LUCE, formerly Cloud and Aerosol Lidar for Global Scale Observations of the Ocean-Land-Atmosphere System (CALIGOLA), is an advanced multi-disciplinary space lidar mission for Earth Sciences, primarily focusing on the observation of the atmosphere and oceans, aimed at advancing global knowledge on the coupled atmosphere-ocean-land system. It is the first spaceborne Raman-elastic-fluorescence lidar, created through an Agenzia Spaziale Italiana (ASI) and National Aeronautics and Space Administration (NASA) partnership. This mission has been conceived with the aim to provide the international scientific community with an unprecedented dataset of geophysical parameters capable to increase scientific knowledge in the areas of atmospheric, aquatic, terrestrial, cryospheric and hydrological sciences. The mission is planned to be launched in the time frame 2035-2037, with an expected lifetime of 3-5 years. This conference contribution aims at providing an overview of the different mission scientific objectives, with a primary focus on atmospheric and ocean sciences, and a preliminary assessment of the expected system performance in a variety of environmental scenarios.

How to cite: Di Girolamo, P. and the LUCE: The space lidar mission LUCE: a multi-disciplinary observatory for Earth Sciences, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5843, https://doi.org/10.5194/egusphere-egu26-5843, 2026.

Wildfire activities across Canada have increased significantly in the last several years. Intense wildfires release large amounts of smoke aerosols that can be lifted into the upper troposphere and lower stratosphere, providing a large episodic source of carbonaceous aerosols, composed primarily of organic carbon and black carbon. These smoke particles can persist for weeks to months and be transported over long distances, whereby extending their atmospheric influence far from the source regions. Smoke particles can greatly impact the Earth’s climate directly by scattering and absorbing solar radiation and indirectly by modifying cloud formation and properties. During long-range transport, smoke aerosols undergo chemical and microphysical aging, which may alter their size, composition, optical properties, and ice nucleation ability. In addition, smoke particles in the high altitudes can act as ice-nucleating particles (INP) to trigger cirrus cloud formation via heteorogeneous nucleation, modifying ice crystal number concnetrations, particle size and cloud optical properties. From the end of May 2025, extreme wildfire outbreak in Canada lifted smoke particles up to the lower stratosphere that were transported across the North Atlantic to Europe. In this study, we paramerize the aging transformations of smoke aerosols by comparing their lidar ratios (= extinction-to-backscatter ratio) and particle linear depolarization ratios (PLDR) directly retrieved by ATLID (the ATmospheric LIDar) onboard the EarthCARE satellite along the transport pathway of the smoke plumes. To do so, we make use of the HYSPLIT forward trajectories to track the smoke plume evolving from fire locations. Furthermore, we derive the cirrus cloud PLDR from ATLID as well as ice crystal number concentration (Ni) and effective radius (Re) from the lidar-radar synergy combing co-located ATLID and CPR (the Cloud Profiling Radar). Finally, we are able to compare PLDR, Ni, and Re between disturbed cirrus clouds by smoke aerosols and pristine ones to identify the impact of smoke particles on cirrus clouds. 

How to cite: Li, Q. and Gross, S.: Aerosol aging and cirrus cloud modification from Canadian wildfire smoke transported to Europe in 2025 observed by EarthCARE, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11060, https://doi.org/10.5194/egusphere-egu26-11060, 2026.

Accurate cloud base height (CBH) over the Tibetan Plateau—Earth's Third Pole—is essential for constraining Asian monsoon dynamics, glacial melt projections, and water security, affecting 1.9 billion people downstream. However, ERA5 reanalysis systematically underestimates CBH by up to 5.20 km in southern regions, propagating errors into climate models and hydrological forecasts. Here, we present a two-step machine learning framework that progressively eliminates this hidden bias. Step 1 refines the ERA5 retrieval algorithm using three years of ground-based lidar observations (October 2021–December 2024), reducing the site-level mean bias error from 1.8 km to 0.1 km and improving the regional correlation with CALIPSO from 0.25 to 0.40. Step 2 applies an Optuna-optimized XGBoost model trained on high-confidence CALIPSO observations (N=106,718), fusing the refined ERA5 data with vertical atmospheric profiles and surface attributes. The final product achieved a test-set RMSE of 1.87 km (R²=0.71, MBE=−0.02 km), with seasonal correlations reaching 0.72–0.86 and southern plateau bias reduced from −5.20 km to −0.11 km, a 97.9% improvement. This scalable approach enables reliable, long-term CBH reconstruction, which is critical for advancing climate model parameterizations and water resource assessments across High Mountain Asia.

