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.

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.

Calibration of an atmospheric lidar is often required due to variations in the electro-optical system. Rayleigh fitting commonly performed may fail under various conditions. Temporal and spatial variations both affect lidar signals. We hence opt for spatiotemporal analysis. We present a novel deep-learning (DL) lidar calibration model based on convolutional neural networks (CNN). We demonstrate our method on simulated data that mimics natural ground-based pulsed time-of-flight lidar signals. Such an approach can better address measurements with a poor signal-to-noise ratio (SNR) and provide a more frequent calibration.

This work presents a spectrally resolved Raman lidar (SRRL) for simultaneous measurement of volume backscattering coefficient spectra (backscatter spectra for short) of atmospheric three-phase water and fluorescent aerosols. The SRRL emits 354.8-nm laser light and records both N2Raman echoes around 386.7 nm and spectral signals in a specially modified spectral range of ~393–424 nm. By defining normalized spectra, a unique spectra decomposition approach is developed for successive retrieval of normalized spectra components of fluorescent aerosols, water vapor, ice water, and liquid water. Furthermore, the backscatter spectra of each component are obtained by referencing the N2Raman signals. Three typical measurement cases are provided. For the clear-day case, the lidar-measured normalized water vapor Raman spectra are found to be nearly invariant in shape and can serve as background spectra reference for decomposition of mixed-phase water Raman spectra. Besides, fluorescence backscatter intensities are usually weak enough to be neglected in clear areas. For the fluorescence case, the fluorescence backscatter intensities become much stronger to prevent accurate measurements of water vapor. For the mixed-phase water virga case, the decomposed backscatter spectra of condensed ice and liquid water indicate the coexistence of ice water and less amount of liquid water in the falling water virga. In conclusion, this kind of SRRL is believed to have provided the potential for simultaneous and accurate profiling of atmospheric water vapor and cloud liquid/ice water and opened up new perspectives for studies of cloud–aerosol interaction.

Methods based on statistical learning have become prevalent in various signal processing disciplines and have recently gained traction in atmospheric lidar studies. Nonetheless, such methods often require large quantities of annotated or resolved data. Such data is rare and requires effort, especially when exploring evolving phenomena. Existing simulators and databases primarily focus on atmospheric vertical profiles. We propose the Atmospheric Lidar Data Augmentation (ALiDAn) framework to fill this gap. ALiDAn serves as an end-to-end generation and augmentation framework of spatiotemporal and multi-wavelength resolved lidar simulated data. ALiDAn employs a hybrid approach of physical models, data statistics, and sampling processes. Additionally, it takes into account geographical and seasonal characteristics of aerosols, meteorological conditions, along with short- and long-term phenomena that affect lidar measurements. This approach can provide diversified data and robust benchmarks to assist in developing and validating new lidar processing algorithms. We demonstrate simulations compatible with a pulsed time-of-flight lidar. Our approach leverages a broader use of existing databases and can inspire similar data augmentation to other types of lidars and active sensors.