Articles | Volume 17, issue 8
https://doi.org/10.5194/essd-17-4023-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-4023-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
21 Aug 2025
Data description paper |
| 21 Aug 2025
| 21 Aug 2025
Low-level atmospheric turbulence dataset in China generated by combining radar wind profiler and radiosonde observations
Deli Meng
State Key Laboratory of Severe Weather Meteorological Science and Technology, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Xiong'an Atmospheric Boundary Layer Key Laboratory of China Meteorological Administration, Beijing 100085, China
State Key Laboratory of Severe Weather Meteorological Science and Technology, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Guizhou New Meteorological Technology Co., Ltd, Guiyang 550001, China
Juan Chen
AVIC Leihua Electronic Technology Research Institute, Wuxi 214063, China
Xiaoran Guo
State Key Laboratory of Severe Weather Meteorological Science and Technology, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Ning Li
State Key Laboratory of Severe Weather Meteorological Science and Technology, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Yuping Sun
State Key Laboratory of Severe Weather Meteorological Science and Technology, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Zhen Zhang
State Key Laboratory of Severe Weather Meteorological Science and Technology, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
Na Tang
State Key Laboratory of Severe Weather Meteorological Science and Technology, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Hui Xu
State Key Laboratory of Severe Weather Meteorological Science and Technology, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Tianmeng Chen
State Key Laboratory of Severe Weather Meteorological Science and Technology, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Rongfang Yang
Hebei Meteorological Technology and Equipment Center, Shijiazhuang 050022, China
Jiajia Hua
Xiong'an Atmospheric Boundary Layer Key Laboratory of China Meteorological Administration, Beijing 100085, China
Related authors
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, 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.
Xiaozhong Cao, Qiyun Guo, Haowen Luo, Rongkang Yang, Peng Zhang, Jianping Guo, Jincheng Wang, Die Xiao, Jianping Du, Zhongliang Sun, Shijun Liu, Sijie Chen, and Anfan Huang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2012, https://doi.org/10.5194/egusphere-2025-2012, 2025
Short summary
Short summary
This study aims to introduce in-situ profiling techniques and cost-effective technology for upper-air observation—the Round-trip Drifting Sounding System (RDSS)—which reduces costs relative to intensive sounding and achieves three sounding phases: Ascent-Drift-Descent (ADD). The RDSS not only provides additional data for weather analysis and numerical prediction models but also makes substantial contributions to targeted observations.
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.
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.
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.
Ifeanyichukwu C. Nduka, Chi-Yung Tam, Jianping Guo, and Steve Hung Lam Yim
Atmos. Chem. Phys., 21, 13443–13454, https://doi.org/10.5194/acp-21-13443-2021, https://doi.org/10.5194/acp-21-13443-2021, 2021
Short summary
Short summary
This study analyzed the nature, mechanisms and drivers for hot-and-polluted episodes (HPEs) in the Pearl River Delta, China. A total of eight HPEs were identified and can be grouped into three clusters of HPEs that were respectively driven (1) by weak subsidence and convection induced by approaching tropical cyclones, (2) by calm conditions with low wind speed in the lower atmosphere and (3) by the combination of both aforementioned conditions.
Tianmeng Chen, Zhanqing Li, Ralph A. Kahn, Chuanfeng Zhao, Daniel Rosenfeld, Jianping Guo, Wenchao Han, and Dandan Chen
Atmos. Chem. Phys., 21, 6199–6220, https://doi.org/10.5194/acp-21-6199-2021, https://doi.org/10.5194/acp-21-6199-2021, 2021
Short summary
Short summary
A convective cloud identification process is developed using geostationary satellite data from Himawari-8.
Convective cloud fraction is generally larger before noon and smaller in the afternoon under polluted conditions, but megacities and complex topography can influence the pattern.
A robust relationship between convective cloud and aerosol loading is found. This pattern varies with terrain height and is modulated by varying thermodynamic, dynamical, and humidity conditions during the day.
