Jos de Kloe

The European Space Agency's Aeolus mission (2018–2023) was the first satellite to deliver global wind profile observations using Doppler wind lidar technology. Aeolus significantly advanced numerical weather prediction (NWP) and atmospheric science, particularly in improving forecast skill and understanding global circulation and aerosol transport. With its successor mission Aeolus-2 now in development, a detailed assessment of Aeolus' long-term performance is essential to guide future system design and processing strategies. This study analyses the evolution and interrelation of key parameters from Aeolus Level-1B (L1B) and Level-2B (L2B) data products from processor Baseline 16, including signal-to-noise ratio (SNR), error estimate (EE), and wind data coverage. For the first time, L1B instrument parameters are averaged onto the L2B wind grid, enabling direct correlation with final product quality and tracking of performance changes across the mission. A major focus is placed on wind data coverage. Traditional metrics counted valid observations without considering variable horizontal and vertical bin sizes. Here, we assess the atmospheric area covered, accounting for the varying horizontal and vertical extent of wind bins from the Rayleigh and Mie channel across different mission phases. This reveals important changes in data yield and the influence of events like wildfires (2019) and the Hunga Tonga eruption (2022). We also evaluate how well the EE represents actual wind uncertainty using observation minus short-range NWP forecast differences. Under low-SNR conditions, the Rayleigh-clear EE slightly overestimates random error, whereas in the high-SNR regime, the Mie-cloudy EE tends to underestimate uncertainty. These findings provide critical input for optimising Aeolus-2 instrument design and data processing and offer a valuable framework for future Doppler wind lidar missions by improving data evaluation, quality control, and assimilation readiness.

The European Space Agency's Aeolus mission (2018–2023) was the first satellite to deliver global wind profile observations using Doppler wind lidar technology. Aeolus significantly advanced numerical weather prediction (NWP) and atmospheric science, particularly in improving forecast skill and understanding global circulation and aerosol transport. With its successor mission Aeolus-2 now in development, a detailed assessment of Aeolus' long-term performance is essential to guide future system design and processing strategies. This study analyses the evolution and interrelation of key parameters from Aeolus Level-1B (L1B) and Level-2B (L2B) data products from processor baseline 16, including signal-to-noise ratio (SNR), error estimate (EE), and wind data coverage. For the first time, L1B instrument parameters are interpolated onto the L2B wind grid, enabling direct correlation with final product quality and tracking of performance changes across the mission. A major focus is placed on wind data coverage. Traditional metrics counted valid observations without considering variable horizontal and vertical bin sizes. Here, we assess the atmospheric area covered, accounting for the varying horizontal and vertical extent of wind bins from the Rayleigh and Mie channel across different mission phases. This reveals important changes in data yield and the influence of events like wildfires (2019) and the Hunga Tonga eruption (2022). We also evaluate how well the EE represents actual wind uncertainty using observation minus short-range NWP forecast differences. Under low-SNR conditions, the Rayleigh-clear EE slightly overestimates random error, whereas in the high-SNR regime, the Mie-cloudy EE tends to underestimate uncertainty. These findings provide critical input for optimising Aeolus-2 instrument design and data processing and offer a valuable framework for future Doppler wind lidar missions by improving data evaluation, quality control, and assimilation readiness.

The European Space Agency's Aeolus mission (2018–2023) was the first satellite to deliver global wind profile observations using Doppler wind lidar technology. Aeolus significantly advanced numerical weather prediction (NWP) and atmospheric science, particularly in improving forecast skill and understanding global circulation and aerosol transport. With its successor mission Aeolus-2 now in development, a detailed assessment of Aeolus' long-term performance is essential to guide future system design and processing strategies. This study analyses the evolution and interrelation of key parameters from Aeolus Level-1B (L1B) and Level-2B (L2B) data products from processor baseline 16, including signal-to-noise ratio (SNR), error estimate (EE), and wind data coverage. For the first time, L1B instrument parameters are interpolated onto the L2B wind grid, enabling direct correlation with final product quality and tracking of performance changes across the mission. A major focus is placed on wind data coverage. Traditional metrics counted valid observations without considering variable horizontal and vertical bin sizes. Here, we assess the atmospheric area covered, accounting for the varying horizontal and vertical extent of wind bins from the Rayleigh and Mie channel across different mission phases. This reveals important changes in data yield and the influence of events like wildfires (2019) and the Hunga Tonga eruption (2022). We also evaluate how well the EE represents actual wind uncertainty using observation minus short-range NWP forecast differences. Under low-SNR conditions, the Rayleigh-clear EE slightly overestimates random error, whereas in the high-SNR regime, the Mie-cloudy EE tends to underestimate uncertainty. These findings provide critical input for optimising Aeolus-2 instrument design and data processing and offer a valuable framework for future Doppler wind lidar missions by improving data evaluation, quality control, and assimilation readiness.