How to cite: Zhao, W., Wang, Y., and Pan, Y.: Machine Learning Reveals Hidden Bias in ERA5 Cloud Heights Over Earth's Third Pole, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2474, https://doi.org/10.5194/egusphere-egu26-2474, 2026.

Aerosols play a key role in air quality, weather, and climate. Ground-based active remote sensing can contribute to the continuous monitoring of aerosol vertical profiles, especially when operating within regional, national and international networks. In fact, networked Automated-Lidar-Ceilometers (ALC) are now widely used to this purpose, monitoring the low and middle troposphere. However, conversion of their raw data into quantitative geophysical information is not straightforward.

In this work, we present a model-supported approach to retrieve vertically-resolved aerosol optical and physical properties (aerosol backscatter and extinction coefficients, surface area, volume and mass concentrations) from elastic lidar systems. It extends previous results and processing capabilities of lidar and/or ALC data developed and employed within the Italian ALC network ALICENET (Dionisi et al., 2018; Bellini et al., 2024). In particular, we present here an upgraded version of the model, which relies on a Monte Carlo framework generating a large ensemble of light-scattering computations at multiple, lidar-relevant wavelengths (355, 532, 910, and 1064 nm) and targeted to reproduce a continental aerosol type mixed to low-to-moderate contributions of desert dust. With respect to previous model configurations (e.g., Dionisi et al., 2018), the new version simulate the coarse, dust particles as spheroids, taking advantage of the open-access spheroid package GRASP (Dubovik et al., 2006). This also allows computation of the aerosol depolarization ratio in addition to the other aerosol optical and physical properties. The model simulations are then used to derive mean functional relationships linking aerosol backscatter and particle depolarization ratio to the other aerosol properties. This upgraded version of the model was indeed developed within ALICENET to assist inversion of new commercially available ALC systems with polarization capability (PLC, as the Vaisala CL61). In this work, we will present: a) the numerical model simulations results, b) their evaluation through independent aerosol data from AERONET sun-photometers and 3) their practical use within the operative ALICENET inversion of PLC data to derive aerosol optical and physical properties. In fact, application of the new functional relationships shows improved agreement of PLC-retrievals with columnar aerosol optical depth and in situ mass measured at ground level in dust-loaded conditions. These results suggest that the proposed methodology could be applied to operational ALC/PLC networks operating in low-to-moderate dust-affected conditions, thus supporting radiative transfer, atmospheric chemistry, and air quality studies.

References:

• Dionisi, et al., A multiwavelength numerical model in support of quantitative retrievals of aerosol properties from automated lidar ceilometers and test applications for AOT and PM10 estimation, Atmos. Meas. Tech., 11, 6013–6042, https://doi.org/10.5194/amt-11-6013-2018, 2018.
• Bellini, et al., ALICENET– an Italian network of automated lidar ceilometers for four-dimensional aerosol monitoring: infrastructure, data processing, and applications, Atmos. Meas. Tech., 17, 6119–6144, https://doi.org/10.5194/amt 17-6119-2024, 2024.
• Dubovik et al., Application of spheroid models to account for aerosol particle nonsphericity in remote sensing of desert dust, J. Geophys. Res., 111, D11208, https://doi.org/10.1029/2005JD006619, 2006.

How to cite: Goi, A., Diémoz, H., Bellini, A., Bracci, A., and Barnaba, F.: Model-assisted retrievals of aerosol properties from Polarization-sensitive Automated Lidar-Ceilometers and test applications to Vaisala CL61 measurements during desert dust transport episodes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11591, https://doi.org/10.5194/egusphere-egu26-11591, 2026.

Aerosol microphysical parameters (e.g., size distributions and complex refractive index) control scattering and absorption and underpin quantitative estimates of aerosol radiative effects and aerosol–cloud interactions. Retrieving them from multiwavelength Raman lidar is inherently ill-posed: measurement noise and systematic uncertainties quickly erode multi-channel constraints under weak signals, and conventional LUT/iterative inversions are too slow (seconds to minutes per profile) for network-scale or high-throughput processing.