Jianping Guo, Boming Liu, Wei Gong, Lijuan Shi, Yong Zhang, Yingying Ma, Jian Zhang, Tianmeng Chen, Kaixu Bai, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Chem. Phys., 21, 2945–2958, https://doi.org/10.5194/acp-21-2945-2021, https://doi.org/10.5194/acp-21-2945-2021, 2021
Short summary
Short summary
Vertical wind profiles are crucial to a wide range of atmospheric disciplines. Aeolus is the first satellite mission to directly observe wind profile information on a global scale. However, Aeolus wind products over China have thus far not been evaluated by in situ comparison. This work is expected to let the public and science community better know the Aeolus wind products and to encourage use of these valuable data in future research and applications.
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. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-41, https://doi.org/10.5194/acp-2021-41, 2021
Revised manuscript not accepted
Short summary
Short summary
Vertical wind profiles are crucial to a wide range of atmospheric disciplines. 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 expected to let the public and science community better know the Aeolus wind products and to encourage use of these valuable data in future researches and applications.
Kaixu Bai, Ke Li, Chengbo Wu, Ni-Bin Chang, and Jianping Guo
Earth Syst. Sci. Data, 12, 3067–3080, https://doi.org/10.5194/essd-12-3067-2020, https://doi.org/10.5194/essd-12-3067-2020, 2020
Short summary
Short summary
PM2.5 data from the national air quality monitoring network in China suffered from significant inconsistency and inhomogeneity issues. To create a coherent PM2.5 concentration dataset to advance our understanding of haze pollution and its impact on weather and climate, we homogenized this PM2.5 dataset between 2015 and 2019 after filling in the data gaps. The homogenized PM2.5 data is found to better characterize the variation of aerosol in space and time compared to the original dataset.
Yang Yang, Min Chen, Xiujuan Zhao, Dan Chen, Shuiyong Fan, Jianping Guo, and Shaukat Ali
Atmos. Chem. Phys., 20, 12527–12547, https://doi.org/10.5194/acp-20-12527-2020, https://doi.org/10.5194/acp-20-12527-2020, 2020
Short summary
Short summary
This study analyzed the impacts of aerosol–radiation interaction on radiation and meteorological forecasts using the offline coupling of WRF and high-frequency updated AOD simulated by WRF-Chem. The results revealed that aerosol–radiation interaction had a positive influence on the improvement of predictive accuracy, including 2 m temperature (~ 73.9 %) and horizontal wind speed (~ 7.8 %), showing potential prospects for its application in regional numerical weather prediction in northern China.
Ruqian Miao, Qi Chen, Yan Zheng, Xi Cheng, Yele Sun, Paul I. Palmer, Manish Shrivastava, Jianping Guo, Qiang Zhang, Yuhan Liu, Zhaofeng Tan, Xuefei Ma, Shiyi Chen, Limin Zeng, Keding Lu, and Yuanhang Zhang
Atmos. Chem. Phys., 20, 12265–12284, https://doi.org/10.5194/acp-20-12265-2020, https://doi.org/10.5194/acp-20-12265-2020, 2020
Short summary
Short summary
In this study we evaluated the model performances for simulating secondary inorganic aerosol (SIA) and organic aerosol (OA) in PM2.5 in China against comprehensive datasets. The potential biases from factors related to meteorology, emission, chemistry, and atmospheric removal are systematically investigated. This study provides a comprehensive understanding of modeling PM2.5, which is important for studies on the effectiveness of emission control strategies.
Boming Liu, Jianping Guo, Wei Gong, Lijuan Shi, Yong Zhang, and Yingying Ma
Atmos. Meas. Tech., 13, 4589–4600, https://doi.org/10.5194/amt-13-4589-2020, https://doi.org/10.5194/amt-13-4589-2020, 2020
Short summary
Short summary
Vertical wind profiles are crucial to a wide range of atmospheric disciplines. However, the wind profile across China remains poorly understood. Here we reveal the salient features of winds from the radar wind profile of China, including the main instruments, spatial coverage and sampling frequency. This work is expected to allow the public and scientific community to be more familiar with the nationwide network and encourage the use of these valuable data in future research and applications.
