Articles | Volume 26, issue 5
https://doi.org/10.5194/acp-26-3339-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-3339-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Mar 2026
Research article |
| 04 Mar 2026
| 04 Mar 2026
On the nationwide variability of low-level jets prior to warm-season nocturnal rainfall in China revealed by radar wind profilers
Ning Li
State Key Laboratory of Severe Weather Meteorological Science and Technology & Specialized Meteorological Support Technology Research Center, Chinese Academy of Meteorological Sciences, Beijing 100081, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
National Meteorological Centre, Beijing 100081, China
State Key Laboratory of Severe Weather Meteorological Science and Technology & Specialized Meteorological Support Technology Research Center, Chinese Academy of Meteorological Sciences, Beijing 100081, China
CMA Field Scientific Experiment Base for Low-Altitude Economy Meteorological Support of Unmanned Aviation in Guangdong-Hong Kong-Macao Greater Bay Area, Shenzhen 518108, China
Xiaoran Guo
State Key Laboratory of Severe Weather Meteorological Science and Technology & Specialized Meteorological Support Technology Research Center, Chinese Academy of Meteorological Sciences, Beijing 100081, China
CMA Field Scientific Experiment Base for Low-Altitude Economy Meteorological Support of Unmanned Aviation in Guangdong-Hong Kong-Macao Greater Bay Area, Shenzhen 518108, China
Tianmeng Chen
State Key Laboratory of Severe Weather Meteorological Science and Technology & Specialized Meteorological Support Technology Research Center, Chinese Academy of Meteorological Sciences, Beijing 100081, China
CMA Field Scientific Experiment Base for Low-Altitude Economy Meteorological Support of Unmanned Aviation in Guangdong-Hong Kong-Macao Greater Bay Area, Shenzhen 518108, China
Zhen Zhang
State Key Laboratory of Severe Weather Meteorological Science and Technology & Specialized Meteorological Support Technology Research Center, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Na Tang
State Key Laboratory of Severe Weather Meteorological Science and Technology & Specialized Meteorological Support Technology Research Center, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Yifei Wang
State Key Laboratory of Severe Weather Meteorological Science and Technology & Specialized Meteorological Support Technology Research Center, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Honglong Yang
CMA Field Scientific Experiment Base for Low-Altitude Economy Meteorological Support of Unmanned Aviation in Guangdong-Hong Kong-Macao Greater Bay Area, Shenzhen 518108, China
Yongguang Zheng
National Meteorological Centre, Beijing 100081, China
Yongshui Zhou
Guizhou Meteorological Observatory, Guiyang 550001, China
Related authors
Hui Xu, Jianping Guo, Jianbo Deng, Rongfang Yang, Deli Meng, Zhen Zhang, Ning Li, Yuping Sun, Shuairu Jiang, Tianmeng Chen, Juan Chen, Liping Zeng, Yongshui Zhou, and Bing Tong
Atmos. Chem. Phys., 26, 14303–14327, https://doi.org/10.5194/acp-26-14303-2026, https://doi.org/10.5194/acp-26-14303-2026, 2026
Short summary
Short summary
Using a new national network of 80 cloud radars, we mapped how clouds are layered and how high they reach across China. Clouds vary strongly by region, season, and time of day, with deeper clouds in humid conditions and shallower clouds under stable or strong shear conditions. Afternoon single‑layer low CBH is significantly higher than all‑hour, especially over evergreen broadleaf. These findings help improve weather and climate predictions.
Weilong Deng, Jianping Guo, Ning Li, Zhen Zhang, Yifei Wang, and Yi Yang
Atmos. Chem. Phys., 26, 14259–14275, https://doi.org/10.5194/acp-26-14259-2026, https://doi.org/10.5194/acp-26-14259-2026, 2026
Short summary
Short summary
Heavy rain far ahead of weather fronts is easily overlooked and hard to forecast. Using five warm seasons of rain gauge and wind radar data from eastern China's plains, we found that these brief, intense events supplied nearly half of heavy rainfall at some sites. Before rain began, fast winds at low heights became more common and the strongest upward air movements intensified. These changes could help identify warning signs, but their ability to predict individual downpours still needs testing.
Tian Xian, Yuping Sun, Tianmeng Chen, Hui Xu, Xiaoran Guo, Zhen Zhang, Zhongwang Wu, Huaquan Hu, Juan Chen, Liping Zeng, Yifei Wang, Shuairu Jiang, Pengzhan Yao, Ning Li, Honglong Yang, and Jianping Guo
EGUsphere, https://doi.org/10.5194/egusphere-2026-5822, https://doi.org/10.5194/egusphere-2026-5822, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Turbulence in the planetary boundary layer (PBL) affects cloud and precipitation, yet little is known about it across China. With summertime 2022 data from 132 radar wind profilers, we map the turbulence dissipation rate (ε) within and above the PBL. Stability and wind shear jointly govern turbulence within the PBL, whereas shear dominates above it; clouds enhance turbulence aloft, and topography and land cover shape its regional pattern, offering new constraints for weather and climate models.
Xiaoran Guo, Jianping Guo, Ning Li, Zhen Zhang, Tianmeng Chen, Yu Shi, Pengzhan Yao, Shuairu Jiang, Lei Zhao, and Fei Hu
Atmos. Chem. Phys., 26, 2391–2409, https://doi.org/10.5194/acp-26-2391-2026, https://doi.org/10.5194/acp-26-2391-2026, 2026
Short summary
Short summary
Wind gusts threaten safety and infrastructure but are hard to predict. To address this gap, we studied an extreme wind gust event in Beijing on 30 May 2024. We used seven radar wind profilers to track how this gust developed. It formed when cold northeasterly air clashed with warm southerly winds as the storm moved downhill. Evaporation of rain cooled the air, boosting downward air movement and wind strength. The turbulence transferring energy from small to large eddies intensify winds.
Deli Meng, Jianping Guo, Juan Chen, Xiaoran Guo, Ning Li, Yuping Sun, Zhen Zhang, Na Tang, Hui Xu, Tianmeng Chen, Rongfang Yang, and Jiajia Hua
Earth Syst. Sci. Data, 17, 4023–4037, https://doi.org/10.5194/essd-17-4023-2025, https://doi.org/10.5194/essd-17-4023-2025, 2025
Short summary
Short summary
This study provides a high-resolution dataset of low-level atmospheric turbulence across China, using radar and weather balloon observations. It reveals regional and seasonal variations in turbulence, with stronger activity in spring and summer. The dataset supports weather forecasting, aviation safety, and low-altitude flight planning, aiding China's growing low-altitude economy, and is accessible at https://doi.org/10.5281/zenodo.14959025.
Xiaoran Guo, Jianping Guo, Deli Meng, Yuping Sun, Zhen Zhang, Hui Xu, Liping Zeng, Juan Chen, Ning Li, and Tianmeng Chen
Earth Syst. Sci. Data, 17, 3541–3552, https://doi.org/10.5194/essd-17-3541-2025, https://doi.org/10.5194/essd-17-3541-2025, 2025
Short summary
Short summary
Optimal atmospheric dynamic conditions are essential for convective storms. This study generates a dataset of high-resolution divergence and vorticity profiles using the measurements of a radar wind profiler mesonet in Beijing. The negative divergence and positive vorticity are present ahead of rainfall events. This suggests that this dataset can help improve our understanding of the pre-storm environment and has the potential to be applied in weather forecasting.
