Differential absorption lidar (DIAL) enables range-resolved measurements of near-surface ozone. However, practical on/off wavelength pairs for ozone DIAL typically require separations of several nanometers, which introduce wavelength-dependent aerosol extinction and backscatter effects into the ozone retrieval. In this study, we develop a forward-simulation framework that couples representative vertical distributions of ozone and aerosols to quantify wavelength-selection-induced bias in lower-tropospheric ozone DIAL retrievals, using signed deviation in the forward simulations and relative deviation for normalized bias evaluation. Based on the simulation results, a solid-state, Raman-shifting-compatible wavelength pair at 280/295 nm is identified as a near-optimal configuration.To mitigate aerosol-induced bias, an explicit aerosol-transmittance correction is implemented using auxiliary elastic backscatter channels at 355 and 532 nm, and is validated against co-located daytime ozonesonde launches conducted at Xilinhot, China.

Ultraviolet differential absorption lidar (DIAL) enables range-resolved profiling of tropospheric ozone concentrations; however, aerosol-induced spectral differences between on-line and off-line channels represent a potential source of uncertainty in ozone retrieval. Another non-negligible uncertainty source arises from the temperature dependence of ozone absorption cross sections. In this study, we propose a measurementconstrained joint correction framework for a 280/295 nm ozone DIAL, which leverages co-located multiwavelength Raman lidar observations for constraint. Specifically, aerosol lidar ratios and & Aring;ngstro & uml;m exponents derived from multi-wavelength Raman inversion are integrated into the aerosol correction terms of the DIAL retrieval equation, while temperature profiles retrieved by a pure rotational Raman lidar are employed to obtain altitude-dependent ozone absorption cross sections with high accuracy. Simulations were performed to quantify the impacts of aerosol-related parameters and temperature-dependent absorption cross sections on ozone retrieval results, and the findings demonstrate that both factors can introduce systematic deviations. Field measurements conducted in Xilinhot (northern China) were validated against co-located ozonesonde data. The results show that under enhanced aerosol loading conditions, the aerosol correction reduces the profile-averaged mean relative deviation by up to 23.4%, whereas the temperature correction yields a relatively weaker reduction of approximately 1% within the 0.5-3 km altitude range. Overall, the proposed joint correction method improves the agreement between DIAL-retrieved ozone profiles and ozonesonde observations across all four different scenarios intercomparison cases investigated in this study.

Optical filtering based on orbital angular momentum (OAM) modulation can effectively suppress background noise and multiple-scattering light in water, thereby improving the signal-to-noise ratio (SNR) of underwater lidar. Although OAM modulation has been investigated in previous studies, a full-link numerical model is still required to evaluate overall system detection performance. In this work, a full-link numerical simulation model of an underwater lidar system is developed based on the random phase screen method, incorporating the transmitter, underwater beam propagation, target characteristics, and receiver. The model can be used to investigate the effects of turbulence strength, laser energy, and telescope diameter on system performance. Under similar parameter conditions, comparison with water-pool experimental results shows good agreement in the maximum detection range, reaching approximately 15 attenuation lengths, which validates the model. Based on this model, the influences of turbulence strength, laser energy, and telescope diameter on the maximum detection range are analyzed, together with the effect of turbulence on beam divergence angle and horizontal resolution. The simulation results show that increasing turbulence strength leads to spot broadening and degradation of horizontal resolution, and this degradation becomes more pronounced when the field of view (FOV) is reduced. The proposed model provides an effective tool for evaluating the detection performance of underwater lidar systems employing OAM-based optical filtering.

