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3. Correlation Between Hail Diameter and VILD in Bayannur

Author

Xu, Yunong, Su, Debin, Yang, Ning, Sun, Xiaoguang, Wang, Haijiang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Wang, Wei, editor, Liu, Xin, editor, Na, Zhenyu, editor, and Zhang, Baoju, editor

Published
2022
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7. Innovative cloud quantification: deep learning classification and finite-sector clustering for ground-based all-sky imaging.

Author

Luo, Jingxuan, Pan, Yubing, Su, Debin, Zhong, Jinhua, Wu, Lingxiao, Zhao, Wei, Hu, Xiaoru, Qi, Zhengchao, Lu, Daren, and Wang, Yinan

Subjects
ARTIFICIAL neural networks, DEEP learning, COMPUTER vision, CLIMATE research, METEOROLOGICAL stations
Abstract

Accurate cloud quantification is essential in climate change research. In this work, we construct an automated computer vision framework by synergistically incorporating deep neural networks and finite-sector clustering to achieve robust whole-sky image-based cloud classification, adaptive segmentation and recognition under intricate illumination dynamics. A bespoke YOLOv8 (You Only Look Once 8) architecture attains over 95 % categorical precision across four archetypal cloud varieties curated from extensive annual observations (2020) at a Tibetan highland station. Tailor-made segmentation strategies adapted to distinct cloud configurations, allied with illumination-invariant image enhancement algorithms, effectively eliminate solar interference and substantially boost quantitative performance even in illumination-adverse analysis scenarios. Compared with the traditional threshold analysis method, the cloud quantification accuracy calculated within the framework of this paper is significantly improved. Collectively, the methodological innovations provide an advanced solution to markedly escalate cloud quantification precision levels imperative for climate change research while offering a paradigm for cloud analytics transferable to various meteorological stations. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Rain-Drop Size Distribution Case Study in Chengdu Based on 2DVD Observations

Author

Liu, Yan, Su, Debin, Lei, Hongyu, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Wang, Wei, editor, Liu, Xin, editor, Na, Zhenyu, editor, Jia, Min, editor, and Zhang, Baoju, editor

Published
2020
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10. Comparative Analysis of Reflectivity from an Updated SC Dual Polarization Radar and a SA System in CINRAD Network

Author

Liu, Yue, Su, Debin, Tan, Xue, Wang, Haijiang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Wang, Wei, editor, Liu, Xin, editor, Na, Zhenyu, editor, Jia, Min, editor, and Zhang, Baoju, editor

Published
2020
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11. Doppler Weather Radar Network Joint Observation and Reflectivity Data Mosaic

Author

Jiela, Qutie, Wang, Haijiang, He, Jiaoyang, Su, Debin, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Wang, Wei, editor, Liu, Xin, editor, Na, Zhenyu, editor, Jia, Min, editor, and Zhang, Baoju, editor

Published
2020
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12. Analysis of Characteristics of Atmospheric Structure Constant of Refractive Index Based on Wind Profiler Radar in Precipitation

Author

Li, Yating, Su, Debin, Fan, Xingang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Liu, Xin, editor, Na, Zhenyu, editor, Wang, Wei, editor, Mu, Jiasong, editor, and Zhang, Baoju, editor

Published
2020
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13. Ship Target Detection in High-Resolution SAR Images Based on Information Theory and Harris Corner Detection

Author

Wang, Haijiang, Ran, Yuanbo, Liu, Shuo, Deng, Yangyang, Su, Debin, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Liu, Xin, editor, Na, Zhenyu, editor, Wang, Wei, editor, Mu, Jiasong, editor, and Zhang, Baoju, editor

Published
2020
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16. Understanding the dynamical-microphysical-electrical processes associated with severe thunderstorms over the Beijing metropolitan region