Deli Meng, Jianping Guo, Xiaoran Guo, Yinjun Wang, Ning Li, Yuping Sun, Zhen Zhang, Na Tang, Haoran Li, Fan Zhang, Bing Tong, Hui Xu, and Tianmeng Chen
Atmos. Chem. Phys., 24, 8703–8720, https://doi.org/10.5194/acp-24-8703-2024, https://doi.org/10.5194/acp-24-8703-2024, 2024
Short summary
Short summary
The turbulence in the planetary boundary layer (PBL) over the Tibetan Plateau (TP) remains unclear. Here we elucidate the vertical profile of and temporal variation in the turbulence dissipation rate in the PBL over the TP based on a radar wind profiler (RWP) network. To the best of our knowledge, this is the first time that the turbulence profile over the whole TP has been revealed. Furthermore, the possible mechanisms of clouds acting on the PBL turbulence structure are investigated.
Xiaoran Guo, Jianping Guo, Tianmeng Chen, Ning Li, Fan Zhang, and Yuping Sun
Atmos. Chem. Phys., 24, 8067–8083, https://doi.org/10.5194/acp-24-8067-2024, https://doi.org/10.5194/acp-24-8067-2024, 2024
Short summary
Short summary
The prediction of downhill thunderstorms (DSs) remains elusive. We propose an objective method to identify DSs, based on which enhanced and dissipated DSs are discriminated. A radar wind profiler (RWP) mesonet is used to derive divergence and vertical velocity. The mid-troposphere divergence and prevailing westerlies enhance the intensity of DSs, whereas low-level divergence is observed when the DS dissipates. The findings highlight the key role that an RWP mesonet plays in the evolution of DSs.
Wei You, Zengliang Zang, Daichun Wang, Wei Zhong, Shuang Mei, Yiwen Hu, Jianping Guo, and Dan Niu
EGUsphere, https://doi.org/10.5194/egusphere-2026-5573, https://doi.org/10.5194/egusphere-2026-5573, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Models struggle to show how tiny airborne particles are distributed with height. We combined satellite laser measurements with a weather and air-quality model. For two cases over China in December 2019, this added particles where the model had too few and better matched observed vertical patterns. It also improved estimates of total particle amount and surface pollution, though the surface benefit faded after fourteen hours. The approach is promising, but more cases are needed before wider use.
Tian Xian, Yuping Sun, Tianmeng Chen, Hui Xu, Xiaoran Guo, Zhen Zhang, Zhongwang Wu, Huaquan Hu, Juan Chen, Liping Zeng, Yifei Wang, Shuairu Jiang, Pengzhan Yao, Ning Li, Honglong Yang, and Jianping Guo
EGUsphere, https://doi.org/10.5194/egusphere-2026-5822, https://doi.org/10.5194/egusphere-2026-5822, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Turbulence in the planetary boundary layer (PBL) affects cloud and precipitation, yet little is known about it across China. With summertime 2022 data from 132 radar wind profilers, we map the turbulence dissipation rate (ε) within and above the PBL. Stability and wind shear jointly govern turbulence within the PBL, whereas shear dominates above it; clouds enhance turbulence aloft, and topography and land cover shape its regional pattern, offering new constraints for weather and climate models.
Weilong Deng, Jianping Guo, Ning Li, Zhen Zhang, Yifei Wang, and Yi Yang
Atmos. Chem. Phys., 26, 14259–14275, https://doi.org/10.5194/acp-26-14259-2026, https://doi.org/10.5194/acp-26-14259-2026, 2026
Short summary
Short summary
Heavy rain far ahead of weather fronts is easily overlooked and hard to forecast. Using five warm seasons of rain gauge and wind radar data from eastern China's plains, we found that these brief, intense events supplied nearly half of heavy rainfall at some sites. Before rain began, fast winds at low heights became more common and the strongest upward air movements intensified. These changes could help identify warning signs, but their ability to predict individual downpours still needs testing.
Hui Xu, Jianping Guo, Jianbo Deng, Rongfang Yang, Deli Meng, Zhen Zhang, Ning Li, Yuping Sun, Shuairu Jiang, Tianmeng Chen, Juan Chen, Liping Zeng, Yongshui Zhou, and Bing Tong
Atmos. Chem. Phys., 26, 14303–14327, https://doi.org/10.5194/acp-26-14303-2026, https://doi.org/10.5194/acp-26-14303-2026, 2026
Short summary
Short summary
Using a new national network of 80 cloud radars, we mapped how clouds are layered and how high they reach across China. Clouds vary strongly by region, season, and time of day, with deeper clouds in humid conditions and shallower clouds under stable or strong shear conditions. Afternoon single‑layer low CBH is significantly higher than all‑hour, especially over evergreen broadleaf. These findings help improve weather and climate predictions.
Qi Zhang, Tianmeng Chen, Jianping Guo, Bin Deng, Han Li, and Yu Wu
Geosci. Model Dev., 19, 9555–9580, https://doi.org/10.5194/gmd-19-9555-2026, https://doi.org/10.5194/gmd-19-9555-2026, 2026
Short summary
Short summary
This study presents an adaptive observation-weighting scheme for thermodynamic profile retrievals from ground-based microwave radiometers and Mie–Raman lidars. The method dynamically estimates observational contributions during the retrieval process, replacing the commonly used static weighting assumption. Evaluation using 107 heavy-precipitation cases shows improved retrieval accuracy, particularly for atmospheric moisture profiles.
Juan Zhao, Jianping Guo, Jidong Gao, and Honglong Yang
EGUsphere, https://doi.org/10.5194/egusphere-2026-3350, https://doi.org/10.5194/egusphere-2026-3350, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Expanding low-altitude activities demand better lower-atmosphere weather data. Using observing system simulation experiments (OSSEs) over the Beijing-Tianjin-Hebei region, we tested if meteorological drone observations of temperature, humidity, and wind improve short-term rainfall forecasts. Dense, multi-height networks with wind measurements yield best accuracy, especially over mountains where storms form. These findings guide low-altitude observing network design to support safe operations.
Rongfang Yang, Deli Meng, Guanglei Zhang, Lihui Liu, Guocui Li, Zhen Zhang, and Jianping Guo
EGUsphere, https://doi.org/10.5194/egusphere-2026-4218, https://doi.org/10.5194/egusphere-2026-4218, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
The air near the ground affects weather, pollution, and low-flying aircraft safety, but is hard to measure over hills. We flew a drone with weather sensors and compared it with a tall measurement tower. The drone accurately captured how wind, temperature, and humidity change with height. It shook most when strong shifting winds met weak stability, especially near cloud base. This helps make drone weather observations and low-altitude flight safer.
Qi Zhang, Tianmeng Chen, Jianping Guo, and Xun Li
Atmos. Meas. Tech., 19, 4853–4873, https://doi.org/10.5194/amt-19-4853-2026, https://doi.org/10.5194/amt-19-4853-2026, 2026
Short summary
Short summary
Accurate monitoring of boundary-layer water vapor prior to nocturnal heavy precipitation remains challenging. This study extends a physically constrained retrieval framework by integrating Raman lidar observations to improve water vapor profile estimation. The method shows improved accuracy compared to reanalysis data and captures coherent pre-precipitation moisture evolution, demonstrating its potential for studying and monitoring severe weather processes.
