Search Results (5)

Accurate measurements of wind fields in the troposphere and stratosphere are essential for advancing atmospheric dynamics research, improving weather prediction, and supporting aerospace operations. However, a single Doppler lidar technique usually has limited capability to provide vertically extended wind profiles across both aerosol-rich lower altitudes and molecular-dominated higher altitudes. In this paper, we present a hybrid Doppler lidar system that combines a 355 nm scanning incoherent Rayleigh Doppler lidar with a 1550 nm coherent aerosol Doppler lidar for multi-scale wind field detection. The coherent Doppler lidar is used for boundary-layer wind retrievals, while the Rayleigh Doppler lidar, based on the double-edge technique, extends wind profiling from the upper boundary layer to approximately 40 km. Field deployments demonstrate continuous wind profiling from 50 m to 40 km, extending from the boundary layer to the stratosphere. Comparisons with radiosonde measurements show good agreement during the field campaigns, supporting the feasibility of this hybrid configuration for vertically extended wind profiling. The resulting high-resolution wind measurements across multiple atmospheric regions provide valuable data sources for studies of multi-scale circulation research, gravity wave dynamics, and climate-related atmospheric processes. Full article

Large language models (LLMs) offer new possibilities for natural-language interaction with geospatial analysis systems, but their use in remote sensing instrument data analysis remains limited by weak execution control, poor reproducibility, and limited integration with domain-specific computation. The paper presents an agent for Incoherent Doppler wind LiDAR (ICDL) data analysis, named ICDL-Agent, a tool-augmented LLM framework for remote sensing instrument workflows. The system maps conversational user requests to executable analysis pipelines for wind retrieval, uncertainty estimation, visualization, and higher-level diagnostics through structured planning over a registry of domain-specific tools. To improve execution reliability, the system combines schema-constrained workflow generation, shared-state reuse of intermediate scientific products, and validation with bounded repair. In addition to supporting routine LiDAR processing, the framework can generate new tools when required and adapt to related analytical tasks through domain-aware guidance and procedural documentation. We evaluate the system on multiple atmospheric wind-observation datasets in China and show that it faithfully reproduces the refined Doppler wind-retrieval pipeline, achieving representative R²/MAE values of 0.52/3.73 m/s against ERA5 and 0.80/2.31 m/s against radiosonde observations, while supporting downstream analyses such as profile comparison, climatological interpretation, and gravity-wave diagnostics. More broadly, this study demonstrates how constrained LLM orchestration can support LiDAR researchers, remote-sensing instrument teams, and geospatial analysts seeking transparent, reproducible, and automated scientific data-processing workflows. Full article

The rapid development of artificial intelligence (AI) and deep learning has revolutionized the field of data analysis in recent years, including signal data acquired by remote sensors. Light Detection and Ranging (LiDAR) technology is widely used in atmospheric research for measuring various atmospheric parameters. Wind measurement using LiDAR data has traditionally relied on the spectral centroid (SC) algorithm. However, this approach has limitations in handling LiDAR data, particularly in low signal-to-noise ratio (SNR) regions. To overcome these limitations, this study leverages the capabilities of customized deep-learning techniques to achieve accurate wind profile reconstruction. The study uses datasets obtained from the European Centre for Medium Weather Forecasting (ECMWF) Reanalysis v5 (ERA5) and the mobile Incoherent Doppler LiDAR (ICDL) system constructed by the University of Science and Technology of China. We present a simulation-based approach for generating wind profiles from the statistical data and the associated theoretical calculations. Whereafter, our team constructed a convolutional neural network (CNN) model based on the U-Net architecture to replace the SC algorithm for LiDAR data post-processing. The CNN-generated results are evaluated and compared with the SC results and the ERA5 data. This study highlights the potential of deep learning-based techniques in atmospheric research and their ability to provide more accurate and reliable results. Full article

Coherent Doppler wind lidar (CDWL) uses transmitted laser pulses to measure wind velocity distribution. However, the echo signal of CDWL is easily affected by atmospheric turbulence, which can decrease the signal-to-noise ratio (SNR) of lidar. To improve the SNR, this paper proposes a pulse accumulation method based on the cross-correlation function to estimate the phase of the signal. Compared with incoherent pulse accumulation, the proposed method significantly enhances the correlation between signals from different periods to obtain high SNR gains that arise from pulse accumulation. Using simulation, the study evaluates the effectiveness of this phase estimation method and its robustness against noise in algorithms which analyze Doppler frequency shifts. Furthermore, a CDWL is developed for measuring the speed of an indoor motor turntable and the outdoor atmospheric wind field. The phase estimation method yielded SNR gains of 28.18 dB and 32.03 dB for accumulation numbers of 500 and 1500, respectively. The implementation of this method in motor turntable speed measurements demonstrated a significant reduction in speed error—averaging 9.18% lower than that of incoherent accumulation lidar systems. In experiments that measure atmospheric wind fields, the linear fit curve slope between the measured wind speed and the wind speed measured via a commercial wind-measuring lidar can be reduced from 1.146 to 1.093. Full article

The Fabry–Perot interferometer (FPI) plays a crucial role as the frequency discriminator in the incoherent Doppler wind lidar. However, in the practical receiver system, reflections occurring between optical elements introduce non-normal incident components in the light beams passing through the FPI. This phenomenon results in the deformation of the FPI transmission spectral lines. Based on that, a theoretical model has been developed to describe the transmission spectrum of the FPI when subjected to obliquely incident light beams with a divergence angle. By appropriately adjusting the model parameters, the simulated transmission spectrum of the FPI edge channels can coincide with the experimentally measured FPI spectral line. Subsequently, the impact of deformations in the transmission spectrum of the two edge channels on wind measurements is evaluated. The first implication is a systematic shift of 30.7 m/s in line-of-sight (LOS) wind velocities. This shift is based on the assumption that the lidar echo is solely backscattered from atmospheric molecules. The second consequence is the inconsistency in the response sensitivities of Doppler frequency shift between Rayleigh signals and Mie signals. As a result, the lidar system fails to fully achieve its initial design objectives, particularly in effectively suppressing interference from Mie signals. The presence of aerosols can introduce a significant error of several meters per second in the measurement of LOS wind velocity. Full article