Author

Qie, Xiushu, Yuan, Shanfeng, Chen, Zhixiong, Wang, Dongfang, Liu, Dongxia, Sun, Mengyu, Sun, Zhuling, Srivastava, Abhay, Zhang, Hongbo, Lu, Jingyu, Xiao, Hui, Bi, Yongheng, Feng, Liang, Tian, Ye, Xu, Yan, Jiang, Rubin, Liu, Mingyuan, Xiao, Xian, Duan, Shu, Su, Debin, Sun, Chengyun, Xu, Wenjing, Zhang, Yijun, Lu, Gaopeng, Zhang, Da-Lin, Yin, Yan, and Yu, Ye

Published
2021
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17. A Primitive Research on Precipitation Observation Parameters with 35 GHZ Cloud Radar

Author

Lai, Chen, Su, Debin, Qi, Yang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Mu, Jiasong, editor, Jia, Min, editor, Wang, Wei, editor, Feng, Xuhong, editor, and Zhang, Baoju, editor

Published
2019
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25. Multiscale Representation of Radar Echo Data Retrieved through Deep Learning from Numerical Model Simulations and Satellite Images.

Author

Zhu, Mingming, Liao, Qi, Wu, Lin, Zhang, Si, Wang, Zifa, Pan, Xiaole, Wu, Qizhong, Wang, Yangang, and Su, Debin

Subjects
DEEP learning, REMOTE-sensing images, NUMERICAL weather forecasting, STANDARD deviations, RADAR, COMPUTER simulation, ECHO
Abstract

Radar reflectivity data snapshot fine-grained atmospheric variations that cannot be represented well by numerical weather prediction models or satellites, which poses a limit for nowcasts based on model–data fusion techniques. Here, we reveal a multiscale representation (MSR) of the atmosphere by reconstructing the radar echoes from the Weather Research and Forecasting (WRF) model simulations and the Himawari-8 satellite products using U-Net deep networks. Our reconstructions generated the echoes well in terms of patterns, locations, and intensities with a root mean square error (RMSE) of 5.38 dBZ. We find stratified features in this MSR, with small-scale patterns such as echo intensities sensitive to the WRF-simulated dynamic and thermodynamic variables and with larger-scale information about shapes and locations mainly captured from satellite images. Such MSRs with physical interpretations may inspire innovative model–data fusion methods that could overcome the conventional limits of nowcasting. [ABSTRACT FROM AUTHOR]

Published
2023
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26. Estimating Precipitation Using LSTM-Based Raindrop Spectrum in Guizhou.

Author

Wang, Fuzeng, Cao, Yaxi, Wang, Qiusong, Zhang, Tong, and Su, Debin

Subjects
RAINDROP size, RADAR meteorology, STRATUS clouds, RAIN gauges, STATISTICAL correlation, PRECIPITATION gauges
Abstract

The change in raindrop spectrum characteristics is an important factor affecting the accuracy of estimations of precipitation. The in-depth study of raindrop spectrum characteristics is of great interest for understanding precipitation process and improving quantitative radar precipitation estimation. In this paper, the raindrop size distributions at Longli (57913), Puding (57808) and Luodian (57916) stations in Guizhou were analyzed from the perspective of precipitation microphysical characteristics. The results showed that the raindrop size distribution was different among different regions. The correlation coefficients of the mass-weighted average diameter for the rain intensities at these three stations were 46.89%, 49.51%, and 47.03%, respectively, which were slightly lower than the normal correlation coefficients of the average volume diameter for the rain intensities: 67.80%, 71.28%, and 71.46%, respectively. Based on the data from the Guiyang weather radar, raindrop spectrometer, and automatic rain gauge, the dynamic Z-I relationship method and the LSTM neural network method were used to estimate precipitation. The correlation coefficients of the dynamic Z-I relationship method and the LSTM neural network method at the three stations studied were 0.8432, 0.7763, and 0.8658 and 0.8745, 0.9125, and 0.8676, respectively. Regarding the process of stratiform cloud precipitation, the correlation coefficients of the dynamic Z-I relationship method and LSTM neural network method at the three stations were 0.6933, 0.0902, and 0.1409 and 0.7114, 0.4984, and 0.4902, respectively. In the estimation of cumulative precipitation for 45 days from 1 July to 16 August 2020, the relative errors of the neural network estimation at the three stations were −4.25%, −11.35%, and −8.68% and the relative errors of the dynamic Z-I relationship estimation were −2.68%, −7.41%, and −21.23%, respectively. The final relative error of the neural network was slightly worse than that of the dynamic Z-I relationship in the cumulative precipitation estimations of Longli station and Puding station, but the overall correlation coefficients of the LSTM neural network method were better than those of the dynamic Z-I relationship method. [ABSTRACT FROM AUTHOR]