Xiaozhong Cao, Qiyun Guo, Haowen Luo, Jincheng Wang, Rongkang Yang, Die Xiao, Yinfeng Liu, Zhongliang Sun, Shijun Liu, Sijie Chen, Anfan Huang, Guo Jianping, and Peng Zhang
Atmos. Meas. Tech., 19, 3231–3251, https://doi.org/10.5194/amt-19-3231-2026, https://doi.org/10.5194/amt-19-3231-2026, 2026
Short summary
Short summary
The Ascent-Drift-Descent Radiosonde System (ADDRS) represents an emerging cost-effective technology for upper-air measurements, Its 'Internet cloud + instrument terminal' design enables targeted observations for extreme weather research. Initial data application shows high potential for numerical weather prediction, The pilot application of ADDRS is list in the World Meteorological Organization Region II Coordination 2025-2027 demonstration project (No.RA II-18-I-DP-2).
Rumo Wang, Tianyi Fan, Zhanqing Li, and Jianping Guo
EGUsphere, https://doi.org/10.5194/egusphere-2026-1730, https://doi.org/10.5194/egusphere-2026-1730, 2026
Short summary
Short summary
This study investigated whether aerosols from anthropogenic emissions can make storm environment more favorable for producing tornados. We numerically modeled a real storm in the populated eastern China and found that aerosols can warm the low-level air and strengthen the updraft, allowing the storm to more effectively ingest environmental vorticity, and thus favoring the production of tornados. This suggests that air pollution may increase severe weather risk in densely populated regions.
Yonglin Fang, Hancheng Hu, Xiangdong Zheng, Jianping Guo, Xingbing Zhao, Fang Ma, and Hao Wu
Atmos. Chem. Phys., 26, 4089–4104, https://doi.org/10.5194/acp-26-4089-2026, https://doi.org/10.5194/acp-26-4089-2026, 2026
Short summary
Short summary
This study shows how temperature inversions (warm air above cold) trap pollution in China. Using six years of national data, we find these "lids" frequently cause severe haze, especially in winter, by increasing pollution probability and intensity. Impacts differ by region and inversion type/strength. These findings help improve air quality forecasts and regional pollution control strategies.
Zhe Tong, Boming Liu, Xin Ma, Jianping Guo, Haowei Zhang, Haoyu Dong, Ge Han, Yingying Ma, and Wei Gong
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-73, https://doi.org/10.5194/essd-2026-73, 2026
Revised manuscript accepted for ESSD
Short summary
Short summary
Near-surface wind is crucial for weather and wind energy studies. This work uses physically constrained machine learning combined with satellite wind observations to generate a wind speed profile dataset within the global atmospheric boundary layer. The dataset accurately depicts the spatial distribution and variation of global wind speeds with improved accuracy, finer vertical detail, and reduced data gaps, supporting boundary-layer meteorology, climate studies, and wind energy applications.
Dukun Chen, Weifeng Su, Shaojie Jiang, Honglong Yang, Chunsheng Zhang, Shutong Jiang, Dongyang Chang, Yuxin Liang, Hao Wang, Xin Yang, Tzung-May Fu, Zhenzhong Zeng, Lei Zhu, Huizhong Shen, Chen Wang, and Jianhuai Ye
Atmos. Chem. Phys., 26, 3607–3620, https://doi.org/10.5194/acp-26-3607-2026, https://doi.org/10.5194/acp-26-3607-2026, 2026
Short summary
Short summary
This research presents a sensor-free method to transform UAVs (unmanned aerial vehicles) into precise wind measurement platforms by calibrating their flight dynamics against wind. The approach quantifies how wind tilts the aircraft, enabling accurate estimation of wind speed and direction without additional sensors. This provides a low-cost, high-resolution monitoring of complex wind patterns, with applications in pollution tracking, air quality modeling, and operational safety in the emerging low-altitude economy.
Xiaoran Guo, Jianping Guo, Ning Li, Zhen Zhang, Tianmeng Chen, Yu Shi, Pengzhan Yao, Shuairu Jiang, Lei Zhao, and Fei Hu
Atmos. Chem. Phys., 26, 2391–2409, https://doi.org/10.5194/acp-26-2391-2026, https://doi.org/10.5194/acp-26-2391-2026, 2026
Short summary
Short summary
Wind gusts threaten safety and infrastructure but are hard to predict. To address this gap, we studied an extreme wind gust event in Beijing on 30 May 2024. We used seven radar wind profilers to track how this gust developed. It formed when cold northeasterly air clashed with warm southerly winds as the storm moved downhill. Evaporation of rain cooled the air, boosting downward air movement and wind strength. The turbulence transferring energy from small to large eddies intensify winds.
Qi Zhang, Tianmeng Chen, Jianping Guo, Yu Wu, Bin Deng, and Junjie Yan
Geosci. Model Dev., 19, 505–522, https://doi.org/10.5194/gmd-19-505-2026, https://doi.org/10.5194/gmd-19-505-2026, 2026
Short summary
Short summary
We propose TCKF1D-Var, a thermodynamic-constrained variational framework for ground-based microwave radiometer retrievals. Using virtual potential temperature, a ratio-based cost function, and a microphysics closure, it reduces biases relative to ERA5 and 1D-Var, improves cloud liquid water representation, and enhances heavy rainfall precursors, extending lead times. This approach strengthens continuous profiling and supports high-impact weather nowcasting.
Deli Meng, Jianping Guo, Juan Chen, Xiaoran Guo, Ning Li, Yuping Sun, Zhen Zhang, Na Tang, Hui Xu, Tianmeng Chen, Rongfang Yang, and Jiajia Hua
Earth Syst. Sci. Data, 17, 4023–4037, https://doi.org/10.5194/essd-17-4023-2025, https://doi.org/10.5194/essd-17-4023-2025, 2025
Short summary
Short summary
This study provides a high-resolution dataset of low-level atmospheric turbulence across China, using radar and weather balloon observations. It reveals regional and seasonal variations in turbulence, with stronger activity in spring and summer. The dataset supports weather forecasting, aviation safety, and low-altitude flight planning, aiding China's growing low-altitude economy, and is accessible at https://doi.org/10.5281/zenodo.14959025.
Jinhong Xian, Zongxu Qiu, Huayan Rao, Zhigang Cheng, Xiaoling Lin, Chao Lu, Honglong Yang, and Ning Zhang
Atmos. Chem. Phys., 25, 8427–8441, https://doi.org/10.5194/acp-25-8427-2025, https://doi.org/10.5194/acp-25-8427-2025, 2025
Short summary
Short summary
We studied how turbulence kinetic energy (TKE) changes in the lower atmosphere over Shenzhen, focusing on its role in weather and climate. Using advanced wind lidar technology, we tracked how TKE varies with height and across seasons. We found that heat near the ground drives turbulence, while wind effects become stronger higher up. Our results help improve weather and climate models by providing better data on how turbulence behaves in the atmosphere, aiding understanding of climate change.
Jinghao Zhai, Yin Zhang, Pengfei Liu, Yujie Zhang, Antai Zhang, Yaling Zeng, Baohua Cai, Jingyi Zhang, Chunbo Xing, Honglong Yang, Xiaofei Wang, Jianhuai Ye, Chen Wang, Tzung-May Fu, Lei Zhu, Huizhong Shen, Shu Tao, and Xin Yang
Atmos. Chem. Phys., 25, 7959–7972, https://doi.org/10.5194/acp-25-7959-2025, https://doi.org/10.5194/acp-25-7959-2025, 2025
Short summary
Short summary
Our study shows that the optical properties of brown carbon depend on its source. Brown carbon from ozone pollution had the weakest light absorption but the strongest wavelength dependence, while biomass burning brown carbon showed the strongest absorption and the weakest wavelength dependence. Nitrogen-containing organic carbon compounds were identified as key light absorbers. These results improve understanding of brown carbon sources and help refine climate models.