We propose AecroFormer, an end-to-end regression model that incorporates multi-head attention to learn cross-wavelength coupling and deliver physically coherent, range-resolved vertical-profile retrievals with improved stability under real-world SNR and noise. For channel combinations such as 3β+2α, AecroFormer achieves an inference speed of 7.4×10⁻⁵ s per range gate on an NVIDIA GeForce RTX 5080, delivering orders-of-magnitude acceleration relative to LUT/iterative schemes that typically operate from minute-level down to sub-second per range gate (e.g., Müller et al., 1999; Wang et al., 2022). Noise robustness tests show that the model maintains practical accuracy as noise increases: even at 20% noise, it remains stable with MAE(mᵣ) ≈ 0.0758 and MRE(rₑ) ≈ 32.9%.

Focusing on the two important application-critical profile products—effective radius (rₑ) and aerosol volume concentration—we assessed real-world applicability through  an observation-based consistency check using operational measurements from the Aksu site (Xinjiang, China) in January 2024, selecting four days for validation. Retrieved aerosol volume concentrations were converted to 0–2 km boundary-layer mean PM₂.₅ using an empirical density assumption and matched against surface air-quality observations (n = 28). The comparison yields a PM₂.₅ bias of 4.69 ± 26.87 µg/m³ and a relative bias of 3.29%, indicating that the method reproduces both the magnitude and variability observed by ground monitoring in a network-operational setting.

Overall, AecroFormer substantially reduces the computational cost while preserving noise-robust retrieval performance, enabling a practical transition from offline, slow microphysical inversions to near-real-time, high-throughput, and deployable processing. It also provides a reusable algorithmic foundation for future extensions under more realistic bimodal forward assumptions and tightly controlled uncertainty constraints.

How to cite: Zou, W., Yin, Z., Bu, Z., Wang, X., and Müller, D.: AecroFormer: Fast, Noise-Robust Aerosol Microphysical Retrieval for Multiwavelength Raman Lidar, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8670, https://doi.org/10.5194/egusphere-egu26-8670, 2026.

The climate in the Arctic is changing rapidly. The near-surface air temperature increased much faster than on global average in recent years, a phenomenon called Arctic Amplification. This Arctic Amplification leads to a weaker and wavier jet stream, potentially allowing a more frequent transport of airmasses into the Arctic which have their origin in the mid-latitude. These mid-latitude airmasses are responsible for an influx of warm and moist air, significantly influencing the energy budget in the Arctic due to their radiative effects. But airmass transport from the mid-latitudes has also an impact on cloudiness in the Arctic as well as on cloud properties, as they strongly depend on the conditions under which the clouds form. The main focus on cloud so far, however, was on lower-level clouds. Arctic high level ice clouds are hard to study. Satellite measurements do often not provide data with sufficient accuracy or resolution, and in-situ measurement have rarely been performed.

In March and April 2022, the HALO-(AC)³ campaign was conducted, using the German High Altitude and LOnge range (HALO) research aircraft equipped with a remote sensing payload. With HALO it was possible to perform high altitude measurements deep inside the Arctic. The measurements provided high accurate and highly resolved information about the atmosphere along the flight path. Key instruments during HALO-(AC)³ have been the combined airborne water vapor differential absorption and high spectral resolution lidar WALES, and the Doppler cloud radar MIRA-35. We use the measurements of the lidar to characterize the environmental conditions in Arctic and mid-latitude airmasses, i.e. the humidity field. Ice cloud microphysical properties are derived from the synergy of lidar and radar using an optimal estimate retrieval. The combination of the characterization of the environmental conditions and the cloud properties allows to study differences in the microphysics of ice clouds in the Arctic depending on the origin of the airmasses they are forming in. We will give an overview of our measurements, the characterization of the environmental conditions, and will show differences in the cloud macro- and microphysical properties of the observed ice clouds.

How to cite: Gross, S., Dekoutsidis, G., Wirth, M., and Ewald, F.: Studying differences in microphysics of ice clouds in the Arctic depending on airmass origin using lidar-radar synergy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12298, https://doi.org/10.5194/egusphere-egu26-12298, 2026.

