the high-resolution radiosonde wind speed (Fig. 4a) is about 8 m/s higher than compared to the radiosonde velocity aggregated to the range-bin setting of Aeolus (Fig. 4b).
At that time of the mission, i.e., : shortly after the launch, the Aeolus range-bins had a resolution of 250 m up to 2 km height to perform necessary ground echo characterizations. Above this height, the vertical resolution was 1 km up to the altitude of 270 13 km and then set to 2 km for higher altitudes as a consequence of the limitation of 24 range bins in total. Thus, considering the vertical binning, the Aeolus observations are correct, while they miss important information on the tropical jet stream speed as impressively shown here in this one example. As a consequence, the range-bins were changed to a resolution of 1 km up to an altitude of 19 km on 26 February 2019 to provide the NWP models a much more detailed wind information in a height region very important for weather forecast ::: but :::::::: accepting ::: the :::::::: drawback :: of ::: an :::::::: increased :::::: random ::::: error. Noticeable is the good coverage of the Rayleigh clear winds above 3-4 km above sea level (a.s.l.) along the whole track.

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The pronounced tropical jet with westerly winds as observed by the radiosonde is seen in all Rayleigh clear observations as prominent feature (reddish colours). In the lower troposphere, easterly winds prevail (bluish colours) throughout the whole region. Mie cloudy winds are available only in the lowermost 3 km where low-level clouds occurred and sporadically at high altitudes most probably due to the occurrence of cirrus clouds. The Mie cloudy winds show steady easterly winds at the cloud layer at around 2.5 km in agreement with the Rayleigh clear winds as discussed above. However, a short statement is needed for the obviously strong westerly winds just above these easterly winds at an altitude of 3 km. These westerly winds are simply an artefact caused by the hot pixel at range bin 13 which was left out in the analysis presented in Fig. 4 but is visualized by VirES in Fig. 5. Thus, these wind measurements at this altitude :::::::: (indicated ::: by :::::: reddish ::::: colors :::: just ::::: above ::: the ::::: bluish :::::: colors :: in ::: the :::::::: lowermost ::::::: profile) should be neglected until the hot pixel correction is in place.

Case study 2: 6 December 2018 290
The second case study discussed in this paper is from 6 December 2018 when RV Polarstern was west of Namibia (see Fig. 1) and thus already in the subtropical region. The radiosonde was launched around 50 km away from the Aeolus ground track.
The lidar observations shown in Fig. 6 indicate no clouds at all but aerosol up to 800 m around the overpass at about 1750 UTC. Low clouds with a bottom height at around 750 m a.s.l. were observed before 1500 UTC and after 2030 UTC.
These clouds might be the reason for the two obtained Mie cloudy observations ::::::::::: observations below 1 km a.s.l. as presented in 295 Fig. 7. As described above, if during the 87 km horizontal accumulation distance some measurements are classified as cloudy, a valid Mie cloudy wind is obtained for the whole observation. Thus, considering the distance of RV Polarstern to the Aeolus ground track and the Aeolus horizontal resolution together with the cloud occurrence before and after the overpass as detected with the lidar, it is quite obvious that clouds were partly existent in the Aeolus observational domain and could be used for the Mie wind retrieval.

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The winds observed with the Mie methodology in these as cloudy classified atmospheric regions agree perfectly with the HLOS wind obtained from the radio sounding. Also, the vertically aggregated radiosonde velocities as shown in Fig. 7b do match to the Mie cloudy winds due to the relatively high vertical resolution of Aeolus in the lowermost 2 km of the atmosphere.
Valid wind observations retrieved with the Rayleigh methodology are available for altitude ranges between 4 km and 21 km having its maximum at an altitude of around 10.5 km. As Polarstern crossed the Aeolus ground track in the evening, the 305 positive wind speed values in the Fig. 7 indicate westerly winds. Again, the issue concerning the low resolution of Aeolus at higher altitudes is obvious in this comparison. Even though the low resolved radiosonde measurements fit with the Aeolus ones, the high resolved radiosonde profile (Fig. 7a) shows much more and stronger changes in wind velocity, e.g., at 17 km height, compared to the low resolution one (Fig. 7b).
It is interesting to note that in case of the Rayleigh clear observations, the profile with further distance (blue line) to RV

