In this study, we present AecroFormer, a novel neural network model for aerosol microphysical retrieval from multi-wavelength Raman lidar observations. Integrating Multi-Head Attention (MHA) into a lightweight MLP backbone, AecroFormer addresses the limitations of traditional retrieval methods in terms of accuracy, noise sensitivity, and computational efficiency. The model effectively captures the nonlinear and coupled relationships inherent in inversions based on Mie light-scattering theory, which assumes spherical particle geometry. It is trained on 1.1 million synthetic samples using various channel combinations (e.g., 3β+3α, 3β+2α), where β represents particle backscatter coefficients and α represents particle extinction coefficients at one or more of the commonly used observation wavelengths (355, 532, and 1064 nm). Under 10% Gaussian noise, AecroFormer achieves mean absolute errors below 0.07 (mr) and 0.015 (mi) of the real and imaginary part of the complex refractive index of aerosol particles. AecroFormer keeps the relative error of the effective radius (re) of the investigated monomodal particle size distributions below 30%. The per-point inference time is 7.4×10⁻⁵ seconds. The 3β+2α configuration is identified as the minimal viable Raman lidar setup, with extinction channels critical for constraining refractive index–related parameters. Validation is limited by the availability of suitable datasets. However, we identified one published case that enabled testing the AecroFormer concept using real lidar and in-situ aircraft observations, confirming its physical consistency under realistic atmospheric conditions. Overall, AecroFormer offers a robust, generalizable, and efficient solution for lidar-based aerosol retrieval, demonstrating strong potential for operational deployment.

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%. Complementary statistical metrics—including KL divergence and 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.

The optical components of space-borne sensors are subject to environmental degradation, resulting in changing response characteristics, especially at different wavelengths. Consequently, the implementation of radiometric calibration is imperative to ensure the accuracy and reliability of the sensors. Calibration requires the capability to provide known excitations across the sensor’s spectral range, which is difficult to achieve with the existing means. Thus, this letter proposes a ground-based multiwavelengths active light source for nighttime absolute radiometric calibration (ANARC), capable of generating light with a uniform intensity distribution, three monochromatic wavelengths, and variable intensity within the satellite detection field of view. The absolute intensity of ANARC is precalibrated by an unmanned aerial vehicle (UAV) equipped with an optical power meter. The atmospheric transmittance during satellite overpasses is measured by co-located lidar systems. We conducted two test experiments targeting the NOAA-20 satellite. The deviations of the top-of-atmosphere (TOA) ANARC emissivity from the measured emissivity from the Visible/Infrared Imaging Radiometer Suite Day Night Band were −8.06% and 1.02%, respectively. At the same time, ANARC can cover the dynamic range of the high gain stage in the low light band (LLB) of the moderate resolution spectral imager-low light (MERSI-LL) in FengYun-3 E. This preliminary verification suggests the feasibility of using ANARC for nighttime sensor calibration, which provides a new opportunity to improve radiometric calibration accuracy. It could also facilitate hyperspectral sensor calibration due to three monochromatic wavelengths of ANARC.

The uncertainty of water cloud feedback on radiative forcing is one of the largest obstacles to producing confident projections of the global climate. Sufficient measurements of water clouds are crucial to addressing this issue. However, existing techniques based on remote sensing or in situ instruments face limitations in data capacity attributed to the short lifetime, high temporal variability, and complex vertical structure of water clouds. In this study, taking advantage of a dual-field-of-view (dual-FOV) high-spectral-resolution lidar (HSRL), we developed a novel algorithm to obtain diurnal simultaneous profiles of water cloud optical and microphysical properties with high temporal-spatial resolution. This technique does not rely on the widely used subadiabatic assumption about the vertical structure of water clouds. The retrieval algorithm, validated by simulations and cloud radar measurements, was applied to field experiment data collected at the Beijing and Hangzhou sites in China. The relationship functions between water cloud properties are presented to enhance our understanding of the underlying processes. Furthermore, the vertical distributions of retrieved properties are compared to the subadiabatic assumption. The dual-FOV HSRL technique enables comprehensive observations, enhancing our understanding of water clouds and providing significant insights into the interactions among clouds, aerosols, precipitation, and radiation.

Cloud vertical structure detection is essential for understanding atmospheric dynamics. Currently, cloud boundaries can be effectively identified based on lidar and millimeter-wave radar. However, how to integrate the two observation methods and remove the interference of rainfall on cloud identification are crucial for precise detection of cloud boundaries. This study develops a robust cloud boundary detection method combining radar and lidar observations with ability to identify rainfall effectively. Consistency analysis at Sheyang meteorological station using radiosonde data showed that lidar detected 41.1% of clouds and radar detected 93.3% of clouds compared to composite detection. The composite method overestimated the cloud base by 855.1 m and underestimated the cloud top by 551.2 m compared to radiosonde, as radiosonde measurements are affected not only by drift but also by rainfall, which mainly affects cloud base detection. Utilizing Doppler velocity and the lidar-radar cloud base difference improved rainfall detection by 41.4% over Doppler velocity alone. Observations are consistent with ground-based rain gauge, with Doppler velocities providing good identification of significant rainfall. Also, different cloud bases detected by lidar and radar providing additional identification of drizzle. With the rainfall removed, the error of rainwater path offered by microwave radiometry during rainfall is reduced by 32.4%. Overall, this study proposes a threshold-insensitive lidar-radar composite cloud identification method. It has good robustness and more precise detection of cloud boundaries for its ability to identify vertical rainfall regions.

Knowledge on the aerosol distribution in the Earth's atmosphere is not sufficient to calculate or even estimate the impact of aerosols on global and regional climate. One of the techniques to gain more knowledge in this field is advanced lidar remote sensing. Indeed, there are several problems connected with the retrieval of microphysical parameters of the aerosol from the data gained by Raman lidar measurements. The calculation can be reduced to two basic steps; in the first step, the aerosol extinction and backscatter profiles have to be extracted from the Raman signals, then those profiles are used for the retrieval of the microphysical properties. In the framework of the EARLINET-ASOS project, new algorithms for both of these parts of the problem are being developed with the long-term goal of being used for a continuous data evaluation of the data produced by the EARLINET stations.