The Vaisala CL61 is increasingly deployed in both research infrastructures, such as ACTRIS, and operational meteorological networks for applications including aviation and air-quality forecasting. As a new generation elastic backscatter lidar, it extends conventional ceilometer capabilities by providing depolarization ratio measurement. While this measurement is highly valuable, especially for unattended, autonomous operation, its use in network applications requires careful characterization.

We developed a methodology for identifying background signals and suitable liquid cloud layers to evaluate the long-term performance of multiple CL61 instruments across different sites. Results show some variability between instruments, with several of these early production units exhibiting a pronounced decrease in laser power over time, accompanied by increased background noise. Although internal calibration normally compensates for laser power degradation, external atmospheric calibration at the Lindenberg site revealed that this compensation becomes insufficient when laser power falls below 40%.

Termination hood measurements were used to characterize instrument noise and bias profiles. Both were found to exhibit temperature dependence and to deviate from zero in the near range, below approximately 2 km but extending up to 5 km for one instrument. A method for bias correction, along with an estimation of the associated uncertainty, is presented. In addition, an aerosol inversion approach is also introduced for retrieving the profile of aerosol particle backscatter coefficient, aerosol depolarization ratio, and their corresponding uncertainties. A case study demonstrates that bias-corrected, aerosol-inverted depolarization ratio can differ by up to 0.1 from the original instrument values, emphasizing the importance of accounting for instrumental bias and, in particular, molecular contributions at the CL61 operating wavelength of 905 nm.

Lastly, we observed signal loss in one instrument and found that it was due to optical lens fogging caused by insufficient internal heating linked to firmware behaviour. It is particularly important to identify and exclude such periods to ensure the reliability of the measurement.

How to cite: Le, V., J. O’Connor, E., Filioglou, M., and Vakkari, V.: Insights into long-term Atmospheric Profiling with the Vaisala CL61 Ceilometer, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17151, https://doi.org/10.5194/egusphere-egu26-17151, 2026.

Multi-wavelength lidar measurements are crucial for aerosol remote sensing as they can provide additional information for aerosol characterisation. For such measurements typically the fundamental of Nd:YAG lasers at 1064nm and the first and second harmonic at 532nm and 355nm are used. However, due to limitations in the dynamic range and quantum efficiency of detectors, signal detection for the near infrared is challenging. Accordingly, special focus lies on the contribution of our new ACTRIS CARS (Centre for Aerosol Remote Sensing) reference lidar module for 1064nm equipped with novel APD recorder setups providing high signal quality at 1064nm compared to what was possible so far. (Haimerl, 2025)  

For the EGU conference 2026 we will present intensive aerosol properties retrieved for 3 wavelengths from combined measurements in troposphere and up to lower stratosphere of the portable reference lidar system POLIS-9 of ACTRIS CARS at LMU and of the quality assured ACTRIS lidar system TONI.

The measurements were conducted in the context of an intercomparison campaign at the KIT IMK-IFU* institute in Garmisch-Partenkirchen between 01.10.2025 and 13.11.2025. The POLIS-9 reference lidar system is a combination of two portable lidar modules POLIS-6 and POLIS-1064. POLIS-6 has co- and cross-polar channels for 355nm and 532nm and vibrational Raman channels respectively. The POLIS-1064 upgrade offers 1064nm co- and cross-polar channels and a rotational Raman channel. TONI at KIT IMK-IFU is equipped with co- and cross-polar channels and vibrational Raman channels at 355nm and 532nm and a total elastic channel at 1064nm. For additional observational capabilities in the stratosphere also a lidar from KIT IMK-IFU located on nearby Zugspitze Mountain with one 532 total channel was utilized. (Haimerl, 2026) 

Aerosol products were retrieved for different aerosol cases, like smoke layers on several days during the campaign, a Saharan Dust layer on 13.11.2025 up to 4km and clean atmosphere condition on 07.11.2025. Moreover, we also try to characterise a persistent layer between 10km and 20km in the stratosphere, potentially attributed to volcanic aerosol. (Trickl, 2024)

A detailed discussion of retrieval results will then be presented at the conference. Also, we are aiming to take close overpasses of the EarthCare satellite during our campaign into account and use our retrieval results for validating the satellite data.

This project receives funding from European Union’s Horizon research and innovation programme under grant agreement No. 871115. ACTRIS-D is funded by German Federal Ministry for Education and Research (BMBF) under grant agreements 01LK2001A-K & 01LK2002A-G.