The European Space Agency's Aeolus mission (2018–2023) was the first satellite to deliver global wind profile observations using Doppler wind lidar technology. Aeolus significantly advanced numerical weather prediction (NWP) and atmospheric science, particularly in improving forecast skill and understanding global circulation and aerosol transport. With its successor mission Aeolus-2 now in development, a detailed assessment of Aeolus' long-term performance is essential to guide future system design and processing strategies. This study analyses the evolution and interrelation of key parameters from Aeolus Level-1B (L1B) and Level-2B (L2B) data products from processor baseline 16, including signal-to-noise ratio (SNR), error estimate (EE), and wind data coverage. For the first time, L1B instrument parameters are interpolated onto the L2B wind grid, enabling direct correlation with final product quality and tracking of performance changes across the mission. A major focus is placed on wind data coverage. Traditional metrics counted valid observations without considering variable horizontal and vertical bin sizes. Here, we assess the atmospheric area covered, accounting for the varying horizontal and vertical extent of wind bins from the Rayleigh and Mie channel across different mission phases. This reveals important changes in data yield and the influence of events like wildfires (2019) and the Hunga Tonga eruption (2022). We also evaluate how well the EE represents actual wind uncertainty using observation minus short-range NWP forecast differences. Under low-SNR conditions, the Rayleigh-clear EE slightly overestimates random error, whereas in the high-SNR regime, the Mie-cloudy EE tends to underestimate uncertainty. These findings provide critical input for optimising Aeolus-2 instrument design and data processing and offer a valuable framework for future Doppler wind lidar missions by improving data evaluation, quality control, and assimilation readiness.

The European Space Agency's Aeolus mission (2018–2023) was the first satellite to deliver global wind profile observations using Doppler wind lidar technology. Aeolus significantly advanced numerical weather prediction (NWP) and atmospheric science, particularly in improving forecast skill and understanding global circulation and aerosol transport. With its successor mission Aeolus-2 now in development, a detailed assessment of Aeolus' long-term performance is essential to guide future system design and processing strategies. This study analyses the evolution and interrelation of key parameters from Aeolus Level-1B (L1B) and Level-2B (L2B) data products from processor baseline 16, including signal-to-noise ratio (SNR), error estimate (EE), and wind data coverage. For the first time, L1B instrument parameters are interpolated onto the L2B wind grid, enabling direct correlation with final product quality and tracking of performance changes across the mission. A major focus is placed on wind data coverage. Traditional metrics counted valid observations without considering variable horizontal and vertical bin sizes. Here, we assess the atmospheric area covered, accounting for the varying horizontal and vertical extent of wind bins from the Rayleigh and Mie channel across different mission phases. This reveals important changes in data yield and the influence of events like wildfires (2019) and the Hunga Tonga eruption (2022). We also evaluate how well the EE represents actual wind uncertainty using observation minus short-range NWP forecast differences. Under low-SNR conditions, the Rayleigh-clear EE slightly overestimates random error, whereas in the high-SNR regime, the Mie-cloudy EE tends to underestimate uncertainty. These findings provide critical input for optimising Aeolus-2 instrument design and data processing and offer a valuable framework for future Doppler wind lidar missions by improving data evaluation, quality control, and assimilation readiness.