Black Si (b-Si) synthesized via femtosecond (fs) laser processing has emerged as a pivotal candidate material for next-generation infrared (IR) photodetectors. Its unique hierarchical micro/nanostructures, in synergy with supersaturated impurity doping, enable dual core functionalities-broadband light trapping and sub-band gap absorption-which collectively extend the spectral response of Si-based devices from visible to near-infrared. Endowed with inherent advantages including complementary metal-oxide-semiconductor (CMOS) process compatibility, low-cost scalable fabrication, and industrial adaptability, b-Si provides a viable technical route to address the intrinsic limitations of traditional III-V and II-VI semiconductor materials, such as lattice mismatch-induced epitaxial defects, complex growth processes, and prohibitive production costs. This review systematically summarizes recent advances in fs laser-processed b-Si for IR detection, with a focus on three interrelated thematic areas: (1) the fundamental physical mechanisms governing enhanced light absorption, encompassing multiple scattering effects and impurity band-mediated sub-band gap transitions; (2) key performance optimization strategies, including precision morphology engineering, gradient impurity/co-doping protocols, and advanced post-processing techniques (e.g., heterostructure construction, plasmonic resonance enhancement, and atomic-layer-deposition-based surface passivation); (3) integration methodologies between b-Si and diverse photodetector architectures, such as planar/vertical p-n junctions, Schottky barrier diodes, and hybrid van der Waals heterostructures. Finally, the critical challenges restricting the practical deployment of fs laser-engineered b-Si are delineated, and prospective development directions are proposed-including CMOS-compatible mass fabrication, hetero-material integration for mid-wave IR extension, multispectral detection system design, and long-term stability enhancement via novel passivation strategies. This review aims to establish a comprehensive reference framework for rational design, performance optimization, and industrial translation of high-sensitivity, broadband, low-noise Si-based IR detectors.

The origins, spatial distribution, and diffusion mechanisms of aerosols hold practical guiding significance for regional haze governance. The vertical and horizontal fluxes of aerosols serve as effective parameters for assessing the diffusion efficiency of aerosols, but they are less exploited due to insufficient observations. This study uses polarization lidar to differentiate between the aerosol sources of dust and non-dust and to estimate the mass concentration profiles of each. Combining the wind profiles acquired from Doppler wind lidar, the vertical and horizontal fluxes profiles of two type aerosols are calculated. This approach is designed to account for the influence of local aerosol transport mechanisms on air pollution, enabling a more precise reflection of the internal variations within a particular region. A winter haze event in Beijing from 2-4 November 2023 was analyzed to distinguish the vertical distribution of and mass concentration and fluxes of aerosols brought by dust transported from the northwest monsoon and those from anthropogenic emissions within the North China plain area. Further analysis of different wind zones revealed that the aerosol concentration and fluxes from urban areas (regions with a higher density of anthropogenic sources) can be used to assess the local pollutant diffusion capacity, as well as the influence of vertical turbulence on ground PM10 concentrations. Taking Beijing as an example, this study investigated the diffusion characteristics of urban aerosols, ultimately providing technical means and data references for early warning of urban air pollution and assessment of air pollution control measures.

Abstract. Cloud-layer identification is a key prerequisite for automated ground-based lidar processing, but remains challenging in Raman lidar networks because of incomplete near-range overlap, weak high-level cloud echoes, day–night signal-to-noise ratio (SNR) contrasts, energy fluctuations, and aerosol–cloud ambiguity. Leveraging the China Aerosol Raman Lidar NETwork (CARLNET), we propose a multi-channel cloud-layer identification framework that operates on 355/532/1064 nm elastic signals and 355/532 nm volume depolarization ratio, reducing dependence on absolute calibration and retrieved optical products. The framework combines an anisotropic encoder–decoder architecture with a three-stage training strategy, including self-supervised pretraining, traditional-algorithm-guided probabilistic distillation, and fine-tuning with limited expert refinement. Strict cross-site and cross-time evaluation on held-out sites shows that, without using any test-site labels, the framework achieves an F1 score of 0.9371 and reduces the mean absolute errors of cloud-base and cloud-top heights to 113 m and 213 m, respectively, outperforming training without pretraining by approximately 50 m and 70 m and the conventional baseline for cloud-top height by about 400 m. A labeled-data-size ablation further shows improved label efficiency, with near-plateau performance reached at about 40 labeled days. Consistency checks against co-located radiosonde moist-layer indications support the realism of identified high-level weak-echo clouds and the robustness of cross-site deployment. These results demonstrate a transferable and calibration-decoupled cloud-layer identification technique for network-scale Raman lidar processing.