Haimerl, M. (2025) POLIS1064 – A polarization Raman lidar with state-of-the-art recorders for minimizing analogue signal distortions, Proc. European lidar conference Warsow 2025.

Haimerl, M. (2026) Retrieval of tropospheric and stratospheric aerosol properties at 3 wavelengths from combined measurements of two ACTRIS lidar systems, Proc. ACTRIS Science Conference Oslo, 2026.

Trickl, T. et. al (2024) Measurement report: Violent biomass burning and volcanic eruptions – a new period of elevated stratospheric aerosol over central Europe (2017 to 2023) in a long series of observations, Atmos. Chem. Phys., 24.

(*IMK-IFU: Institute of Meteorology and Climate Research, Atmospheric Environmental Research Department)

How to cite: Haimerl, M., Siomos, N., Freudenthaler, V., Vogelmann, H., and Posyniak, M.: Retrieval of 3 wavelengths aerosol properties from combined measurements of two ACTRIS lidar systems in troposphere and stratosphere, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19607, https://doi.org/10.5194/egusphere-egu26-19607, 2026.

Reliable identification of aerosols and clouds in multiwavelength lidar observations remains essential for atmospheric monitoring and climate research. However, conventional processing pipelines rely heavily on expert-driven inversions and threshold-based algorithms. In this work, we present a deep-learning (DL) image segmentation framework designed to operate directly on image-like representations of the range-corrected signal (RCS) and applicable across distinct lidar platforms.

The models were trained on DL4Lidar, a new expert-annotated dataset derived from the ALHAMBRA multi-spectral Raman lidar (Granada, Spain). Using Mask R-CNN implemented using Detectron2 framework, we systematically explored wavelength selection, visualization scale bounds, and architectural variants to maximize the discrimination of atmospheric structures. The resulting class-specific models capture the characteristic morphology and spatiotemporal variability of aerosols and clouds without relying on inversion-based preprocessing, demonstrating the suitability of computer-vision techniques for processing raw lidar observations.

To assess robustness beyond the training instrument, the trained models were directly applied, without retraining or domain adaptation, to measurements from MULHACEN, an independent Raman lidar located in the same facilities as ALHAMBRA but with different hardware characteristics and signal levels. Despite these instrumental differences, the models exhibit stable behavior, correctly identifying cloud and aerosol structures across a wide range of atmospheric situations. This cross-instrument evaluation highlights the capacity of the proposed method to generalize under realistic domain shifts, suggesting that morphological characteristics learned from RCS imagery are transferable across similar ground-based systems.

Experiments and sensitivity analysis of the models will be evaluated for different variables such as attenuated backscatter vs. RCS used as input images. Moreover, the best DL model resulting from the sensitivity analysis will be tested on other lidar instruments within the EARLINET/ACTRIS network and spaceborne observations such as ATLID onboard the EarthCARE mission.

Overall, this work introduces a unified DL-based pipeline for atmospheric structure segmentation from multi-wavelength lidar measurements, demonstrating its potential for operational use and large-scale automated analysis for atmospheric classification across heterogeneous lidar platforms.

Acknowledgements

This research is part of the Spanish national project PID2023-151817OA-I00, titled DeepAtmo, funded by MICIU/AEI/10.13039/501100011033.

How to cite: Canella-Ortiz, A., Tabik, S., Fernández-Carvelo, S., Rodríguez-Navarro, O., Alados-Arboledas, L., and del Águila, A.: Atmospheric classification using lidar data and deep learning-based image segmentation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1153, https://doi.org/10.5194/egusphere-egu26-1153, 2026.

Ground-based lidar networks have expanded rapidly in recent years, providing continuous, high-resolution profiles of aerosols, precipitation and clouds for both operational meteorology and climate research. Among them, the EUMETNET E-Profile network now operates more than 400 single-wavelength ceilometers, enabling unprecedented spatial and temporal coverage of backscatter measurements. However, unlike synergistic radar-lidar systems such as Cloudnet, ceilometers alone do not provide operational target classification of hydrometeors or aerosol/clear-sky discrimination.