The European Space Agency's Aeolus mission (2018–2023) was the first satellite to deliver global wind profile observations using Doppler wind lidar technology. Aeolus significantly advanced numerical weather prediction (NWP) and atmospheric science, particularly in improving forecast skill and understanding global circulation and aerosol transport. With its successor mission Aeolus-2 now in development, a detailed assessment of Aeolus' long-term performance is essential to guide future system design and processing strategies. This study analyses the evolution and interrelation of key parameters from Aeolus Level-1B (L1B) and Level-2B (L2B) data products from processor baseline 16, including signal-to-noise ratio (SNR), error estimate (EE), and wind data coverage. For the first time, L1B instrument parameters are interpolated onto the L2B wind grid, enabling direct correlation with final product quality and tracking of performance changes across the mission. A major focus is placed on wind data coverage. Traditional metrics counted valid observations without considering variable horizontal and vertical bin sizes. Here, we assess the atmospheric area covered, accounting for the varying horizontal and vertical extent of wind bins from the Rayleigh and Mie channel across different mission phases. This reveals important changes in data yield and the influence of events like wildfires (2019) and the Hunga Tonga eruption (2022). We also evaluate how well the EE represents actual wind uncertainty using observation minus short-range NWP forecast differences. Under low-SNR conditions, the Rayleigh-clear EE slightly overestimates random error, whereas in the high-SNR regime, the Mie-cloudy EE tends to underestimate uncertainty. These findings provide critical input for optimising Aeolus-2 instrument design and data processing and offer a valuable framework for future Doppler wind lidar missions by improving data evaluation, quality control, and assimilation readiness.

The Earth Clouds, Aerosol and Radiation Explorer (EarthCARE) is a joint mission of the European Space Agency (ESA) and the Japan Aerospace Exploration Agency (JAXA) for monitoring the aerosols, clouds, and precipitation, and for radiation closure studies. The Atmospheric Lidar (ATLID) is a High Spectral Resolution Lidar system and one of the four instruments that will be deployed onboard the platform. ATLID will use linearly polarized emission at 355 nm while pointing at 3o off-nadir and will detect the molecular (Rayleigh) and particulate (Mie) backscattered signals as well as the cross-polar component of the backscatter signals, aiming to provide profiles of the optical properties of aerosols and optically thin clouds such as the particle backscatter and extinction coefficients, and the depolarization ratio. In preparation for the calibration and validation (cal/val) activities that will be performed for ATLID upon the EarthCARE launch in May 2024, a lidar simulator tool (CARDINAL Campaign Tool; CCT) has been developed for providing realistic simulations of the ATLID lidar signals and the Level 1 (L1) products of the attenuated particulate (Mie) backscatter, the attenuated molecular (Rayleigh) backscatter, and the attenuated cross-polar backscatter. In brief, the CCT workflow includes the parametrization of an atmospheric scene with the use of model fields and/or measurements from airborne or ground-based lidars, a lidar radiative transfer model, and an instrument model based on the ATLID design. The CCT simulates the lidar signals that would be recorded from ATLID for the provided atmospheric scene and obtains the corresponding ATLID L1 products.In this study, measurements of eVe lidar from the ASKOS campaign (Cabo Verde, 2021/2022), are used as an input in the simulator for obtaining realistic ATLID L1 profiles. eVe lidar is a combined linear/circular polarization Raman lidar operating at 355 nm for aerosol profiling and consists ESA’s ground reference system for the cal/val of the ESA Aeolus and EarthCARE missions. Several cases of different aerosol layers and cirrus clouds are investigated.Furthermore, the simulated ATLID L1 profiles will be used in the Level 2A processing chain (A-PRO) to derive realistic profiles of the particle backscatter and extinction coefficients, and the linear depolarization ratio. The realistic ATLID L2A profiles will be compared with the corresponding L2 profiles from eVe lidar, aiming to investigate the detection sensitivity of ATLID products on real aerosol layers.Upcoming plans for the validation of EarthCARE mission include the exploitation of eVe lidar in an overpass cross point. The key aspects of this validation will be presented. In brief, the system will undergo an upgrade to enhance its capabilities for the cal/val activities of EarthCARE mission, retaining its combined linear/circular configuration while incorporating state-of-the-art equipment tailored for measurements on multiple scattering effects and automations to enhance the measurement procedures.