Xiaoran Guo, Jianping Guo, Deli Meng, Yuping Sun, Zhen Zhang, Hui Xu, Liping Zeng, Juan Chen, Ning Li, and Tianmeng Chen
Earth Syst. Sci. Data, 17, 3541–3552, https://doi.org/10.5194/essd-17-3541-2025, https://doi.org/10.5194/essd-17-3541-2025, 2025
Short summary
Short summary
Optimal atmospheric dynamic conditions are essential for convective storms. This study generates a dataset of high-resolution divergence and vorticity profiles using the measurements of a radar wind profiler mesonet in Beijing. The negative divergence and positive vorticity are present ahead of rainfall events. This suggests that this dataset can help improve our understanding of the pre-storm environment and has the potential to be applied in weather forecasting.
Juan Zhao, Jianping Guo, and Xiaohui Zheng
Geosci. Model Dev., 18, 4075–4101, https://doi.org/10.5194/gmd-18-4075-2025, https://doi.org/10.5194/gmd-18-4075-2025, 2025
Short summary
Short summary
A series of observing system simulation experiments are conducted to assess the impact of multiple radar wind profiler (RWP) networks on convective-scale numerical weather prediction. Results from three southwest-type heavy rainfall cases in the Beijing–Tianjin–Hebei region suggest the added forecast skill of ridge and foothill networks associated with the Taihang Mountains over the existing RWP network. This research provides valuable guidance for designing optimal RWP networks in the region.
Seoung Soo Lee, Chang Hoon Jung, Jinho Choi, Young Jun Yoon, Junshik Um, Youtong Zheng, Jianping Guo, Manguttathil G. Manoj, Sang-Keun Song, and Kyung-Ja Ha
Atmos. Chem. Phys., 25, 705–726, https://doi.org/10.5194/acp-25-705-2025, https://doi.org/10.5194/acp-25-705-2025, 2025
Short summary
Short summary
This study attempts to test a general factor that explains differences in the properties of different mixed-phase clouds using a modeling tool. Although this attempt is not to identify a factor that can perfectly explain and represent the properties of different mixed-phase clouds, we believe that this attempt acts as a valuable stepping stone towards a more complete, general way of using climate models to better predict climate change.
Jinhong Xian, Zongxu Qiu, Hongyan Luo, Yuanyuan Hu, Xiaoling Lin, Chao Lu, Yan Yang, Honglong Yang, and Ning Zhang
Atmos. Chem. Phys., 25, 441–457, https://doi.org/10.5194/acp-25-441-2025, https://doi.org/10.5194/acp-25-441-2025, 2025
Short summary
Short summary
The existing methods for observing turbulent kinetic energy (TKE) budget terms can only rely on ground-based towers. We have developed a new detection method that can directly observe and analyze the generation and dissipation mechanisms of turbulent energy at different heights in the vertical direction of the boundary layer. This research result will extend our study of TKE budget terms from near the ground to high altitude, providing a higher and more detailed perspective.
Zhiqi Xu, Jianping Guo, Guwei Zhang, Yuchen Ye, Haikun Zhao, and Haishan Chen
Earth Syst. Sci. Data, 16, 5753–5766, https://doi.org/10.5194/essd-16-5753-2024, https://doi.org/10.5194/essd-16-5753-2024, 2024
Short summary
Short summary
Tropical cyclones (TCs) are powerful weather systems that can cause extreme disasters. Here we generate a global long-term TC size and intensity reconstruction dataset, covering a time period from 1959 to 2022, with a 3 h temporal resolution, using machine learning models. These can be valuable for filling observational data gaps and advancing our understanding of TC climatology, thereby facilitating risk assessments and defenses against TC-related disasters.
Deli Meng, Jianping Guo, Xiaoran Guo, Yinjun Wang, Ning Li, Yuping Sun, Zhen Zhang, Na Tang, Haoran Li, Fan Zhang, Bing Tong, Hui Xu, and Tianmeng Chen
Atmos. Chem. Phys., 24, 8703–8720, https://doi.org/10.5194/acp-24-8703-2024, https://doi.org/10.5194/acp-24-8703-2024, 2024
Short summary
Short summary
The turbulence in the planetary boundary layer (PBL) over the Tibetan Plateau (TP) remains unclear. Here we elucidate the vertical profile of and temporal variation in the turbulence dissipation rate in the PBL over the TP based on a radar wind profiler (RWP) network. To the best of our knowledge, this is the first time that the turbulence profile over the whole TP has been revealed. Furthermore, the possible mechanisms of clouds acting on the PBL turbulence structure are investigated.
Xiaoran Guo, Jianping Guo, Tianmeng Chen, Ning Li, Fan Zhang, and Yuping Sun
Atmos. Chem. Phys., 24, 8067–8083, https://doi.org/10.5194/acp-24-8067-2024, https://doi.org/10.5194/acp-24-8067-2024, 2024
Short summary
Short summary
The prediction of downhill thunderstorms (DSs) remains elusive. We propose an objective method to identify DSs, based on which enhanced and dissipated DSs are discriminated. A radar wind profiler (RWP) mesonet is used to derive divergence and vertical velocity. The mid-troposphere divergence and prevailing westerlies enhance the intensity of DSs, whereas low-level divergence is observed when the DS dissipates. The findings highlight the key role that an RWP mesonet plays in the evolution of DSs.
Kaixu Bai, Ke Li, Liuqing Shao, Xinran Li, Chaoshun Liu, Zhengqiang Li, Mingliang Ma, Di Han, Yibing Sun, Zhe Zheng, Ruijie Li, Ni-Bin Chang, and Jianping Guo
Earth Syst. Sci. Data, 16, 2425–2448, https://doi.org/10.5194/essd-16-2425-2024, https://doi.org/10.5194/essd-16-2425-2024, 2024
Short summary
Short summary
A global gap-free high-resolution air pollutant dataset (LGHAP v2) was generated to provide spatially contiguous AOD and PM2.5 concentration maps with daily 1 km resolution from 2000 to 2021. This gap-free dataset has good data accuracies compared to ground-based AOD and PM2.5 concentration observations, which is a reliable database to advance aerosol-related studies and trigger multidisciplinary applications for environmental management, health risk assessment, and climate change analysis.
Boming Liu, Xin Ma, Jianping Guo, Renqiang Wen, Hui Li, Shikuan Jin, Yingying Ma, Xiaoran Guo, and Wei Gong
Atmos. Chem. Phys., 24, 4047–4063, https://doi.org/10.5194/acp-24-4047-2024, https://doi.org/10.5194/acp-24-4047-2024, 2024
Short summary
Short summary
Accurate wind profile estimation, especially for the lowest few hundred meters of the atmosphere, is of great significance for the weather, climate, and renewable energy sector. We propose a novel method that combines the power-law method with the random forest algorithm to extend wind profiles beyond the surface layer. Compared with the traditional algorithm, this method has better stability and spatial applicability and can be used to obtain the wind profiles on different land cover types.
Jinhong Xian, Chao Lu, Xiaoling Lin, Honglong Yang, Ning Zhang, and Li Zhang
Atmos. Meas. Tech., 17, 1837–1850, https://doi.org/10.5194/amt-17-1837-2024, https://doi.org/10.5194/amt-17-1837-2024, 2024
Short summary
Short summary
Improving the monitoring capability of atmospheric turbulence can help unravel the mystery of turbulence. Based on some assumptions, scientists have proposed various detection methods. However, these assumptions limit their applicability. We abandoned these assumptions and proposed a more accurate method, revealing some new results. Our method can provide more accurate three-dimensional features of turbulence, which will have a huge driving effect on the development of turbulence.