In this study, we explore the capability of artificial intelligence methods to infer Cloudnet-level target classifications directly from ceilometer backscatter profiles. The approach treats standardized 24-h time-height backscatter as image-like inputs and applies convolutional encoder-decoder architectures for semantic segmentation of atmospheric structures. Training and validation were performed using data from multiple Cloudnet reference stations at different latitudes under diverse meteorological conditions, enabling the model to learn station-agnostic spatio-temporal patterns associated with hydrometeors and aerosol layers.

Initial results demonstrate that key Cloudnet hydrometeor categories and clear-sky/aerosol regions can be recovered from ceilometer-only input, even in the absence of synergistic radar information. These findings indicate that single-wavelength backscatter can be used as input in computer-vision models, in order to extract physically meaningful patterns from the temporal evolution of the signal.

This work establishes the basis for a future near-real-time classification framework scalable to the E-Profile network. The methodology also opens new opportunities for cross-validation with spaceborne lidar and radar products, particularly from the EarthCARE mission, and for generating long-term occurrence statistics that may inform studies on cloud processes, aerosol-cloud interactions and model performance.

Acknowledgements:

This research is part of the Spanish national project PID2023-151817OA-I00, titled DeepAtmo, funded by MICIU/AEI/10.13039/501100011033 and Horizon Europe program under the Marie Sklodowska-Curie Staff Exchange Actions with the project GRASP-SYNERGY (grant agreement No. 101131631). This work is also part of the 2024 Leonardo Grant for Researchers and Cultural Creators from the BBVA Foundation. Ana del Águila is part of Juan de la Cierva programme through grant JDC2022-048231-I funded by MICIU/AEI/10.13039/501100011033 and by European Union “NextGenerationEU”/PRTR.

How to cite: del Águila, A., Billault-Roux, A.-C., Sauvageat, E., Canella-Ortiz, A., Molina-Párraga, L., Alados-Arboledas, L., and Haefele, A.: Deep Learning-Based Hydrometeor Classification from E-Profile Ceilometers Using Cloudnet Reference Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1123, https://doi.org/10.5194/egusphere-egu26-1123, 2026.

Carbon dioxide is the most important anthropogenic greenhouse gas. Therefore, measuring its distribution and variability in the atmosphere with high precision, accuracy, and resolution is key to a better understanding of the carbon cycle and radiative forcing. Especially, continuous profiling at the same location over longer periods of time provides insights about local sources and sinks. Since most of these are located on the ground, ground-based lidar systems with their ability of range-resolved measurements are particularly interesting because passive remote sensing satellites (e.g. OCO-2/3) cannot provide range-resolved data close to the surface. To realize carbon dioxide measurements, we integrated an additional channel into our eye-safe, fully automated ground-based Raman lidar ARTHUS (Atmospheric Raman Temperature and HUmidity Sounder) [1]. So far, more than 90 nights of CO₂ profiles have been collected at the Land-Atmosphere Feedback Observatory (LAFO) of the University of Hohenheim, Stuttgart, Germany [2]. Profiles of CO₂, temperature, and humidity, as well as particle extinction and particle backscatter coefficients, are measured simultaneously with five receiver channels. With averaging of 1 h and 400 m under nocturnal, cloud-free conditions, the uncertainties of the CO₂ mixing ratio measurements are only <2.8 ppm up to a distance of 2 km . When averaging over the full night, e.g., 13 hours and 400 m, the uncertainties are <1 and <2 ppm up to distances of ~2.5 and 4.0 km, respectively. Compared to measurements presented at last year’s EGU General Assembly [3], the lidar CO₂ signal intensity could be improved by a factor of up to 8.

Since 2025, a newly installed two-mirror scanner enables measurements in any direction. In December 2025, we performed measurements with an elevation angle of 2° close to the surface in order to investigate CO₂ sources and sinks. Furthermore, nearby in-situ CO₂ sensors on towers at 2 and 10 m height above ground at distances of 600 and 1000 m to the lidar now allow for improved calibration and comparisons. We will present and discuss these new low-level scans at the conference.