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Polarstern is in better agreement with the radiosonde measurements than the closer one (green line). Especially between 7 km and 12 km it is very similar to the radiosonde profile. Figure 8 shows the wind profiles along the Aeolus track close to RV Polarstern. There was a region with strong winds in higher altitudes just south of the research vessel -namely the subtropical jet. Obviously, there was a significant horizontal (north-south) gradient in high-altitude winds at the time of the overpass as  seen in Fig. 8. Nevertheless, the profile represented by the green line in Fig. 7 was measured more southward along the Aeolus 315 track than the "blue profile". As the radiosonde drifted about 20 km to the north during its ascent, it is a logical consequence that the Aeolus profile measured more northerly (blue) fits better to the radiosonde. Therefore, this analysis confirms that Aeolus is well able to capture horizontal wind gradients at several heights with its Rayleigh and also Mie technique (see Fig. 8b).

Case studies 3-6
In order to provide a final overview of the validation cases obtained during the cruise, four remaining overpass cases are 320 presented in Fig. 9. The remaining cases are less favourable than the already presented ones due to larger distances in time and space between the research vessel location and the Aeolus observations. But they are still very valuable for the Aeolus validation in an area where almost no ground-truth observations exist. In addition, they are considered for a statistical analysis presented below.
On 27 November 2018 (Fig. 9a), the overpass region was exactly inside the ITCZ, where enhanced vertical turbulence can the Mie cloudy observations ::::::::::: observations deviate significantly from the radiosonde observations (at around 9 km a.s.l.). The Rayleigh clear winds agree in shape with the radiosonde observation but the Aeolus observation at 14 km differs significantly from the radiosonde. From the available information, it is not possible to conclude if this strong wind speed change within a horizontal distance of 150 km is an atmospheric feature or if there are issues in the Aeolus wind retrievals. For these reasons, we excluded this case from the statistical analysis presented below.

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On 2 December 2018 (Fig. 9b), the mean distance between the radiosonde and the Aeolus observations was 100 km to 122 km. The radiosonde profile shows a stronger vertical fluctuation of the horizontal wind velocity and direction than in the previously discussed case studies. Especially between 12 km to 16 km, large and fast changes of wind direction and thus the projected HLOS were observed by the radiosonde. Due to its large ::::: coarse vertical resolution, Aeolus is only partly able to detect these rapid changes. Nevertheless, considering the vertical heterogeneity in the wind field, the agreement is acceptable 340 for most Rayleigh clear wind observations. Aeolus-derived winds follow mostly the shape and magnitude of the radiosonde winds except for a large deviation at around 4 km (green profile). The reason for that is unclear. Probably the atmospheric classification of Aeolus was not working properly for this scene and thus cross-talk of cloud signals could have led to the deviation in the derived Rayleigh clear winds. The observed Mie cloudy winds, however, agree all well on this day. Mie cloudy winds were observed at around 1 km where partly stratiform clouds were present according to the lidar measurements (not 345 shown). Mie cloudy winds could also be retrieved very close to the surface and agree very well with the radiosonde observation taking into account the estimated uncertainty and the distance between the two measurements.
On 3 December 2018 (Fig. 9c), the mean distances between the closest Aeolus profiles and the radiosonde location were less than 100 km. A good agreement between the two measurements was achieved on this day. For the last point of intersection on 10 December 2018 (Fig. 9d), RV Polarstern was more than 100 km away from the Aeolus track. Like in the second case study, 350 the Rayleigh clear profile which was further away is partly in better agreement with the radiosonde profile than the closer one.
Also, the small-scale structures in wind speed could not be resolved by Aeolus as discussed above. Nevertheless, within the uncertainty range, a satisfying agreement was achieved for the two last case studies.

Statistical analysis
In this subsection, the performed comparisons are statistically analyzed. The offset between Aeolus and the radiosonde -the 355 so-called bias -which represents the systematic error of the Aeolus wind measurements is of special interest. For this analysis, the Aeolus wind observation values are plotted against the corresponding values of the radiosondes averaged to the Aeolus height resolution (as discussed above) to focus on the instrumental behaviour of Aeolus only. We hereby assume that the atmospheric variability between the two measurements will not cause a bias but only increases noise, i.e., the random error.
Nevertheless, the validation case of 27 November 2018 is not included in the statistics due to the large horizontal distance of 360 the two measurements together with the fact that the observations were taken directly inside the ITCZ.
When calculating the mean value of this distribution, one gets 1.52 m/s as bias for the Rayleigh clear wind observations. If one uses the median of the distribution for the bias calculation, one gets a bias of 1.47 m/s and thus a little less than as calculated from the mean. If one forces the linear regression to have a slope of 1, the retrieved offset is practically the same as the mean 375 deviation between the radiosonde and Aeolus. As this is expected for a Gaussian distribution, it confirms the ::: one ::::: could ::::::: assume, :: in ::::::::: accordance :::: with ::: the ::::: shape :: of ::: the :::::::::: distribution :::::: shown :: in ::: Frequency distribution of the difference between Mie cloudy and radiosonde wind speeds for the same data set. Radiosonde data is aggregated to the Aeolus vertical resolution and projected to the HLOS of Aeolus.