Jianping Guo, Jian Zhang, Jia Shao, Tianmeng Chen, Kaixu Bai, Yuping Sun, Ning Li, Jingyan Wu, Rui Li, Jian Li, Qiyun Guo, Jason B. Cohen, Panmao Zhai, Xiaofeng Xu, and Fei Hu
Earth Syst. Sci. Data, 16, 1–14, https://doi.org/10.5194/essd-16-1-2024, https://doi.org/10.5194/essd-16-1-2024, 2024
Short summary
Short summary
A global continental merged high-resolution (PBLH) dataset with good accuracy compared to radiosonde is generated via machine learning algorithms, covering the period from 2011 to 2021 with 3-hour and 0.25º resolution in space and time. The machine learning model takes parameters derived from the ERA5 reanalysis and GLDAS product as input, with PBLH biases between radiosonde and ERA5 as the learning targets. The merged PBLH is the sum of the predicted PBLH bias and the PBLH from ERA5.
Hui Xu, Jianping Guo, Bing Tong, Jinqiang Zhang, Tianmeng Chen, Xiaoran Guo, Jian Zhang, and Wenqing Chen
Atmos. Chem. Phys., 23, 15011–15038, https://doi.org/10.5194/acp-23-15011-2023, https://doi.org/10.5194/acp-23-15011-2023, 2023
Short summary
Short summary
The radiative effect of cloud remains one of the largest uncertain factors in climate change, largely due to the lack of cloud vertical structure (CVS) observations. The study presents the first near-global CVS climatology using high-vertical-resolution soundings. Single-layer cloud mainly occurs over arid regions. As the number of cloud layers increases, clouds tend to have lower bases and thinner layer thicknesses. The occurrence frequency of cloud exhibits a pronounced seasonal diurnal cycle.
Boming Liu, Xin Ma, Jianping Guo, Hui Li, Shikuan Jin, Yingying Ma, and Wei Gong
Atmos. Chem. Phys., 23, 3181–3193, https://doi.org/10.5194/acp-23-3181-2023, https://doi.org/10.5194/acp-23-3181-2023, 2023
Short summary
Short summary
Wind energy is one of the most essential clean and renewable forms of energy in today’s world. However, the traditional power law method generally estimates the hub-height wind speed by assuming a constant exponent between surface and hub-height wind speeds. This inevitably leads to significant uncertainties in estimating the wind speed profile. To minimize the uncertainties, we here use a machine learning algorithm known as random forest to estimate the wind speed at hub height.
Seoung Soo Lee, Junshik Um, Won Jun Choi, Kyung-Ja Ha, Chang Hoon Jung, Jianping Guo, and Youtong Zheng
Atmos. Chem. Phys., 23, 273–286, https://doi.org/10.5194/acp-23-273-2023, https://doi.org/10.5194/acp-23-273-2023, 2023
Short summary
Short summary
This paper elaborates on process-level mechanisms regarding how the interception of radiation by aerosols interacts with the surface heat fluxes and atmospheric instability in warm cumulus clouds. This paper elucidates how these mechanisms vary with the location or altitude of an aerosol layer. This elucidation indicates that the location of aerosol layers should be taken into account for parameterizations of aerosol–cloud interactions.
Seoung Soo Lee, Jinho Choi, Goun Kim, Kyung-Ja Ha, Kyong-Hwan Seo, Chang Hoon Jung, Junshik Um, Youtong Zheng, Jianping Guo, Sang-Keun Song, Yun Gon Lee, and Nobuyuki Utsumi
Atmos. Chem. Phys., 22, 9059–9081, https://doi.org/10.5194/acp-22-9059-2022, https://doi.org/10.5194/acp-22-9059-2022, 2022
Short summary
Short summary
This study investigates how aerosols affect clouds and precipitation and how the aerosol effects vary with varying types of clouds that are characterized by cloud depth in two metropolitan areas in East Asia. As cloud depth increases, the enhancement of precipitation amount transitions to no changes in precipitation amount with increasing aerosol concentrations. This indicates that cloud depth needs to be considered for a comprehensive understanding of aerosol-cloud interactions.
Peilin Song, Yongqiang Zhang, Jianping Guo, Jiancheng Shi, Tianjie Zhao, and Bing Tong
Earth Syst. Sci. Data, 14, 2613–2637, https://doi.org/10.5194/essd-14-2613-2022, https://doi.org/10.5194/essd-14-2613-2022, 2022
Short summary
Short summary
Soil moisture information is crucial for understanding the earth surface, but currently available satellite-based soil moisture datasets are imperfect either in their spatiotemporal resolutions or in ensuring image completeness from cloudy weather. In this study, therefore, we developed one soil moisture data product over China that has tackled most of the above problems. This data product has the potential to promote the investigation of earth hydrology and be extended to the global scale.
Kaixu Bai, Ke Li, Mingliang Ma, Kaitao Li, Zhengqiang Li, Jianping Guo, Ni-Bin Chang, Zhuo Tan, and Di Han
Earth Syst. Sci. Data, 14, 907–927, https://doi.org/10.5194/essd-14-907-2022, https://doi.org/10.5194/essd-14-907-2022, 2022
Short summary
Short summary
The Long-term Gap-free High-resolution Air Pollutant concentration dataset, providing gap-free aerosol optical depth (AOD) and PM2.5 and PM10 concentration with a daily 1 km resolution for 2000–2020 in China, is generated and made publicly available. This is the first long-term gap-free high-resolution aerosol dataset in China and has great potential to trigger multidisciplinary applications in Earth observations, climate change, public health, ecosystem assessment, and environment management.
Linye Song, Shangfeng Chen, Wen Chen, Jianping Guo, Conglan Cheng, and Yong Wang
Atmos. Chem. Phys., 22, 1669–1688, https://doi.org/10.5194/acp-22-1669-2022, https://doi.org/10.5194/acp-22-1669-2022, 2022
Short summary
Short summary
This study shows that in most years when haze pollution (HP) over the North China Plain (NCP) is more (less) serious in winter, air conditions in the following spring are also worse (better) than normal. Conversely, there are some years when HP in the following spring is opposed to that in winter. It is found that North Atlantic sea surface temperature (SST) anomalies play important roles in HP evolution over the NCP. Thus North Atlantic SST is an important preceding signal for NCP HP evolution.
Boming Liu, Jianping Guo, Wei Gong, Yong Zhang, Lijuan Shi, Yingying Ma, Jian Li, Xiaoran Guo, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-26, https://doi.org/10.5194/amt-2022-26, 2022
Publication in AMT not foreseen
Short summary
Short summary
Aeolus is the first satellite mission to directly observe wind profile information on a global scale. However, Aeolus wind products over China were thus far not evaluated by in-situ comparison. This work is the comparison of wind speed on a large scale between the Aeolus, ERA5 and RS , shedding important light on the data application of Aeolus wind products.