References:

[1] Lange, D. et al.: Compact Operational Tropospheric Water Vapor and Temperature Raman Lidar with Turbulence Resolution. Geophys. Res. Lett. (2019). DOI: 10.1029/2019GL085774

[2] Späth, F., et al.: The land–atmosphere feedback observatory: a new observational approach for characterizing land–atmosphere feedback. Geoscientific Instrumentation, Methods and Data Systems (2023). DOI: 10.5194/gi-12-25-2023

[3] Schumacher, M., D. Lange, A. Behrendt, V. Wulfmeyer: CO₂ Measurements with Raman Lidar in the Lower Troposphere. EGU25-8872 (2025) DOI: 10.5194/egusphere-egu25-8872

How to cite: Schumacher, M., Lange, D., Behrendt, A., and Wulfmeyer, V.: CO2 Profiling with Automated Scanning Raman Lidar, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12789, https://doi.org/10.5194/egusphere-egu26-12789, 2026.

We will present the strategy and results of a combination of six scanning lidars to investigate the interplay between daytime surface fluxes, surface layer gradients, convective boundary layer dynamics and development, as well as the characteristics of the interfacial layer and the lower free troposphere. Our observations were made above the agricultural fields of University of Hohenheim [1], Stuttgart, Germany in spring and summer 2025 in the frame of the research unit Land Atmosphere Feedback Initiative (LAFI, https://lafi-dfg.de/) of the German Research Foundation (DFG). For this, the automated Raman lidar ARTHUS (Atmospheric Temperature and Humidity Sounder) built in our institute in recent years, was extended with a scanner for atmospheric measurements in the surface layer just above the canopy. ARTHUS [2] is an eye-safe rotational Raman lidar with five receiver channels detecting the elastic backscatter signal at 355 nm, two rotational Raman signals with opposite temperature dependence, as well as the two vibrational Raman signals of water vapor and carbon dioxide. These scanning measurements were performed during intensive observation periods for 50 minutes of each hour while during the remaining 10 minutes of each hour as well as during non-IOP days vertical pointing measurements were made. These surface layer observations of ARTHUS were combined with data measured with two Doppler lidars making simultaneously cross-cutting low-level scans for horizontal wind profiling near the surface. Two more Doppler lidars were measuring vertical wind fluctuations and horizontal wind speed and direction. One of these two Doppler lidars was operated in constant vertical pointing mode while the other was operated in a six-beam scanning mode with an elevation angle of 45°. Our water vapor differential absorption lidar (WVDIAL) made vertical-pointing observations of turbulent moisture fluctuations up to the free troposphere. The WVDIAL uses a Titanium-Saphire laser pumped with the second-harmonic radiation of a Nd:YAG laser as transmitter emitting online and offline laser pulses near 820 nm with 200 Hz into the atmosphere. The atmospheric backscatter signals are collected with a 80-cm telescope. While also the WVDIAL can scan in any direction, it was operated in constant vertical-pointing mode during LAFI.

[1]        Späth, F., et al.: The land–atmosphere feedback observatory: a new observational approach for characterizing land–atmosphere feedback. Geoscientific Instrumentation, Methods and Data Systems (2023). DOI: 10.5194/gi-12-25-2023

[2]        Lange, D. et al.: Compact Operational Tropospheric Water Vapor and Temperature Raman Lidar with Turbulence Resolution. Geophys. Res. Lett. (2019). DOI: 10.1029/2019GL085774

How to cite: Behrendt, A., Schumacher, M., Lange, D., von Klitzing, L., Abbas, S., Branch, O., Mauder, M., and Wulfmeyer, V.: Studying Land-Atmosphere Feedback Processes With a Synergy of Six Scanning Lidars, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13239, https://doi.org/10.5194/egusphere-egu26-13239, 2026.

 Atmospheric turbulence mainly induced by Vertical Wind Shear (VWS) can alter significantly the accurate positioning of space launching vehicles due to any possible distortions in their heading angles during their early stages of the flights. In this study, we developed the observation-based real-time detection system of the objective magnitude of atmospheric turbulence derived from the VWS near the NARO Space Center (NSC) in South Korea for ensuring successful launch missions of currently planned and future space vehicles. Here, we estimated an objective turbulence intensity, as a function of Eddy Dissipation Rate (EDR) that is converted from the VWS based on directly measured wind data from a Doppler wind lidar and intensive field experiments of radiosondes at the NSC for launching missions. First, we applied rigorous quality control (QC) of wind observation data to remove and filter out spurious wind data, which resulted in a high degree of agreement between the radiosonde and Doppler wind lidar measurements. This allowed us to calculate more reliable VWS to be converted to EDR using the lognormal mapping technique. Probability density functions (PDFs) of the VWS in different seasons and altitudes were established, and then used to construct the best-fit curves of prescribed lognormal function by minimizing the root mean square errors from the actual PDFs. Using the mean and standard deviation of these best-fit curves, the relationships between VWS and EDR were finally obtained and used to develop a real-time EDR estimation algorithm based on the observed wind data at the NSC. Newly developed real-time EDR estimation will provide a critical information to make a final Go or No-Go decision of the launching missions by rapidly detecting VWS-based EDR signals at the NSC site.

