Accurate, timely and wide-ranging radar Quantitative Precipitation Estimation (QPE) is crucial for effective water resource management and climate research. However, the insufficient consideration of surface meteorological conditions such as moisture information leads to significant biases for existing QPE methods. This paper proposes a novel quantitative precipitation estimation network based on hierarchical multi-branch convolutional blocks (MCB-Net). The network incorporates relative humidity data and radar data into a multimodal-fusion architecture, effectively extracting information from diverse data sources and correcting precipitation loss due to evaporation. Additionally, a series of MCB blocks are designed to replace the traditional convolutional operation with multi-branch convolutional units, enabling MCB-Net to capture complicated spatial and temporal features related to precipitation. The indices including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Ratio (MBR) and Correlation Coefficient (CC) are adopted to evaluate the performance of the proposed method. Experimental results demonstrate that MCB-Net outperforms conventional method and other deep learning based QPE networks including Volume-to-Point CNN network, U-Net and ResNet. The visualized results illustrate that the proposed network can effectively mitigate precipitation overestimation and enhance the accuracy of precipitation estimation.

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.

Deep learning (DL)-based precipitation nowcasting algorithms have garnered significant attention in recent years. However, the presence of variable spatial scales in precipitation patterns poses challenges for methods that solely focus on capturing spatiotemporal correlations at a single scale. Moreover, current DL-based algorithms tend to model short-term (e.g., 10-min time span) rainfall locally neglecting long-term, global (e.g., 2-h time span) life-cycle evolution. Furthermore, widely used pixel-wise losses are prone to produce low effective-spatial-resolution predictions. To this end, we introduce a multiscale deformable transformer network to leverage echo contexts from image patches of varying spatial scales. Meanwhile, a multihead deformable self-attention mechanism is introduced for capturing precipitation spatiotemporal dynamics in a global manner. Moreover, to improve the spatial resolution of predictions, the Fourier space regularization and adversarial losses are proposed by narrowing the discrepancy of the Fourier spectra of predictions and references. Thanks to the introduced loss function, our model generates highly effective spatial-resolution predictions with abundant details. Extensive experiments on two real datasets show the substantial superiority of our method in terms of critical success index (CSI) compared to recent competitive approaches. At the same time, our predictions have more realistic precipitation details and significantly better fidelity. For example, on a vertically integrated liquid (VIL) product dataset, compared to baseline methods, our approach reduces the Fréchet inception distance (FID) value by a factor of$2\sim 4$while improves the CSI score by 3%~5% approximately.

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.

The correct distinction between precipitation echo (PE) and non-precipitation echo (NPE) of weather radar is one of the key steps in the quantitative precipitation estimation (QPE). Based on the high spatiotemporal resolution data of the Himawari-8 satellite and the precipitation data of the rain gauge, this paper proposes a fuzzy logic algorithm (FLA) to remove NPE in weather radar data through statistically analyzing the probability density distribution of the satellite temperature of black body (TBB) according to different precipitation scenarios. Two methods, i.e. the FLA and the threshold method (TM), are compared and analyzed by using the data in August 2019. The critical success index (CSI) score of the FLA is 0.056 higher than that of the TM (0.796 vs. 0.74), and the probability of detection (POD) score is 0.058 higher than that of the TM (0.802 vs. 0.744). The results show that the proposed method can effectively improve the quality of weather radar data.

Radar quantitative precipitation estimation (QPE) is one of important applications of Doppler weather radar. The use of the rain gauge data to correct and improve the accuracy of radar QPE is important for weather forecasting. Based on observation data of rain gauges, this study proposes two machine learning methods to correct radar QPE product. The Support Vector Regression (SVR) and Random Forest (RF) models are used as the regression model. The rain gauge data is used as the ground truth. The radar QPE data is divided into three intervals: 0–10 mm/h, 10–30 mm/h, and> 30 mm/h. One traditional method, i.e., the PDF method, is selected for comparison. The experimental results show that the machine learning methods achieve better performance than the PDF method. The scatter plot shows that SVR effectively mitigate the overestimation problem which is a main problem of the original radar QPE product.

Global localization in a prior map is an important field in virtual and augmented reality systems, but it is always a challenge to conduct point cloud based localization in the large-scale scene prior map. A large-scale prior map usually means huge amount of calculation for point cloud processing, which leads to the long time required for global localization. To deal with this problem, we propose a fast point cloud global localization method based on point clouds segmentation and learning-based descriptor. On the one hand, cylindrical filtering, ground-point removal and point cloud segmentation are adopted to eliminate a large number of useless points and retain points with rich structures, which improves the efficiency of point cloud registration. On the other hand, reliable 3D point cloud descriptor, two-phase search strategy for place recognition and geometric consistency verification are used to ensure the localization accuracy. Experiments prove that the proposed method achieves good localization effect on both KITTI and MVSEC datasets. Under the condition of ensuring the high localization accuracy, the time for point clouds to complete the global localization is greatly reduced.

This paper presents a case study on measuring water vapor profile using a combination of ground-based microwave radiometer and cloud radar. The correlation of water vapor density measured by radiosonde and twelve brightness temperatures observed by microwave radiometer were analyzed to select appropriate channels for retrieving water vapor density. Then, a retrieval algorithms of water vapor density for non-precipitating atmosphere was developed using radar reflectivity measured by cloud radar and selected brightness temperatures. In addition, the potential of the combination for improving the accuracy of water vapor density for rainy atmosphere was also analyzed. The study results indicate that the combination of ground-based microwave radiometer and cloud radar is potential for improving the accuracy of vertical profiles of water vapor density in rainy condition.

With respect to multiple attribute decision making (MADM) problems, in which attribute values take the form of intuitionistic uncertain linguistic information, a new decision-making method based on the intuitionistic uncertain linguistic weighted Bonferroni OWA operator is developed. First, the score function, accuracy function, and comparative method of the intuitionistic uncertain linguistic numbers are introduced. Then, an intuitionistic uncertain linguistic Bonferroni OWA (IULBOWA) operator and an intuitionistic uncertain linguistic weighted Bonferroni OWA (IULWBOWA) operator are developed. Furthermore, some properties of the IULBOWA and IULWBOWA operators, such as commutativity, idempotency, monotonicity, and boundedness, are discussed. At the same time, some special cases of these operators are analyzed. Based on the IULWBOWA operator, the multiple attribute decision-making method with intuitionistic uncertain linguistic information is proposed. Finally, an illustrative example is given to illustrate the decision-making steps and to demonstrate its practicality and effectiveness.

This paper describes a new method of detecting 3D convex surfaces from range data using two simplified Gaussian and mean curvatures. Many methods for feature detection from range images are based on the signs of the Gaussian and mean curvatures. Usually, range image regions are classified into one of eight basic surface types. In this paper, a direct method of detecting 3D convex surfaces is proposed using the signs of simplified Gaussian and mean curvatures based on classical differential geometry analysis, under the assumption that a range image surface can be modeled by a Monge patch surface. It is shown that the simplified Gaussian and mean curvatures and usual ones are compared on their different mathematical behaviors from the theory of differential geometry. Experimental results on real range data are presented.

This paper reports a comprehensive study on an image processing and feature extraction tool particularly for 3-D range image applications in manufacturing processes. The library of the algorithms developed in this study is formatted into an easy-to-use imaging tool that consists of many useful algorithms and functions. The objective of this study is to produce a library of 3-D range imaging algorithms that can effectively employ imaging and vision techniques and be applied to industrial automation environments. A group of new algorithms have been developed and tested in manufacturing applications.