Jianping Guo, Jian Zhang, Kun Yang, Hong Liao, Shaodong Zhang, Kaiming Huang, Yanmin Lv, Jia Shao, Tao Yu, Bing Tong, Jian Li, Tianning Su, Steve H. L. Yim, Ad Stoffelen, Panmao Zhai, and Xiaofeng Xu
Atmos. Chem. Phys., 21, 17079–17097, https://doi.org/10.5194/acp-21-17079-2021, https://doi.org/10.5194/acp-21-17079-2021, 2021
Short summary
Short summary
The planetary boundary layer (PBL) is the lowest part of the troposphere, and boundary layer height (BLH) is the depth of the PBL and is of critical importance to the dispersion of air pollution. The study presents the first near-global BLH climatology by using high-resolution (5-10 m) radiosonde measurements. The variations in BLH exhibit large spatial and temporal dependence, with a peak at 17:00 local solar time. The most promising reanalysis product is ERA-5 in terms of modeling BLH.
Seoung Soo Lee, Kyung-Ja Ha, Manguttathil Gopalakrishnan Manoj, Mohammad Kamruzzaman, Hyungjun Kim, Nobuyuki Utsumi, Youtong Zheng, Byung-Gon Kim, Chang Hoon Jung, Junshik Um, Jianping Guo, Kyoung Ock Choi, and Go-Un Kim
Atmos. Chem. Phys., 21, 16843–16868, https://doi.org/10.5194/acp-21-16843-2021, https://doi.org/10.5194/acp-21-16843-2021, 2021
Short summary
Short summary
Using a modeling framework, a midlatitude stratocumulus cloud system is simulated. It is found that cloud mass in the system becomes very low due to interactions between ice and liquid particles compared to that in the absence of ice particles. It is also found that interactions between cloud mass and aerosols lead to a reduction in cloud mass in the system, and this is contrary to an aerosol-induced increase in cloud mass in the absence of ice particles.
Cited articles
Anthes, R. A., Kuo, Y. H., Benjamin, S. G., and Li, Y. F: The evolution of the mesoscale environment of severe local storms: Preliminary modeling results, Mon. Weather Rev., 110, 1187–1213, https://doi.org/10.1175/1520-0493(1982)110<1187:TEOTME>2.0.CO;2, 1982.
Bai, L., Chen, G., Huang, Y., and Meng, Z.: Convection initiation at a coastal rainfall hotspot in South China: Synoptic patterns and orographic effects, J. Geophys. Res.-Atmos., 126, e2021JD034642, https://doi.org/10.1029/2021JD034642, 2021.
Blackadar, A. K.: Boundary layer wind maxima and their significance for the growth of nocturnal inversions, Bull. Am. Meteorol. Soc., 38, 283–290, https://doi.org/10.1175/1520-0477-38.5.283, 1957.
Bonner, W. D.: Climatology of the low-level jet, Mon. Weather Rev., 96, 833–850, https://doi.org/10.1175/1520-0493(1968)096<0833:COTLLJ>2.0.CO;2, 1968.
Cao, Q., Guo, J., Xue, C., Li, Z., Xu, H., Li, N., Sun, Y., Zhang, Z., Wang, Y., Chen, J., Zhou, Y., and Chen, T.: Divergent Sounding-derived Precursor Pathways Enable Discrimination of Dry versus Wet Severe Convective Winds, Geophys. Res. Lett., 52, e2025GL117491, https://doi.org/10.1029/2025GL117491, 2025.
Carbone, R. E. and Tuttle, J. D.: Rainfall occurrence in the US warm season: The diurnal cycle, J. Clim., 21, 4132–4146, https://doi.org/10.1175/2008JCLI2275.1, 2008.
Chen, G., Sha, W., Iwasaki, T., and Wen, Z.: Diurnal Cycle of a Heavy Rainfall Corridor over East Asia, Mon. Weather Rev., 145, 3365–3389, https://doi.org/10.1175/mwr-d-16-0423.1, 2017.
Chen, H., Zhou, T., Neale, R. B., Wu, X., and Zhang, G. J.: Performance of the new NCAR CAM3. 5 in East Asian summer monsoon simulations: Sensitivity to modifications of the convection scheme, J. Clim., 23, 3657–3675, https://doi.org/10.1175/2010JCLI3022.1, 2010.
Chen, T., Guo, J., Guo, X., Zhang, Y., Xu, H., and Zhang, D.-L.: On the multiscale processes leading to an extreme gust wind event in East China: Insights from radar wind profiler mesonet observations, J. Geophys. Res.-Atmos., 129, e2024JD041484, https://doi.org/10.1029/2024JD041484, 2024.
Chen, X., Zhang, F., and Zhao, K.: Diurnal Variations of the Land-Sea Breeze and Its Related Precipitation over South China, J. Atmos. Sci., 73, 4793–4815, https://doi.org/10.1175/JAS-D-16-0106.1, 2016.
China Meteorological Administration: Specification for Automatic Observation of Ground Meteorology, China Meteorological Press, https://ncooc.121.com.cn/qhgxt/annex/3/32/1200/2024030614361731.pdf (last access: 28 February 2026), 2020.
Davis, C. A., Maning, W. K., Carbone, R., Trier, S., and Tuttle, J.: Coherence of warm-season continental rainfall in numerical weather prediction models, Mon. Weather Rev., 131, 2667–2679, https://doi.org/10.1175/1520-0493(2003)131<2667:COWCRI>2.0.CO;2, 2003.
Dong, F., Zhi, X., Zhang, L., and Ye, C.: Diurnal variations of coastal boundary layer jets over the northern South China Sea and their impacts on diurnal cycle of rainfall over southern China during the early-summer rainy season, Mon. Weather Rev., 149, 3341–3363, https://doi.org/10.1175/MWR-D-20-0292.1, 2021.
Doubler, D. L., Winkler, J. A., Bian, X., Walters, C. K., and Zhong, S.: An NARR-derived climatology of southerly and northerly low-level jets over North America and coastal environs, J. Appl. Meteorol. Climatol., 54, 1596–1619, https://doi.org/10.1175/JAMC-D-14-0311.1, 2015.
Du, Y. and Chen, G.: Heavy Rainfall Associated with Double Low-Level Jets over Southern China. Part I: Ensemble-Based Analysis, Mon. Weather Rev., 146, 3827–3844, https://doi.org/10.1175/MWR-D-18-0101.1, 2018.
Du, Y. and Chen, G.: Heavy Rainfall Associated with Double Low-Level Jets over Southern China. Part II: Convection Initiation, Mon. Weather Rev., 147, 543–565, https://doi.org/10.1175/mwr-d-18-0102.1, 2019.
Du, Y., Zhang, Q., Ying, Y., and Yang, Y.: Characteristics of low-level jets in Shanghai during the 2008-2009 warm seasons as inferred from wind profiler radar data, J. Meteorol. Soc. Jpn. Ser. II, 90, 891–903, https://doi.org/10.2151/jmsj.2012-603, 2012.
Du, Y., Zhang, Q., Chen, Y., Zhao, Y., and Wang, X.: Numerical simulations of spatial distributions and diurnal variations of low-level jets in China during early summer, J. Clim., 27, 5747–5767, https://doi.org/10.1175/JCLI-D-13-00571.1, 2014.
Du, Y., Chen, G., Han, B., Bai, L., and Li, M.: Convection initiation and growth at the coast of South China. Part II: Effects of the terrain, coastline, and cold pools, Mon. Weather Rev., 148, 3871–3892, https://doi.org/10.1175/MWR-D-20-0090.1, 2020.
Fu, S. M., Jin, S. L., Shen, W., Li, D. Y., Liu, B., and Sun, J. H.: A kinetic energy budget on the severe wind production that causes a serious state grid failure in Southern Xinjiang China, Atmos. Sci. Lett., 21, e977, https://doi.org/10.1002/asl.977, 2020.
