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The Atmospheric Laser Doppler Instrument (ALADIN) aboard Aeolus was the first spaceborne high-resolution lidar and measured vertical profiles of aerosol optical properties at 355 nm at an incidence angle of ∼ 35°. Although Aeolus was primarily developed to provide vertical profiles of wind speed, aerosols and cloud products, its lidar surface returns (LSRs) have been shown to contain useful information about ultraviolet (UV) surface reflectivity and have agreed well with passive remote sensing reflectance. With a focus on the process to incorporate the LSR algorithm into the Aeolus Level-2A product, we describe the methodology and evaluate the results of the adopted LSR retrieval. The algorithm combines attenuated backscattering parameters (Level-2 Aeolus Profile Processor Algorithm, L2 AEL-PRO, data) with information on the surface bin detection (Level-1 data) to produce attenuated LSR estimates (e.g., surface-integrated attenuated backscatter) for all bins where the ground was detected. The correction for producing final LSR estimates at the original Aeolus resolution is performed using the Aeolus L2 retrievals, namely, the aerosol optical depth (AOD) and Rayleigh optical depth, to ensure that LSRs are free of the effects of atmospheric attenuating features, such as optically thick clouds and thick aerosol conditions (AOD > 1.0). The evaluation shows that Aeolus LSR estimates produced using this approach agree well with the UV Lambertian-equivalent reflectivity (LER) from the Global Ozone Monitoring Experiment-2 (GOME-2; LERG) and TROPOspheric Monitoring Instrument (TROPOMI; LERT) climatologies at all spatial scales. For four reference orbits (10 September 2018, 30 November 2018, 11 January 2019 and 1 May 2019), all cloud and aerosol-free LSR estimates agree well with both LER references, with correlation coefficient (r) values varying from 0.55 to 0.71. For monthly scales, the agreement was moderate to high for the LSR–LERT comparison (r = 0.61–0.77 depending on the month) and weak to moderate for the LSR–LERG comparison (r = 0.44–0.64). Globally, the averaged 2.5° × 2.5° LSR estimates exhibit very high agreement with both the LERG (0.90) and LERT (0.92) references. With respect to reproducing the regional monthly dynamics, LSR and LER agree very well in snow- or ice-covered regions (r > 0.90), semiarid regions (r > 0.90), arid regions (r > 0.70), and some regions with mixed vegetation (like Australia; r = 0.94), whereas no agreement was found for ocean regions due to the Aeolus optical setup, which is favorable for the ocean subsurface but not for direct surface backscatter probing. We unveiled four reflectivity clusters of LSRs at the 2.5° × 2.5° grid scale, manifesting a transition from white to darker surfaces in descending LSR magnitude order: (1) ice, (2) snow, (3) surface without snow and (4) water. Regionally, the LSR–LER agreement can vary and yields the highest correlation values in regions where snow is present in winter, indicating the excellent sensitivity of Aeolus LSRs to white surfaces such as snow. This finding is corroborated by the very good agreement of LSRs with modeled snow cover that we demonstrated (r = 0.62–0.74 between these parameters in such regions), while the sensitivity to purely vegetation-driven changes in the surface is lower, as indicated by the comparison between LSRs and the normalized difference vegetation index (NDVI) without snow (r < 0.30 in the regional analysis). By demonstrating the usability of LSRs for scientific applications at non-nadir angles, our work deepens the knowledge about LSRs, which has mostly been based on nadir-looking Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) studies in the past. Using experiences from both the nadir-looking CALIPSO and the highly non-nadir Aeolus mission, a framework for the effective LSR utilization using future lidar missions such as EarthCARE and Aeolus-2 can be effectively designed.

The Atmospheric LAser Doppler INstrument (ALADIN) on board Aeolus was the first high-spectral-resolution lidar (HSRL) in space. It was launched in 2018 and re-entered in 2023. The FeatureMask (A-FM) and extinction profile algorithms (A-PRO) developed for the Earth Cloud Aerosol and Radiation Explorer (EarthCARE) HSRL ATmospheric LIDar (ATLID) have been adapted to Aeolus and called AEL-FM and AEL-PRO, respectively. These algorithms have been purposely built to process low signal-to-noise ratio space-based lidar signals. A short description of the AEL-FM and AEL-PRO algorithms is provided in this paper. AEL-FM and AEL-PRO prototype products (v1.7) have been evaluated using the collocated Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical feature mask (VFM) product and level 2 aerosol profile product for 2 months of data in October 2018 and May 2019. Aeolus and CALIPSO are both polar-orbiting satellites, but they have different overpass times. The evaluations are focused on desert dust aerosols over Africa. These types of scenes are often stable in space (tens of kilometres) and time (on the order of 0.5–1 h), and thus, a useful number of collocated cases can be collected. We have found that the AEL-FM feature mask and the CALIPSO VFM show similar aerosol patterns in the collocated orbits, but AEL-FM does not separate aerosol and cloud features. Aeolus and CALIPSO have a good agreement for the extinction coefficients for the dust aerosols, especially for the cloud-free scenes. The Aeolus aerosol optical thickness (AOT) is larger than the CALIPSO AOT, mainly due to cloud contamination. Because of the missing a cross-polar channel, it is difficult to distinguish aerosols and thin ice clouds using the Aeolus extinction coefficients alone. The AEL-FM and AEL-PRO algorithms have been implemented in the Aeolus level 2A (L2A) processor. The findings here are applicable to the AEL-FM and AEL-PRO products in L2A Baseline 17. This is the first time that the AEL-FM and AEL-PRO products have been evaluated using CALIPSO data.