This study describes the design, implementation, and performance evaluation of an Advanced Multi-Parameter Atmospheric Lidar (AMPAL) system developed for continuous full-day profiling of aerosols, clouds, and key meteorological parameters. The AMPAL system operates simultaneously at wavelengths of 355 nm, 532 nm, and 1064 nm and employs a total of 12 detection channels, enabling concurrent retrieval of atmospheric temperature, water vapor, and aerosol optical properties, including extinction coefficients, backscatter coefficients, and depolarization ratios at all three wavelengths. This configuration supports comprehensive aerosol characterization through 3α + 3β + 3δ profiling. To retrieve extinction coefficients in the near-infrared, a rotational Raman channel centered at 1056 nm is implemented, selected for its temperature-insensitive characteristics, while conventional vibrational Raman channels are utilized for the 355 nm and 532 nm wavelengths. A coaxial optical configuration is adopted for the 532 nm laser beam and its corresponding receiver channels, resulting in a complete overlap height of approximately 300 m, as verified through comparison with a reference lidar system. Comparative analyses indicate strong signal consistency across all 12 channels up to an altitude of 10 km. Validation against radiosonde observations shows that the AMPAL system achieves temperature profiling ranges of up to 8 km during daytime (sunrise) and 10 km during nighttime (sunset), with deviations within ± 0.5 K at a vertical resolution of 240 m and a temporal resolution of 30 min. Measurements of water vapor mixing ratio cover similar altitude ranges, exhibiting deviations below ± 0.5 g kg⁻1 at comparable spatial and temporal resolutions. Finally, results from continuous all-day observations of multiwavelength aerosol, cirrus cloud and planetary boundary layer aerosols optical properties are presented, highlighting the system’s performance for stable, long-term atmospheric monitoring.

Accurate validation of spaceborne lidar data is fundamental for reliable quantification of aerosol vertical distributions, which strongly influence air quality and climate effects. This study presents a comparative analysis of aerosol profiles from the 532 nm High-Spectral-Resolution Lidar (HSRL) onboard China's DQ-1 satellite (ACDL) and ground-based observations from the Asian Dust and Aerosol Lidar Observation Network (AD-Net). Using one year of measurements under minimized spatiotemporal mismatches at three representative coastal stations (Matsue, Tokyo, Hedo), we quantify the sources of observational differences. Results show that discrepancies in detection targets (aerosols/clouds) dominate the total variance (>75%), while instrumental differences contribute 10-25%. Horizontal wind speed, particularly its north-south component, correlates more strongly with discrepancies than vertical wind speed, except in high-concentration aerosol layers where vertical motions become influential. Furthermore, larger differences are associated with increased aerosol extinction coefficients (alpha) and particle depolarization ratios (delta). This work demonstrates that integrated applications of multi-platform lidar data must account for both meteorological controls on aerosol transport and particle microphysical properties. These findings provide a quantitative validation framework for current and future spaceborne HSRL missions and support the integrated application of multi-platform lidar observations in regional aerosol monitoring, air quality assessment, and climate effect research.

The China Aerosol Raman Lidar Network (CARLNET), developed by the China Meteorological Administration, currently comprises 49 multiwavelength polarization Raman lidars used for meteorological and atmospheric-environment monitoring. Timely and automatic quality assessment of the lidar raw signal is vital for a large atmospheric lidar network. This study proposes a quality assessment method of lidar raw data for the CARLNET. By scoring three factors, signal saturation at near-range, Rayleigh fit and effective detection range, and weighting each influence factor according to its importance, each lidar raw data is tagged by a composite score. These scores reflect the quality of lidar raw data, as well as potential issues of lidar systems. Three lidars under three typical weather scenarios are used to analyze the impact of observation scenarios on lidar raw data, and the results show that the proposed method can effectively distinguish the lidar raw data quality under different scenarios. By analyzing the scores of lidar raw data, two potential hardware issues (optical-axis misalignment and signal-receiving issues) are identified, which provide guidance for equipment maintenance. In addition, we applied the method to one-year CARLNET measurement data. Temporally, five representative sites were selected for analysis of their annual data, revealing the seasonal and overall scores of the raw data. Spatially, the signals at the 355 nm, 532 nm, and 1064 nm channels of 49 nationwide distributed lidars were evaluated and categorized into six groups based on their scores, which provides support for lidar network data quality monitoring, operational applications, and scientific research.

The generation of continuous lidar signals for calibrating analog data acquisition boards is a relatively mature technology. However, when the intensity of the lidar signal reaches the level of single-photon detection, there is few effective solutions for generating discrete photon pulse signals with random amplitudes and arrival time. It is difficult to test and calibrate photon counting data acquisition boards. This paper introduces a method for generating discrete photon pulse signals. For the specific implementation, the Xilinx 7000 series FieldProgrammable Gate Array (FPGA) is used as the development platform. And the Monte Carlo algorithm is employed to calculate the amplitude and temporal position of each photon pulse. This enables the real-time generation of each photon's signal, which is then output through a 14-bit Digital-to-Analog Converter (DAC) module. The technology described in this paper could be applied for performance testing and calibration of both the photon counting and the analog data acquisition boards by providing controlled electrical signals.