Cited articles
Brunke, M. A., Cutler, L., Urzua, R. D., Corral, A. F., Crosbie, E., Hair, J., Hostetler, C., Kirschler, S., Larson, V., Li, X. Y., Ma, P. L., Minke, A., Moore, R., Robinson, C. E., Scarino, A. J., Schlosser, J., Shook, M., Sorooshian, A., Thornhill, K. L., Voigt, C., Wan, H., Wang, H. L., Winstead, E., Zeng, X. B., Zhang, S. X., and Ziemba, L. D.: Aircraft observations of turbulence in cloudy and cloud-free boundary layers over the western north Atlantic ocean from ACTIVATE and implications for the earth system model evaluation and development, J. Geophys. Res.-Atmos., 127, e2022JD036480, https://doi.org/10.1029/2022jd036480, 2022.
Chechin, D. G., Lüpkes, C., Hartmann, J., Ehrlich, A., and Wendisch, M.: Turbulent structure of the Arctic boundary layer in early summer driven by stability, wind shear and cloud-top radiative cooling: ACLOUD airborne observations, Atmos. Chem. Phys., 23, 4685–4707, https://doi.org/10.5194/acp-23-4685-2023, 2023.
Chen, Z., Tian, Y. F., and Lue, D. R.: Turbulence parameters in the troposphere-lower stratosphere observed by Beijing MST radar, Remote Sens., 14, 18, https://doi.org/10.3390/rs14040947, 2022a.
Chen, Z., Tian, Y., Wang, Y., Bi, Y., Wu, X., Huo, J., Pan, L., Wang, Y., and Lü, D.: Turbulence parameters measured by the Beijing mesosphere–stratosphere–troposphere radar in the troposphere and lower stratosphere with three models: comparison and analyses, Atmos. Meas. Tech., 15, 4785–4800, https://doi.org/10.5194/amt-15-4785-2022, 2022b.
Clayson, C. A. and Kantha, L.: On turbulence and mixing in the free atmosphere inferred from high-resolution soundings, J. Atmos. Ocean. Tech., 25, 833–852, https://doi.org/10.1175/2007jtecha992.1, 2008.
Cohn, S. A.: Radar Measurements of Turbulent eddy dissipation rate in the troposphere a comparison of techniques, J. Atmos. Ocean. Tech., 12, 85–95, https://doi.org/10.1175/1520-0426(1995)012<0085:Rmoted>2.0.Co;2, 1995.
Dehghan, A. and Hocking, W. K.: Instrumental errors in spectral-width turbulence measurements by radars, J. Atmos. Sol.-Terr. Phy., 73, 1052–1068, https://doi.org/10.1016/j.jastp.2010.11.011, 2011.
Eaton, F. D. and Nastrom, G. D.: Preliminary estimates of the vertical profiles of inner and outer scales from White Sands Missile Range, New Mexico, VHF radar observations, Radio Sci., 33, 895–903, https://doi.org/10.1029/98rs01254, 1998.
Fukao, S., Yamanaka, M. D., Ao, N., Hocking, W. K., Sato, T., Yamamoto, M., Nakamura, T., Tsuda, T., and Kato, S.: Seasonal variability of vertical eddy diffusivity in the middle atmosphere 1. Three-year observations by the middle and upper atmosphere radar, J. Geophys. Res.-Atmos., 99, 18973–18987, https://doi.org/10.1029/94jd00911, 1994.
Fukao, S., Hamazu, K., and Doviak, R. J.: Radar for meteorological and atmospheric observations, Springer, https://doi.org/10.1007/978-4-431-54334-3, 2014.
Gage, K. S. and Balsley, B. B.: Doppler radar probing of the clear atmosphere, B. Am. Meteorol. Soc., 59, 1074–1093, https://doi.org/10.1175/1520-0477(1978)059<1074:Drpotc>2.0.Co;2, 1978.
Ghosh, A. K., Jain, A. R., and Sivakumar, V.: Simultaneous MST radar and radiosonde measurements at Gadanki (13.5° N, 79.2° E) 2. Determination of various atmospheric turbulence parameters, Radio Sci., 38, 12, https://doi.org/10.1029/2000rs002528, 2003.