We previously discovered the sensitivity of Aeolus lidar surface returns (LSR) to surface characteristics and reported very good agreement of LSR with Lambertian Equivalent Reflectances from passive remote sensing instruments for the first year of Aeolus on orbit. In this way, we provided the first evidence that active remote sensing can be used for retrieving unidirectional UV surface reflectivity. Here, as a continuation of this effort, we report the detailed methodological solutions for retrieving and evaluating LSR to be implemented as official L2A product during Phase-F of Aeolus project for its entire lifetime. Unlike our previous report that relied on detecting surface bin using our own methodology and assumptions, we now align the approach of detecting surface bins with the official Aeolus processing methodology for retrieving LSR and elaborate on the resultant differences. Besides that, we report how this successful application of atmospheric spaceborne lidar data for inferring land surface reflectivity properties can be translated for future lidar missions such as EarthCARE and Aeolus-2. On one hand, our results will briefly introduce all the details of the LSR retrieval for Aeolus with its unique and complex optical setup (highly-non nadir incidence and UV wavelength) for broad audience for the first time. On the other hand, we will shed light on the opportunities and challenges of LSR-alike retrievals for future lidar spaceborne missions, thereby trying to minimize the key methodological uncertainties associated with implementation of LSR algorithms.

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) onboard Aeolus was the first spaceborne high-resolution lidar, measuring vertical profiles of aerosol optical properties at 355 nm at an incidence angle of ~ 35°. Although Aeolus had been primarily developed to provide vertical profiles of wind speed, aerosols, and cloud products, its lidar surface returns (LSR) were shown to contain useful information about UV surface reflectivity and agreed well with passive remote sensing reflectance. Within the process to incorporate the LSR algorithm into the Aeolus Level 2A product during the post-commissioning phase of Aeolus, we describe the methodology and evaluate the results of the adopted LSR retrieval. The algorithm combines attenuated backscattering parameters (L2 AEL-PRO data) with the information on the surface bin detection (L1 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 Aerosol Optical Depth (AOD) and Rayleigh Optical Depth to ensure that LSR is free from effects of atmospheric attenuative features such as optically thick clouds and thick aerosol conditions (AOD > 1.0). The evaluation shows that Aeolus LSR estimates produced from this approach agreed well with the UV Lambertian-Equivalent Reflectivity (LER) from GOME-2 (LER_G) and TROPOMI (LER_T) climatologies at all spatial scales. For four reference orbits (September 10, 2018; November 30, 2018; January 11, 2019; and May 1, 2019), all cloud and aerosol-free LSR estimates agree well with both LER references with correlation coefficients (r) varying from 0.55 to 0.71. For monthly scales, the agreement was moderate-to-high for LSR-LER_T (r = 0.61 – 0.77 depending on the month) and was weak-to-moderate for LSR-LER_G comparison (r = 0.44 – 0.64). Globally, the averaged 2.5 × 2.5° LSR estimates exhibit very high agreement with both LERG (0.90) and LERT (0.92) references. In reproducing regional monthly dynamics LSR and LER agree very well in snow/ice-covered regions (r > 0.90), semi-arid regions (r > 0.90), arid regions (r > 0.70), and only some regions with mixed vegetation like Australia (r = 0.94), while no agreement was found for ocean regions due to the Aeolus optical setup, favourable for ocean subsurface, not direct surface backscatter probing. We unveiled four reflectivity clusters of LSR at 2.5 × 2.5 degree grids, manifesting a transition from white to darker surfaces in descending LSR magnitude order: ice, snow, surface without snow, and water. Regionally, the LSR-LER agreement can vary and yields the highest correlation values in regions where snow is present in winter. This pattern is explained by the very good sensitivity of LSR to modelled snow cover we demonstrated (r = 0.62 – 0.74 between these parameters in such regions), while sensitivity to purely vegetation-driven changes of surface is lower, as indicated by the comparison between LSR and NDVI without snow (r < 0.30 in the regional analysis). Overall, our work complemented existing LSR studies that were mostly focused on nadir-looking CALIPSO cases by demonstrating the usability of LSR for scientific applications at non-nadir angles. By taking together CALIPSO and Aeolus experiences, a framework on effective LSR utilization using future lidar missions such as EarthCARE and Aeolus-2 can be effectively designed.