The utilization of array detection technology is pivotal in augmenting the capability of laser ranging systems to detect echo signals. It can amplify the likelihood of successful detection. But there will be variations amongst the channels due to the array detector's inconsistency. A response time model is developed based on the detection mechanism of superconducting nanowire single photon detectors (SNSPD). This model is employed in conjunction with ground target calibration measurements to rectify time bias among the channels within the 4-channel Satellite Laser Ranging (SLR) system. The findings indicate that this method adeptly manages channel deviations and mitigates data fluctuations by rectifying the inherent range walk error of SLR measurement data, resulting in improved data quality. The average precision of satellite measurement data in the first half of 2024 increased by 2.962 . 962 mm .

A 3-5 µm mid-infrared (MIR) laser has a wide range of applications in biological tissue ablation, remote spectral fingerprint recognition, and directional infrared countermeasures. However, the performance of conventional MIR lasers has long been hindered by restricted wavelength radiation, spectral power efficiency, and system stability. Here, a highly efficient, compact, and stable MIR light source is reported, which is directly generated from a supercontinuum (SC) laser with a long wavelength edge of 4.2 µm in a 7 µm core diameter fluorotellurite fiber. Based on the integration of a high-peak-power pump light source and a small-core-diameter nonlinear medium, efficient nonlinear frequency conversion from traditional near-infrared laser to mid-infrared laser has been achieved, resulting in a significantly enhanced MIR spectrum of 3.7 µm, exceeding the pump peak of 2 µm by more than 12 dB. The pump conversion efficiency is 50.8%, with 94.3% of the spectral power distributed above 2.4 µm and 71.4% above 3 µm. This study has opened up a feasible avenue for obtaining high-efficiency mid-infrared band lasers that meet practical application needs.

High quality, low spatially frequency ripples on silicon (Si) surface were directly fabricated by femtosecond (fs) laser irradiation in air and decorated with Au nanoparticles (NPs) developing large-area, low-cost substrates for surface-enhanced Raman spectroscopy (SERS). Rippled subwavelength structures exhibit a significant SERS response thanks to both an electromagnetic (EM) field enhancement, originating from the narrow gaps between adjacent ripples, and a plasmonic coupling among the Au NPs subsequently deposited via magnetron sputtering. SERS mapping shows a good uniformity, with +/- 8 % deviation over a 15 x 20 mu m2 area, and reveals a large disparity in the signal strength with respect to that displayed by the grooves with micron-sized period produced at higher laser fluence, for which a two order of magnitude lower SERS signal is achieved. The SERS substrates with low spatial frequency ripples are able to detect a Raman analyte at a minimal concentration of 10-12 M for Rhodamine B (RhB) and 10-11 M for 4-hydroxybenzoic acid (4-MBA), respectively. Furthermore, a good agreement is attained between the values of the Raman enhancement factors (EFs) obtained experimentally and by simulations through Finite Elements Method calculations. Our findings demonstrate that the proposed approach based on low spatial frequency laser induced periodic surface structures (LSFL) on Si decorated with Au NPs by magnetron sputtering can provide a feasible and efficient method for the fabrication of SERS substrates with high Raman detection capability of analytes in trace amounts.

We report on ultrasensitive detection of dangerous trace elements in food and water by elaborating reusable, crystalline silicon surface-enhanced Raman scattering (SERS) substrates via femtosecond laser processing. Limits of detection (LoDs) of 10-7 μg/L for microcystin-LR (a waterborne algal toxin) and 10-12 M for malachite green (a banned aquaculture additive) are achieved by the fabricated substrates with experimental results closely matching the predictions of finite-element simulations. Interestingly, substrates fabricated in ambient air exhibit an order-of-magnitude higher SERS signal intensity than those produced under vacuum, evidencing an important influence of the processing conditions. The signal enhancement arises from synergistic electromagnetic effects: (1) subwavelength inter-ripple cavities induce intense field confinement, (2) laser-induced nanoscale roughness from nanoparticle redeposition creates additional field localization sites, and (3) Au nanoparticle decoration establishes hybrid plasmonic coupling via particle-substrate and interparticle interactions, generating gradient-distributed 3D hotspots with simulated enhancement factors (EFs) > 1010. SERS mapping demonstrates high signal uniformity across the platform, while also revealing distinct intensity variations between regions with differing surface roughness modulated by processing conditions. Our experimental findings validate a simple and effective strategy for food safety and environmental monitoring with laser-fabricated SERS substrates, also highlighting a critical influence of the processing environment on their final sensitivity.