Acknowledgement: This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI2022-00310 and the NARO Space Center Advancement Project of Korea Aerospace Administration.

How to cite: Kim, J., Kim, J.-H., Lee, D.-B., and Kim, S.-H.: Vertical Wind Shear and Turbulence Detection Using Doppler Lidar and Radiosonde at NARO Space Center in South Korea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15933, https://doi.org/10.5194/egusphere-egu26-15933, 2026.

In mesoscale models, turbulent kinetic energy (TKE) dissipation is commonly parameterized as a function of bulk TKE, implicitly assuming isotropic turbulence in the convective boundary layer (CBL). In this study, we use long-term Doppler lidar observations at the Land-Atmosphere Feedback Observatory (LAFO), University of Hohenheim, Stuttgart, Germany to evaluate this assumption. Two continuously operated Doppler lidars, one in vertical staring mode and one in six-beam scanning mode, provide high-resolution wind measurements within the CBL (Späth et al., 2023). We have analyzed the statistical relationships between vertical velocity variance <w’²>, TKE dissipation (Wulfmeyer et al., 2024), and TKE (Bonin et al., 2017) under daytime convective conditions (06:00–18:00 UTC). The results reveal a nonlinear relationship between <w’²> and TKE, with dissipation scaling to (<w’²>)^3/2. The TKE-based dissipation parametrization from Mellor-Yamada-Nakanishi-Niino (MYNN) shows only lower agreement (R² = 0.50) with lidar observation, whereas the <w’²>-based dissipation shows a significantly stronger agreement (R² = 0.80). Despite this difference, the turbulent length scales derived from TKE and <w’²> exhibits similar characteristics. These findings highlight limitations of bulk-TKE-based parameterizations and demonstrate the value of Doppler-lidar-based diagnostics for improving the turbulence representation in mesoscale models.

References:

Bonin et al., 2017, https://doi.org/10.5194/amt-10-3021-2017

Späth et al, 2023, https://doi.org/10.5194/gi-12-25-2023

Wulfmeyer et al, 2024, https://doi.org/10.5194/amt-17-1175-2024

How to cite: Abbas, S. S., Behrendt, A., and Wulfmeyer, V.: Evaluating Turbulent Kinetic Energy Dissipation Parametrizations Using Doppler Lidars in the Convective Boundary Layer, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18569, https://doi.org/10.5194/egusphere-egu26-18569, 2026.

We present the current status of our transportable, multi-purpose lidar units for investigating small- to large-scale processes in the atmosphere. An array of compact lidars with multiple fields of view will allow for measurements of temperatures, winds, aerosols and metals with high temporal and vertical resolution.

Our lidar units enable the investigation of Mie scattering (aerosols), Rayleigh scattering (air molecules), and resonance fluorescence (e.g. potassium atoms) from the troposphere (5 km) to the thermosphere (100 km). The unique frequency scanning laser and filter techniques allow multiple observations (wind, temperature, aerosols, metal density). The combination of a tunable alexandrite laser emitter and receiver enables high-resolution spectral characterization of the backscattered Doppler signals at day and night. In future, the relevance of such lidar networks will increase for improved weather prediction and long-term trends, monitoring of metal densities (meteoric and space debris impact) as well as calibration and validation of spaceborne missions.

We will present the progress of our lidar development in the IR and UV wavelength range, expanded measurement capabilities (e.g. aerosols, wind) and current results of measurements at 54°N and 69°N.

How to cite: Froh, J., Höffner, J., Mauer, A., Lüke-Mense, T., Eixmann, R., Ernst, F., Saavedra Garfias, P., Baumgarten, G., Munk, A., Scheuer, S., and Strotkamp, M.: From the Troposphere to Thermosphere: Compact Doppler Lidar units for observation networks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18094, https://doi.org/10.5194/egusphere-egu26-18094, 2026.