Gebauer, J. G., Shapiro, A., Fedorovich, E., and Klein, P.: Convection Initiation Caused by Heterogeneous Low-Level Jets over the Great Plains, Mon. Weather Rev., 146, 2615–2637, https://doi.org/10.1175/mwr-d-18-0002.1, 2018.
Guo, X., Guo, J., Zhang, D. L., and Yun, Y.: Vertical divergence profiles as detected by two wind-profiler mesonets over East China: Implications for nowcasting convective storms, Q. J. R. Meteorol. Soc., 149, 1629–1649, https://doi.org/10.1002/qj.4474, 2023.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J-N.: ERA5 hourly data on pressure levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/cds.bd0915c6, 2023.
Higgins, R. W., Yao, Y., Yarosh, E. S., Janowiak, J. E., and Mo, K. C.: Influence of the Great Plains low-level jet on summertime precipitation and moisture transport over the central United States, J. Clim., 10, 481–507, 1997.
Hodges, D. and Pu, Z.: Characteristics and Variations of Low-Level Jets and Environmental Factors Associated with Summer Precipitation Extremes over the Great Plains, J. Clim., 32, 5123–5144, https://doi.org/10.1175/JCLI-D-18-0553.1, 2019.
Holton, J. R.: The diurnal boundary layer wind oscillation above sloping terrain, Tellus, 19, 200–205, https://doi.org/10.3402/tellusa.v19i2.9766, 1967.
Horinouchi, T., Matsumura, S., Ose, T., and Takayabu, Y. N.: Jet-precipitation relation and future change of the mei-yu-baiu rainband and subtropical jet in CMIP5 coupled GCM simulations, J. Clim., 32, 2247–2259, https://doi.org/10.1175/JCLI-D-18-0426.1, 2019.
Huang, X., Sun, J., and Liu, W.: The interaction between low-level jet evolution and severe convective rainstorms under topographic effect, Acta Meteorol. Sin., 78, 551–567, 2020.
Li, N. and Guo, J.: Data for wind profiles associated with LLJs obtained from RWP observations during the warm season (April-October) of 2023–2024 in China, Zenodo [data set], https://doi.org/10.5281/zenodo.17176759, 2025.
Li, N., Guo, J., Wu, M., Zhang, F., Guo, X., Sun, Y., Zhang, Z., Liang, L., and Chen, T.: Low-level jet and its effect on the onset of summertime nocturnal rainfall in Beijing, Geophys. Res. Lett., 51, e2024GL110840, https://doi.org/10.1029/2024GL110840, 2024.
Li, X. and Du, Y.: Statistical relationships between two types of heavy rainfall and low-level jets in South China, J. Clim., 34, 8549–8566, https://doi.org/10.1175/JCLI-D-21-0121.1, 2021.
Liu, B., Ma, Y., Guo, J., and Wei, G.: Boundary layer heights as derived from ground-based radar wind profiler in Beijing, IEEE Trans. Geosci. Remote Sens., 57, 8095–8104, https://doi.org/10.1109/TGRS.2019.2918301, 2019.
Liu, S., Zheng, Y., and Tao, Z.: The analysis of the relationship between pulse of LLJ and heavy rain using wind profiler data, J. Trop. Meteorol., 2, 158–163, 2003 (in Chinese).
Liu, X., Luo, Y., Huang, L., Zhang, D. L., and Guan, Z: Roles of double low-level jets in the generation of coexisting inland and coastal heavy rainfall over south China during the presummer rainy season, J. Geophys. Res.-Atmos., 125, e2020JD032890, https://doi.org/10.1029/2020JD032890, 2020.
Luo, Y., Gong, Y., and Zhang, D. L.: Initiation and organizational modes of an extreme-rain-producing mesoscale convective system along a mei-yu front in East China, Mon. Weather Rev., 142, 203–221, https://doi.org/10.1175/MWR-D-13-00111.1, 2014.
Maddox, R. A.: Large-scale meteorological conditions associated with midlatitude, mesoscale convective complexes, Mon. Weather Rev., 111, 1475–1493, 1983.
Maddox, R. A. and Doswell III, C. A.: An examination of jet stream configurations, 500 mb vorticity advection and low-level thermal advection patterns during extended periods of intense convection, Mon. Weather Rev., 110, 184–197, https://doi.org/10.1175/1520-0493(1982)110<0184:AEOJSC>2.0.CO;2, 1982.
Marengo, J. A., Soares, W. R., Saulo, C., and Nicolini, M.: Climatology of the low-level jet east of the Andes as derived from the NCEP-NCAR reanalyses: Characteristics and temporal variability, J. Clim., 17, 2261–2280, 2004.
Markowski, P. and Richardson, Y.: Mesoscale meteorology in midlatitudes, John Wiley & Sons, https://doi.org/10.1002/9780470682104, 2011.
Miao, Y., Guo, J., Liu, S., Wei, W., Zhang, G., Lin, Y., and Zhai, P.: The Climatology of Low-Level Jet in Beijing and Guangzhou, China, J. Geophys. Res.-Atmos., 123, 2816–2830, https://doi.org/10.1002/2017jd027321, 2018.
Mitchell, M. J., Arritt, R. W., and Labas, K.: A climatology of the warm season Great Plains low-level jet using wind profiler observations, Weather Forecast., 10, 576–591, 1995.
Molod, A., Salmun, H., and Collow, A. B. M.: Annual cycle of planetary boundary layer heights estimated from wind profiler network data, J. Geophys. Res.-Atmos., 124, 6207–6221, https://doi.org/10.1029/2018JD030102, 2019.
Monaghan, A. J., Rife, D. L., Pinto, J. O., Davis, C. A., and Hannan, J. R.: Global precipitation extremes associated with diurnally varying low-level jets, J. Clim., 23, 5065–5084, 2010.
Pan, H. and Chen, G.: Diurnal variations of precipitation over North China regulated by the mountain-plains solenoid and boundary-layer inertial oscillation, Adv. Atmos. Sci., 36, 863–884, https://doi.org/10.1175/2010JCLI3515.1, 2019.
Pitchford, K. L. and London, J.: The low-level jet as related to nocturnal thunderstorms over Midwest United States, J. Appl. Meteorol., 1, 43–47, 1962.
Rasmusson, E. M.: Atmospheric water vapor transport and the water balance of North America. Part I: Characteristics of the water vapor flux field, Mon. Weather Rev., 95, 403–426, https://doi.org/10.1175/1520-0493(1967)095,0403:AWVTAT.2.3.CO;2, 1967.
Rasmussen, K. L. and Houze Jr, R. A.: Convective initiation near the Andes in subtropical South America, Mon. Weather Rev., 144, 2351–2374, https://doi.org/10.1175/MWR-D-15-0058.1, 2016.
Reif, D. Q. and Bluestein, H.: A 20-year climatology of nocturnal convection initiation over the central and southern great plains during the warm season, Mon. Weather Rev., 145, 1615–1639, https://doi.org/10.1175/MWR-D-16-0340.1, 2017.
Roots, M., Sullivan, J. T., and Demoz, B.: Mid-Atlantic nocturnal low-level jet characteristics: a machine learning analysis of radar wind profiles, Atmos. Meas. Tech., 18, 1269–1282, https://doi.org/10.5194/amt-18-1269-2025, 2025.
Stensrud, D. J.: Importance of low-level jets to climate: A review, J. Clim., 9, 1698–1711, 1996.