Water vapor is an active trace component in the troposphere and has a significant impact on meteorology and the atmospheric environment. In order to meet demands for high-precision water vapor and aerosol observations for numerical weather prediction (NWP), the China Meteorological Administration (CMA) deployed 49 Raman aerosol lidar systems and established the first Raman–Mie scattering lidar network in China (CARLNET) for routine measurements. In this paper, we focus on the water vapor measurement capabilities of the CARLNET. The uncertainty of the water vapor Raman channel calibration coefficient (Cw) is determined using an error propagation formula. The theoretical relationship between the uncertainty of the calibration coefficient and the water vapor mixing ratio (WVMR) is constructed based on least squares fitting. Based on the distribution of lidar in regions with different humidity conditions, the method of real-time calibration and quality control based on radiosonde data is established for the first time. Based on the uncertainty requirements of the World Meteorological Organization for water vapor in data assimilation, the calibration and quality control thresholds of the WVMR in regions with different humidity conditions are determined by fitting real-time lidar and radiosonde data. Lastly, based on the radiosonde results, the calibration algorithm established in this study is used to calibrate CARLNET data from October to December 2023. Compared with traditional calibration results, the results show that the stability and detection accuracy of the CARLNET significantly improved after calibration in regions with different humidity conditions. The deviation of the Cw decreased from 12.84~18.47% to 5.41~11.54%. The inversion error of the WVMR compared to radiosonde decreased from 1.05~0.46 g/kg to 0.82~0.34 g/kg. The reliability of the improved calibration algorithm and the CARLNET’s performance have been verified, enabling them to provide high-precision water vapor products for NWP.

A novel method for fabricating ultrafine, large-area convex microlens array (MLA) on silicon substrates is demonstrated by combining femtosecond laser processing with subsequent wet etching. The resulting MLAs features square aperture with a radius as small as 10 mu m and an ultra-short focal length of 30 mu m, specifically designed to maximize light concentration onto the sensors of HgCdTe infrared detectors. The square aperture geometry ensures uniform focusing of incident infrared light while preventing sensor damage from excessive energy concentration and effectively utilizing dead zones typically present in circular-aperture designs. Bessel beam was chosen for processing the MLA due to its non-diffraction properties and superior capability compared to the Gaussian beam in shaping microstructures with large depth-to-diameter ratios. The processed MLA enables to collect over 88 % of infrared light onto the infrared focal plane sensors, resulting in nearly a twofold enhancement in effective utilization efficiency (EUE) for the HgCdTe infrared detector. The silicon substrates, after being processed and fully equipped with MLA on their surfaces, are capable of gathering more than 88 % of infrared light and directing it onto the infrared focal plane sensors, resulting in a nearly twofold improvement in the EUE of the HgCdTe infrared detector. These results demonstrate an effective approach for fabricating ultrasmall microstructures and hold significant promise for enhancing the energy efficiency of infrared detection systems.

Accurate wind speed measurement in coherent wind lidar systems fundamentally depends on proper calibration. Traditional flywheel-based calibration measures the speed of hard targets but suffers from a five-order-of-magnitude discrepancy compared to aerosol backscatter, making it difficult to simulate realistic atmospheric return signals. Alternatively, wind cups provide atmospheric wind speed measurements but rely on specific wind conditions that are inherently unpredictable and often require extended waiting periods. These limitations significantly hinder the effectiveness of both methods for precise system calibration. To overcome these challenges, this study proposes a method for simulating atmospheric wind speed profiles and aerosol scenarios to validate coherent wind lidar performance. In this approach, backscatter signal intensity is simulated using amplitude modulation (AM), while wind speed variations with altitude are simulated using frequency modulation (FM). The proposed method is suitable for validating and optimizing coherent wind lidar performance, including measurement accuracy, wind speed resolution, and retrieval algorithms. The study also investigates the influence of aerosol backscatter intensity on wind speed retrieval accuracy. Results indicate that wind speed retrieval errors increase when the carrier-to-noise ratio (CNR) is either too high or too low. The method enables quantitative identification of the optimal CNR value, which increases with wind speed. Furthermore, the proposed approach achieves a wind speed resolution of approximately 0.0018 m/s across a range of ±31 m/s, meeting the specific requirements for coherent wind lidar calibration.

