Jilin Meteorological Bureau

To address the issues of low calibration accuracy, complex system structure, and cumbersome detection processes in traditional angular measurement equipment, this study proposes an innovative Opto-Mechanical coupled high-precision angular measurement turntable solution based on the principle of reciprocal angular displacement measurement. By systematically analyzing the structural characteristics and error distribution patterns of the turntable, a comprehensive error mathematical model based on relative pose matrices was established, enabling quantitative characterization of the error characteristics in the high-precision angular measurement turntable system. The three-point reverse decoupling algorithm, combined with a systematic continuous error compensation strategy, effectively suppresses installation errors of the components under test on the turntable. Furthermore, by optimizing BP neural network parameters using an improved particle swarm optimization algorithm, the system's error compensation performance was significantly enhanced. Experimental results demonstrate that the compensated system achieves a calibration accuracy of 0.38″, meeting the sub-arcsecond precision calibration requirement. The post-compensation overall error distribution remains within ±0.5″, and the reliability and accuracy of the turntable system were thoroughly validated through multiple comparative experiments. This study provides a novel technical approach for high-precision angular measurement.

To address the challenge of local disparity estimation errors caused by large occlusions in aerial stereo imagery, an UGNet is proposed for dense matching. The network begins with a multi-scale cost aggregation-based disparity uncertainty estimation module, which generates an initial coarse disparity map while estimating uncertainty to effectively localize occlusion areas. Subsequently, an uncertainty-guided GRU-based disparity optimization module is introduced to iteratively reconstruct the disparity map, incorporating cross-correlation and both local and global contextual information. This approach mitigates error propagation in heavily occluded areas and improves estimation accuracy. Experimental results on the public WHUStereo and UAVStereo datasets demonstrate that the proposed method significantly outperforms existing dense matching networks in terms of EPE and 3PE, particularly in occlusion areas. The results indicate strong potential for accurately processing urban aerial stereo imagery under severe occlusion conditions.

Atmospheric fronts are crucial for weather forecasting as they are often associated with intense weather phenomena. Current machine learning methods for identifying atmospheric fronts directly input multiple meteorological element data into networks for training and prediction, without considering feature conflicts among these elements caused by special weather conditions or geographical factors. Such conflicts can increase training difficulty and reduce identification accuracy. To address this, this study proposes the adaptive fuser (AF)‐UNet model, which integrates an adaptive fusion module (AFM) to resolve feature conflicts in multielement meteorological data and employs a dual loss function during training to mitigate severe class imbalance in labels. Experimental results demonstrate that AF‐UNet outperforms existing methods in accuracy and robustness, achieving a critical success index (CSI) of 69.8%, probability of detection of 79.8%, and accuracy of 84.6%. Our work tackles the challenge of feature conflicts in multielement meteorological data that impair network training and recognition accuracy in current machine learning approaches, providing an effective solution to significantly improve atmospheric front identification.

This paper proposes a hybrid coherent integrationbased joint detection method for moving target detection in distributed multiple-input multiple-output (MIMO) wide band phased array systems under complex terrain backgrounds. The method achieves long-time coherent integration within channels through generalized Radon-Fourier transform (GRFT), effectively addressing the energy dispersion problem caused by range cell migration of high-speed targets. Meanwhile, it employs time-delay compensated non-coherent integration techniques to eliminate phase mismatch between multiple channels, significantly enhancing the detection capability for weak targets. Simulations demonstrate that the hybrid coherent energy integration and joint optimization techniques can effectively improve the ground moving target detection performance of distributed MIMO wide band radar systems.

Black carbon (BC) aerosols significantly impact regional air quality and global climate as important light-absorbing atmospheric particles. Using high-temporal resolution BC observation data from urban and suburban sites in Hangzhou and PM10 concentrations, this study analyzed the temporal and spatial distribution characteristics of BC concentrations, precipitation scavenging efficiency, and the efficacy of emission mitigation policies. The results showed that (1) suburban BC concentrations presented a significant interannual decline. Seasonal variation displayed a single peak, with high concentrations in winter and low concentrations in summer. A characteristic bimodal diurnal variation pattern was observed, with peaks during morning and evening rush hours. In terms of spatial distribution, the annual average concentration in urban areas was 20.7% higher than in suburban areas, with the largest difference in winter. (2) The scavenging efficiency of precipitation showed nonlinear characteristics. The average efficiency of light rain was the highest, whereas heavy rainfall showed more complex characteristics. The scavenging efficiency of continuous 12 h precipitation was significantly higher than that of short-term heavy rainfall. (3) Emission mitigation policy implementation had a marked effect, with diesel vehicle restrictions and biomass combustion control reducing BC concentrations by 11% and 19%, respectively.

This study assesses the uncertainty in ozone sounding data obtained from the CYT-1 ozonesonde at the Xilinhot Station of the World Meteorological Organization’s (WMO) Global Climate Observing System’s Reference Upper-Air Network (GRUAN). The primary sources of uncertainty in the ozone sounding data from the Xilinhot GRUAN Station are identified, and a quantitative analysis method is applied to evaluate the contribution of each source to the overall uncertainty. A synthetic uncertainty formula is utilized to develop a model for calculating the uncertainty of the ozone sounding data from this station. The calculated uncertainty results are then analyzed to assess the level of data uncertainty.