Published
2023
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32. Data Analysis for a Hailstorm Event Based on FY-4 Satellite Observation

Author

Fang Xiaohong, Su Debin, Geng Lingli, Wang Zhihui, Yang Ning, and Cao Yang

Subjects
Convection, Meteorology, law, Event based, Doppler radar, Environmental science, Cirrus, Satellite, Weather radar, Terrain, Radar, law.invention
Abstract

Observations from the FY-4 satellite products (cloud type (CLT), cloudtop height (CTH), cloudtop pressure (CTP), cloudtop temperature (CTT)) and C-band Doppler weather radar are compared and analyzed for the process of a hailstorm in Bayan Nur from 15:00 to 18:00 on August 9,2019 (BJT). Analyze and research the characteristics of occurrence, development and change of hailcloud during this hail werther. The results are as follows: (1) The terrain of the Yinshan Mountains has a special strengthening effect on the formation of convective clouds. (2) When the convective cloud matures, the height of the CTH reaches 14km, the CTP is 110hPa, the CTT is 208 K, and the CLT are mainly mixed clouds, cirrus clouds (translucent ice clouds) and supercooled water clouds, and the radar echo intensity reaches 50dBZ. (3) Satellite products have better consistency with radar observations.

Published
2019

33. Comparison and Verification of Rainfall Validation with Parsivel2 and X-band Dual-Polarization Radar

Author

Lu Fangyue, Kang Lu, Cao Yang, Su Debin, and Yang Yating

Subjects
Atmospheric physics, Quantitative precipitation estimation, Disdrometer, law, Attenuation, Doppler radar, X band, Environmental science, Weather radar, Radar, law.invention, Remote sensing
Abstract

Observations acquired from Parsivel2 disdrometer located at the haidian station and X-band dual-polarization radar of IAP (Institute of Atmospheric Physics) were compared and analyzed for the process of severe weather in Beijing on July 20, 2016. The radar reflectivity factor from disdrometer and radar are matched and compared using multiple averaging schemes with different grid sizes in neighboring regions considering the consistency of time and space as well. The accuracy of QPE (quantitative precipitation estimation) will be affected by the attenuation of X-band radar, and can be evaluated precisely from disdrometer observation. In this study, disdrometer was used to obtain the precipitation intensity and reflectivity factor. Based on raindrop size distribution observation and the least square method, a Z/R relation was simulated and applied to QPE calculation, QPE from Z/R relationship of convention and correction, polarization parameters derived, disdrometer observed and from automatic rain gauges were compared and validated. The results show that the corrected QPE were much closer to the Parsivel2 observation, and the QPE from polarization parameter was the optimal one.