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Considering that only five radiosonde launches were used, the observed biases are in agreement with other CAL/VAL teams of this mission (ESA, 2019b): At the first Aeolus CAL/VAL workshop, independent comparisons (not publicly accessible) of several CAL/VAL teams showed global biases in the range of <1 m/s up to 3.3 m/s using different observation periods and NWP models (e.g. Rennie and Isaksen (2019)).  . Meanwhile, the calibration has been updated more regularly and instrument drifts are under investigation to be corrected in future processor updates. For further details, the reader may refer to the stated references. Khaykin et al. (2020) analyzed one wind profile of Aeolus with the Doppler lidar at Observatoire de Haute-Provence and found a good agreement between the two measurements. But below 5 km a.g.l. (above ground level), a stronger deviation was 435 observed which was considered to be caused by horizontal :::::::::: atmospheric : heterogeneity. In our study, however, we could almost never observe any Rayleigh clear wind profile below 4 km which prohibits the discussion of this issue raised by Khaykin et al. (2020). Nevertheless, it could already be an indicator that the laser energy which has been lower than expected (Kanitz et al., 2019b;, leads to less accuracy and therefore more invalid wind observation :::::::::: observations close to the ground (further away from the lidar on-board Aeolus)

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To summarize, the statistics obtained during cruise PS116 with RV Polarstern, even if only consisting of five comparisons with radiosondes, do agree well with findings from other CAL/VAL teams and give an insight of the Aeolus performance shortly after launch -thus still in the commissioning phase. It also shows that Aeolus is able to measure horizontal wind speeds from space and that the retrieved data is reliable within a given uncertainty range and thus ready for data assimilation.
First data assimilation experiments have already shown a positive impact, e.g. as announced by ECMWF (ECMWF, 2019a, b).

Conclusions
Wind products from the first wind lidar in space, ALADIN, on-board the European satellite Aeolus were validated against wind profiles obtained from radiosonde launches on-board the German RV Polarstern during the cruise PS116 in Autumn 2018 across the Atlantic Ocean. Six points of intersection were reached within a radius of 150 km for which additional radiosondes could be launched in time. These unique validation measurements across the Atlantic Ocean are a valuable contribution to the 450 -until now -mainly model-based validations of Aeolus in that region of the Earth.

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With the analysis of dedicated case studies, it was shown, that Aeolus is able to measure accurately atmospheric wind profiles of the nearly west-east wind component. Due to its HSRL technique, Aeolus is able to measure wind speed in, both, clear, particle free atmospheric regions and in regions where clouds or dense aerosol layers occur. The corresponding products are the Rayleigh clear and Mie cloudy winds, respectively.