Accurate identification of clear-air echoes is crucial for reliable cloud boundary detection using ground-based radar. The clear-air echo classification method in the Cloudnet processing chain, which depends on temperature and the depolarization ratio (LDR), faces issues with height-based false alarms and misclassifications during precipitation, especially when LDR data are missing. This study introduces and assesses a deep learning (DL) algorithm for identifying clear-air echoes across multiple sites and climatic conditions. Compared to the Cloudnet algorithm, the DL model provides more continuous classifications and notably reduces errors—reducing cloud-base height underestimation by 19.5% and false detection of meteorological echoes by 1.7%. Furthermore, seasonal analyses of the 1-year dataset at Cloudnet sites with different geophysical features (Jülich, Germany and Lampedusa, Italy) reveal that the influence of temperature on clear-air echoes varies significantly across environments. As a result, a single temperature-based probability function is insufficient to robustly distinguish non-meteorological echoes under diverse climatic conditions. These findings highlight the robustness of DL methods and their potential to enhance cloud radar data quality in complex observational environments.

Hydrometeors are essential to climate change and radiative budget research, making its observation important. Ka-band radar, valued for high spatial resolution and strong penetration, is widely used for observing hydrometeors. But it is prone to interference from insects and turbulence, called clear-air echoes or clutter. To mitigate interference from clear-air echoes, a deep learning dataset using Ka-band radar and lidar observations is designed. It incorporates synchronized radar-lidar data and expert-reviewed manual corrections, covering diverse weather conditions to support robust algorithm evaluation and development. Analysis shows clear-air echoes account for 16.7% of echoes within a range of 0-15 km, causing significant errors in hydrometeors observation if unclassified. A UNet-based baseline model for meteorological echo recognition is presented. The model with squeeze-and-excitation (SE) modules achieves 95.6% mean intersection over union (mIoU), with inference time reduced to 26.9% of the previous algorithm and using just 3.2% GPU time. Sensitivity analysis indicates that reflectivity and Doppler velocity channels contribute the most to prediction accuracy (15.1% and 1.7%, respectively), while the linear depolarization ratio (LDR) adds less than 1.0%. Complementary statistical metrics-including Kullback-Leibler (KL) divergence and Kolmogorov-Smirnov (KS) statistic-further confirm the superior separability of spatial texture features derived from reflectivity. These findings demonstrate that LDR, although useful in manual identification, is not essential for automated classification. This study contributes an open-access, annotated radar-lidar dataset and strong baseline models, offering a reliable benchmark for future research on radar echo classification.

We developed a new methodology for the improved identification of particle microphysical parameters (PMPs) from multiwavelength lidar measurements. The underlying problem is underdetermined and relates to the class of ill-posed problems. In this study, we apply our new methodology to lidar measurements. We investigate how results obtained for typical aerosol mixtures (AMs) in the atmosphere can be improved if information about aerosol types and the number of aerosol types in such an AM is available. We have developed a methodology of Aerosol Typing from Linear estimations for the Analytical Separation (ATLAS) of complex aerosol mixtures in the first part of our study. ATLAS allows us to decompose a complex AM into individual aerosol types in terms of optical data measured by lidar. Optical data derived for individual aerosol types are then separately considered and inverted into Algorithm). We apply our new two-stage (ATLAS-TiARA) synergetic methodology to three lidar-measurement cases corresponding to two-, three-, and four-component AMs. The measurements we use for this study were carried out in the frameworks of the ORACLES-2016 and SHADOW field campaigns and lidar observations at the University of Lille (France), respectively. Results of the new methodology agree with results obtained with data collected by in situ instruments during the ORACLES-2016 campaign. Deviations of number concentration and single-scattering albedo at 532 nm retrieved with the new methodology from respective in situ measurements do not exceed 25% and 0.05, respectively. We find both fine- and coarse-mode particles from all three measurement cases. Fine-mode particles are represented by urban and smoke (haze), whereas coarse-mode particles can be attributed to dust, marine, and pollen aerosols. In summary, the methodology allows us to obtain a more detailed insight into microphysical particle properties. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.