Published
2019

34. Data Comparative Analysis of 2DVD and Parsivel2 Raindrop Spectrometer

Author

Wang Liang, SU Debin, and Liu Yan

Subjects
Atmospheric measurements, Lidar, Disdrometer, Particle number, Spectrometer, Environmental science, Peak value, Atmospheric sciences, Light rain, Intensity (heat transfer)
Abstract

Observations in September 2018 from 2-D video disdrometer (2DVD) and 1-D disdrometer (Parsivel2) are used to study the performance difference of the two instruments at the same location in the campus of CUIT (Chengdu University of Information Technology). The characteristics of rainfall intensity, raindrop size distribution (DSD) and particle number density from the two instruments respectively are analyzed and compared according to rainfall intensity classification. The results show that 1) In the process of light rain, moderate rain, heavy rain and rainstorm, the DSD of the two instruments shows a single peak characteristic, and the peak value appears at the raindrop diameter of about 0.6mm. 2) For the number of raindrops with diameter less than 2mm, 2DVD detection value is greater than Parsivel2 detection value, and the D-value increases with the increase of rainfall intensity. 3) With the increase of rainfall intensity, the proportion of raindrops with diameter less than 2mm to the total raindrops decreases. 4) Parsivel2 can hardly detect raindrops with a diameter less than 0.3mm, while 2DVD has a better detection effect on raindrops with a diameter of about 0.19mm, and the detection ability for raindrops with a diameter less than 1mm is far lower than 2DVD. 5) The rainfall in Chengdu is mainly composed of small raindrops with a diameter of about 1mm. 6) The peak value of rainfall intensity of 2DVD is prior to that of Parsivel2, and the two peaks are basically the same in light rain, but with the increase of rainfall, the peak value of 2DVD will be greater than that of Parsivel2.

Published
2019

35. Echo Feature Identification for Short-time Heavy Precipitation with S-band Observations

Author

Liu Yue, Xu Yunong, and Su Debin

Subjects
Quantitative precipitation estimation, Automatic weather station, Meteorology, law, Doppler radar, Environmental science, Weather radar, Precipitation, S band, Radar, Spatial distribution, law.invention
Abstract

Short-time heavy precipitation refers to a weather phenomenon in which the precipitation intensity is somewhat large in a short time, or the rainfall reaches or exceeds a certain value in the specified short time of period, which is an important indicator to evaluate the occurrence of severe convective weather. To address a process of precipitation in Beijing area, this paper studies the characteristics of weather radar echo under the condition of short-time heavy precipitation by using the ground rainfall observation from automatic weather station (AWS) network and the observations from the CINRAD-SA radar in Beijing area. A comparative analysis of a short-time heavy rainfall in Beijing area is conducted based on surface gauge and radar observations to investigate the temporal, spatial distributions of precipitation and the characteristics of radar echo above the surface stations, and the cross validation of AWS and SA radar data are carried out as well. For rainfall accumulation in 6min, the characteristics of ground precipitation were analyzed. Then, the consistency of radar products and ground precipitation characteristics were studied by analyzing the echo structure and its motion feature and matching quantitative precipitation estimation (QPE) products with surface rainfall observation. The result shows that the temporal and spatial distribution of short-time heavy precipitation have greater uncertainty, however, weather radar echo can reflect the characteristics of short-term heavy precipitation on the ground to a certain extent, and under the condition of short-term heavy precipitation, S-band radar QPE has a good consistency with the precipitation observed on the ground.

Published
2019

36. Understanding the dynamical-microphysical-electrical processes associated with severe thunderstorms over the Beijing metropolitan region

Author

Qie, Xiushu, primary, Yuan, Shanfeng, additional, Chen, Zhixiong, additional, Wang, Dongfang, additional, Liu, Dongxia, additional, Sun, Mengyu, additional, Sun, Zhuling, additional, Srivastava, Abhay, additional, Zhang, Hongbo, additional, Lu, Jingyu, additional, Xiao, Hui, additional, Bi, Yongheng, additional, Feng, Liang, additional, Tian, Ye, additional, Xu, Yan, additional, Jiang, Rubin, additional, Liu, Mingyuan, additional, Xiao, Xian, additional, Duan, Shu, additional, Su, Debin, additional, Sun, Chengyun, additional, Xu, Wenjing, additional, Zhang, Yijun, additional, Lu, Gaopeng, additional, Zhang, Da-Lin, additional, Yin, Yan, additional, and Yu, Ye, additional

Published
2020
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