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Aeolus, i.e., ALADIN, is able to obtain the shape of the wind profile and the magnitude of the wind speed with sufficient accuracy taking into account also the representativeness error introduced by the horizontal distance between the radiosonde and Aeolus ground track and the low horizontal (87 km) and vertical resolution (0.5-2 km) of Aeolus. A proof of concept of the HSR Doppler wind lidar technique in space to measure global wind profiles was therefore already demonstrated. Nevertheless, it was also shown that the height resolution which was set during the commissioning phase was not sufficient to capture 460 the maximum wind speeds in relatively thin strong-wind regions, here discussed in terms of the example of the ::::: events ::: of ::::: strong ::::::: vertical ::::::::: wind-shear ::::: such :: as :::: near ::: the : tropical jet stream. The coarse resolution of Aeolus of 2 km at altitudes above 13 km caused a significant underestimation of the maximum wind speed. Thus, considering the vertical binning, the Aeolus observations were correct, but important information on the tropical jet stream speed were missing. As a consequence, the range-bins were changed to a resolution of 1 km up to an altitude of 19 km on 26 February 2019 to provide the NWP models a 465 much more detailed wind information in such an important atmospheric region.
It has also been discussed that Rayleigh clear winds in the free troposphere have a larger offset, i.e. systematic error, than the corresponding Mie cloudy winds leading to a slight overestimation of the true HLOS wind speed. Mie cloudy winds are only available at atmospheric regions with clouds, but the comparison to the radiosonde profiles shows that the Mie cloudy winds were very accurate, with lower systematic and random errors than the Rayleigh clear winds, and should be used when available 470 in favour of the Rayleigh clear winds. Nevertheless, especially the Rayleigh clear winds are a special highlight of the Aeolus mission as they could close a gap for clear air observations in the global atmospheric observing system which are not covered by atmospheric motion vectors obtained in cloudy regions only. :: In :::: fact, :::::::::::: Rayleigh-clear :::::: winds :::: have :::::: proven ::::: more :::::::: beneficial ::: for :::: NWP :::: than :::::::::: Mie-cloudy ::::: winds ::: so :: far ::::::::::::::::::::::: . : The statistical analysis based on only five radiosondes reveals a good performance of Aeolus in this early phase of the 475 mission having a systematic error (bias) of about 1.5 m/s and random error of 3.3 ::: 4.84 m/s :::::: (scaled :::::: MAD) for the Rayleigh clear winds. The Mie cloudy winds were more accurate with about 1 m/s systematic error and a random error of 1 :::: 1.58 m/s. This is yet higher than claimed in the mission requirements but it should be noted that the data used for validation here is not the final wind data set of Aeolus. Some known :: in ::: the ::::::::: meanwhile :::::::::: discovered instrumental effects and calibrations have not yet been ::::::::::: imperfections ::::: were ::: not implemented in the retrieval algorithms ::: used ::: for ::: the ::::::: autumn :::: 2018 :::: data ::: set. The main challenges 480 of the Aeolus mission are the occurrence of hot pixel, varying biases ::::: pixels, ::::::: varying :::::::: telescope ::::::::::: temperatures, the laser energy development, and the lower atmospheric return signal resulting in a larger Rayleigh random error. ESA is steadily working on the improvements of the wind retrievals and processor updates. Several reprocessing steps of the existing data will take place in the future delivering data with even higher accuracy than the current data set delivered in the commissioning phase of Aeolus. ::::: Once : a :::: final ::::::::::: reprocessing ::: has :::: been ::::: taken ::::: place :: it ::::: could :: be ::::: worth :: to ::: use ::: the ::::::: existent ::: RV ::::::::: Polarstern :::: data :: set ::: to ::::::: quantify To summarize, the validation efforts performed with radiosondes launched during cruise PS116 of RV Polarstern give an insight of the Aeolus performance shortly after launch and thus still in the commissioning phase of Aeolus. It shows that Aeolus is able to measure horizontal wind speeds from space and that the retrieved data is reliable within a given uncertainty range and is usable for data assimilation. As announced by ECMWF (ECMWF, 2019b), first data assimilation experiments have already 490 shown a positive impact. For such experiments, the systematic errors obtained during the CAL/VAL efforts are a prerequisite because they need to be corrected and show the importance of independent CAL/VAL activities. Since the beginning of 2020, Aeolus data is even operationally assimilated at ECMWF (ECMWF, 2020a) and a positive impact on the weather prediction has been shown Isaksen and Rennie, 2019). The recent global shut down due to the COVID-19 epidemic has even shown that Aeolus is able to partly replace the missing aircraft measurements in the global data assimilation 495 system (ECMWF, 2020b).
Data availability. Radiosonde data are available at the PANGAEA Data Center: https://doi.pangaea. de/10.1594/PANGAEA.903888. Aeolus data used in this publication is not yet freely available but will become public in the near future after re-processing has been performed. Since May 2020, Aeolus data is publicly available at the ESA Aeolus Online Dissemination System.
Author contributions. All authors have contributed to the manuscript preparation. HB and AH have performed the data analysis. AH and KO 500 performed the measurements on-board RV Polarstern. UW and BH have contribution to the discussion with their expertise in remote sensing and meteorology. HB has led the manuscript preparation based on the Master thesis of AH.

Competing interests. The authors declare no conflict of interest
Disclaimer. The presented work includes preliminary data (not fully calibrated/validated and not yet publicly released) of the Aeolus mission that is part of the European Space Agency (ESA) Earth Explorer Programme. Further data quality improvements, including in particular a 505 significant product bias reduction, will be achieved before the public data release. The analysis has been performed in the frame of the Aeolus Scientific Calibration and Validation Team (ACVT).