Subrahmayam, K. V., Udupa, S. R., Kumar, K. K., Ramana, M. V., Srinivasulu, J., and Bothale, R. V.: Prediction of zonal wind using machine learning algorithms: Implications to future projections of Indian monsoon jets, J. Indian Soc. Remote Sens., 52, 371–381, 2024.
Sun, S. Q., Zhai, G. H., and Lorenzo, D. O.: The relationship between large-scale low-level jet over east asia and monsoon as well as cross-equatorial currents in summer, Acta Meteorol. Sin., 1, 43–50, 1986.
Trier, S. B., Wilson, J. W., Ahijevych, D. A., and Sobash, R. A.: Mesoscale vertical motions near nocturnal convection initiation in PECAN, Mon. Weather Rev., 145, 2919–2941, https://doi.org/10.1175/MWR-D-17-0005.1, 2017.
Trier, S. B., Davis, C. A., Ahijevych, D. A., Weisman, M. L., and Bryan, G. H.: Mechanisms supporting long-lived episodes of propagating nocturnal convection within a 7-day WRF model simulation, J. Atmos. Sci., 63, 2437–2461, https://doi.org/10.1175/JAS3768.1, 2006.
Tuttle, J. D. and Davis, C. A.: Corridors of warm season precipitation in the central United States, Mon. Weather Rev., 134, 2297–2317, https://doi.org/10.1175/MWR3188.1, 2006.
Uccellini, L. W. and Johnson, D. R.: The coupling of upper and lower tropospheric jet streaks and implications for the development of severe convective storms, Mon. Weather Rev., 107, 682–703, https://doi.org/10.1175/1520-0493(1979)107<0682:TCOUAL>2.0.CO;2, 1979.
Walters, C. K., Winkler, J. A., Shadbolt, R. P., van Ravensway, J., and Bierly, G. D.: A long-term climatology of southerly and northerly low-level jets for the central United States, Ann. Assoc. Am. Geogr., 98, 521–552, 2008.
Wang, D., Zhang, Y., and Huang, A.: Climatic features of the south-westerly low-level jet over southeast China and its association with precipitation over east China, Asia-Pac, J. Atmos. Sci., 49, 259–270, https://doi.org/10.1007/s13143-013-0025-y, 2013.
Weckwerth, T. M. and Wakimoto, R. M.: The initiation and organization of convective cells atop a cold-air outflow boundary, Mon. Weather Rev., 120, 2169–2187, https://doi.org/10.1175/1520-0493(1992)120<2169:TIAOOC>2.0.CO;2, 1992.
Weckwerth, T. M., Hanesiak, J., Wilson, J. W., Trier, S. B., Degelia, S. K., Gallus Jr., W. A., Roberts, R. D., and Wang, X.: Nocturnal convection initiation during PECAN 2015, Bull. Am. Meteorol. Soc., 100, 2223–2239, https://doi.org/10.1175/BAMS-D-18-0299.1, 2019.
Wei, W., Zhang, H. S., and Ye, X. X.: Comparison of low-level jets along the north coast of China in summer, J. Geophys. Res.-Atmos., 119, 9692–9706, https://doi.org/10.1002/2014jd021476, 2014.
Weisman, M. L., Trapp, R. J., Romine, G. S., Davis, C., Torn, R., Baldwin, M., Bosart, L., Brown, J., Coniglio, M., Dowell, D., Evans, A. C., Galarneau, T. J., Haggerty, J., Hock, T., Manning, K., Roebber, P., Romashkin, P., Schumacher, R., Schwartz, C. S., Sobash, R., Stensrud, D., and Trier, S. B.: The mesoscale predictability experiment (MPEX), Bull. Am. Meteorol. Soc., 96, 2127–2149, https://doi.org/10.1175/bams-d-13-00281.1, 2015.
Whiteman, C. D., Bian, X., and Zhong, S.: Low-level jet climatology from enhanced Rawinsonde observations at a site in the southern Great Plains, J. Appl. Meteorol., 36, 1363–1376, https://doi.org/10.1175/1520-0450(1997)036<1363:LLJCFE>2.0.CO;2, 1997.
Xia, R. and Zhao, S.: Diagnosis and modeling of meso-scale systems of heavy rainfall in warm sector ahead of front in South China (middle part of Guangdong province) in June 2005, Chin. J. Atmos. Sci., 33, https://doi.org/10.3878/j.issn.1006-9895.2009.03.06, 2009.
Xue, M., Luo, X., Zhu, K., Sun, Z., and Fei, J.: The controlling role of boundary layer inertial oscillations in Meiyu frontal precipitation and its diurnal cycles over China, J. Geophys. Res.-Atmos., 123, 5090–5115, https://doi.org/10.1029/2018JD028368, 2018.
Yan, Y., Cai, X., Wang, X., Miao, Y., and Song, Y.: Low-level jet climatology of China derived from long-term radiosonde observations, J. Geophys. Res.-Atmos., 126, e2021JD035323, https://doi.org/10.1029/2021JD035323, 2021.
Yu, R., Li, J., Chen, H., and Yuan, W.: Progress in studies of the precipitation diurnal variation over contiguous China, J. Meteorol. Res., 28, 877–902, https://doi.org/10.1007/s13351-014-3272-7, 2014.
Zamora, R. J., Shapiro, M. A., and Doswell III., C. A.: The diagnosis of upper tropospheric divergence and ageostrophic wind using profiler wind observations, Mon. Weather Rev., 115, 871–884, https://doi.org/10.1175/1520-0493(1987)115<0871:TDOUTD>2.0.CO;2, 1987.
Zhang, F., Zhang, Q., Sun, J., and Xu, J.: Convection initiation during the Meiyu environment in the Yangtze-Huai River basin of China, J. Geophys. Res.-Atmos., 128, e2022JD038077, https://doi.org/10.1029/2022JD038077, 2023.
Zhang, M. and Meng, Z.: Warm-sector heavy rainfall in southern China and its WRF simulation evaluation: A low-level-jet perspective, Mon. Weather Rev., 147, 4461–4480, https://doi.org/10.1175/MWR-D-19-0110.1, 2019.
Zhang, Y., Xue, M., Zhu, K., and Zhou, B.: What is the main cause of diurnal variation and nocturnal peak of summer precipitation in Sichuan Basin, China? the key role of boundary layer low-level jet inertial oscillations, J. Geophys. Res.-Atmos., 124, 2643–2664, https://doi.org/10.1029/2018jd029834, 2019.
Zhao, Y., Liao, J., Zhang, Q., Chen, J., Gong, X., Shi, Y., Shi, M., Yang, D., Fan, S., Zhou, X., Cao, L., and Hu, K.: Development of China Ground Climate Normal Value Dataset from 1991 to 2020, Chin. J. Atmos. Sci., 48, 555–571, https://doi.org/10.3878/j.issn.1006-9895.2204.22010, 2024 (in Chinese).
Short summary
Nighttime rainfall often links to low-level jets (LLJs), but we lack clarity on nationwide LLJ features. We here used a nationwide radar wind profiler network to study LLJ changes 2 hours before rainfall, covering China’s 2023–2024 rainy seasons. 56% nighttime rainfall had LLJs. The LLJs-associated heavy rain needed a rapid adjustment of LLJs’ vertical structure, especially a significant intensification within 30 minutes preceding rain. This shows the importance of LLJ in nowcasting rainfall.
Nighttime rainfall often links to low-level jets (LLJs), but we lack clarity on nationwide LLJ...
Altmetrics
Final-revised paper
Preprint