Abstract Global ultraviolet (UV) surface reflectivity climatologies are all composed from daylight passive remote sensing observations of reflected UV light, generally integrated over a distribution of attainable reflection directions. We discovered the sensitivity of Aeolus lidar surface returns (LSR) to surface characteristics, providing the first evidence that active remote sensing can be effectively used for retrieving unidirectional UV surface reflectivity on global scales. LSR reproduces surface reflectivity monthly changes in Sahara, which are visible in the Lambertian Equivalent Reflectivity (LER) climatologies from TROPOMI and GOME-2. Very high correlations (r > 0.90) between gridded LSR and the LER climatologies are reported at global and regional scales for 36 different regions. Three clear land cover gradients are discerned from Aeolus LSR signal: (1) water/land, (2) vegetation/arid areas and (3) no snow/snow. The strongest LSR signal was retrieved over snow, while over vegetation, we found moderate negative agreement (r

Carrying a laser Doppler instrument, the Aeolus satellite was launched in 2018, becoming the first mission for atmospheric wind profile measurements from space. Before utilizing the Aeolus winds for different applications, evaluating their data quality is essential. With the help of ground-based wind profiling radar measurements and the European Centre for Medium-Range Weather Forecasts (ECMWF) model equivalents, this study quantifies the error characteristics of Aeolus L2B (baseline-11) near-real-time horizontal line-of-sight winds across Australia during October 2020–March 2021 by using both inter-comparison and triple collocation analysis. The results of the inter-comparison analysis indicate that both Rayleigh-clear winds and Mie-cloudy winds are in good agreement with the ground-based radar measurements with overall absolute mean biases smaller than 0.7 m s−1 and correlation coefficients larger than or equal to 0.9. Moreover, assuming the radar measurements as the reference data set, Mie-cloudy winds are shown to be more precise than Rayleigh-clear winds with an overall random error of 4.14 and 5.81 m s−1, respectively. Similar results were also found from triple collocation analysis, with error standard deviations of 5.61 and 3.50 m s−1 for Rayleigh-clear winds and Mie-cloudy winds. In addition, the Mie channel is shown to be more capable of capturing the wind in the planetary boundary layer (< 1500 m). The findings of this study demonstrate the good performance of space-borne Doppler lidar for wind profiling and provide valuable information for data assimilation in numerical weather prediction.

The European Space Agency (ESA) Earth Explorer satellite Aeolus provides continuous profiles of the horizontal line-of-sight wind component globally from space. It was successfully launched in August 2018 with the goal to improve numerical weather prediction (NWP). Aeolus data have already been successfully assimilated into several NWP models and have already helped to significantly improve the quality of weather forecasts. To achieve this major milestone the identification and correction of several systematic error sources were necessary. One of them is related to small fluctuations of the temperatures across the 1.5 m diameter primary mirror of the telescope which cause varying wind biases along the orbit of up to 8 m s−1. This paper presents a detailed overview of the influence of the telescope temperature variations on the Aeolus wind products and describes the approach to correct for this systematic error source in the operational near-real-time (NRT) processing. It was shown that the telescope temperature variations along the orbit are due to changes in the top-of-atmosphere reflected shortwave and outgoing longwave radiation of the Earth and the related response of the telescope's thermal control system. To correct for this effect ECMWF model-equivalent winds are used as a reference to describe the wind bias in a multiple linear regression model as a function of various temperature sensors located on the primary telescope mirror. This correction scheme has been in operational use at ECMWF since April 2020 and is capable of reducing a large part of the telescope-induced wind bias. In cases where the influence of the temperature variations is particularly strong it was shown that the bias correction can improve the orbital bias variation by up to 53 %. Moreover, it was demonstrated that the approach of using ECMWF model-equivalent winds is justified by the fact that the global bias of model u-component winds with respect to radiosondes is smaller than 0.3 m s−1. Furthermore, this paper presents the alternative of using Aeolus ground return winds which serve as a zero-wind reference in the multiple linear regression model. The results show that the approach based on ground return winds only performs 10.8 % worse than the ECMWF model-based approach and thus has a good potential for future applications for upcoming reprocessing campaigns or even in the NRT processing of Aeolus wind products.

Global ultraviolet (UV) surface reflectivity climatologies are all composed from daylight passive remote sensing observations of reflected UV light, generally integrated over a distribution of attainable reflection directions. We discovered the sensitivity of Aeolus lidar surface returns (LSR) to surface characteristics, providing the first evidence that active remote sensing can be effectively used for retrieving unidirectional UV surface reflectivity on global scales. LSR reproduces surface reflectivity monthly changes in Sahara, which are visible in the Lambertian Equivalent Reflectivity (LER) climatologies from TROPOMI and GOME-2. Very high correlations (r > 0.90) between gridded LSR and the LER climatologies are reported at global and regional scales for 36 different regions. Three clear land cover gradients are discerned from Aeolus LSR signal: (1) water/land, (2) vegetation/arid areas and (3) no snow/snow. The strongest LSR signal was retrieved over snow, while over vegetation, we found moderate negative agreement (r < - 0.60) between LSR and vegetation index proxy. Overall, the success of the first active remote sensing method for retrieving unidirectional UV surface reflectivity using Aeolus is demonstrated. Our approach can be effectively used to detect unresolved land and, especially, snow cover changes in high latitudes because, unlike passive instruments, Aeolus also provided nighttime observations., (© 2023. Springer Nature Limited.)