Guo, J., Miao, Y., Zhang, Y., Liu, H., Li, Z., Zhang, W., He, J., Lou, M., Yan, Y., Bian, L., and Zhai, P.: The climatology of planetary boundary layer height in China derived from radiosonde and reanalysis data, Atmos. Chem. Phys., 16, 13309–13319, https://doi.org/10.5194/acp-16-13309-2016, 2016.
Hocking, W. K.: Measurement of turbulent energy dissipation rates in the middle atmosphere by radar techniques A review, Radio Sci., 20, 1403–1422, https://doi.org/10.1029/RS020i006p01403, 1985.
Hocking, W. K. and Mu, P. K. L.: Upper and middle tropospheric kinetic energy dissipation rates from measurements of (Cn2)over-bar – review of theories, in-situ investigations, and experimental studies using the Buckland Park atmospheric radar in Australia, J. Atmos. Sol.-Terr. Phy., 59, 1779–1803, https://doi.org/10.1016/s1364-6826(97)00020-5, 1997.
Jacoby-Koaly, S., Campistron, B., Bernard, S., Bénech, B., Girard-Ardhuin, F., Dessens, J., Dupont, E., and Carissimo, B.: Turbulent dissipation rate in the boundary layer via UHF wind profiler Doppler spectral width measurements, Bound.-Lay. Meteorol., 103, 361–389, https://doi.org/10.1023/a:1014985111855, 2002.
Jaiswal, A., Phanikumar, D. V., Bhattacharjee, S., and Naja, M.: Estimation of turbulence parameters using aries st radar and gps radiosonde measurements: first results from the central himalayan region, Radio Sci., 55, 18, https://doi.org/10.1029/2019rs006979, 2020.
Ko, H. C., Chun, H. Y., Geller, M. A., and Ingleby, B.: Global distributions of atmospheric turbulence estimated using operational high vertical-resolution radiosonde data, B. Am. Meteorol. Soc., 105, E2551–E2566, https://doi.org/10.1175/bams-d-23-0193.1, 2024.
Kohma, M., Sato, K., Tomikawa, Y., Nishimura, K., and Sato, T.: Estimate of turbulent energy dissipation rate from the VHF radar and radiosonde observations in the Antarctic, J. Geophys. Res.-Atmos., 124, 2976–2993, https://doi.org/10.1029/2018jd029521, 2019.
Li, Q., Rapp, M., Schrön, A., Schneider, A., and Stober, G.: Derivation of turbulent energy dissipation rate with the Middle Atmosphere Alomar Radar System (MAARSY) and radiosondes at Andoya, Norway, Ann. Geophys., 34, 1209–1229, https://doi.org/10.5194/angeo-34-1209-2016, 2016.
Lilly, D. K., Waco, D. E., and Adelfang, S. I.: Stratospheric mixing estimated from high-altitude turbulence measurements, J. Appl. Meteorol., 13, 488–493, https://doi.org/10.1175/1520-0450(1974)013<0488:Smefha>2.0.Co;2, 1974.
Lotfy, E. R., Abbas, A. A., Zaki, S. A., and Harun, Z.: Characteristics of turbulent coherent structures in atmospheric flow under different shear-buoyancy conditions, Bound.-Lay. Meteorol., 173, 115–141, https://doi.org/10.1007/s10546-019-00459-y, 2019.
Luce, H., Kantha, L., and Hashiguchi, H.: Statistical assessment of a Doppler radar model of TKE dissipation rate for low Richardson numbers, Atmos. Meas. Tech., 16, 5091–5101, https://doi.org/10.5194/amt-16-5091-2023, 2023a.
Luce, H., Kantha, L., Hashiguchi, H., Lawrence, D., Doddi, A., Mixa, T., and Yabuki, M.: Turbulence kinetic energy dissipation rate: assessment of radar models from comparisons between 1.3 GHz wind profiler radar (WPR) and DataHawk UAV measurements, Atmos. Meas. Tech., 16, 3561–3580, https://doi.org/10.5194/amt-16-3561-2023, 2023b.
Lv, Y. M., Guo, J. P., Li, J., Cao, L. J., Chen, T. M., Wang, D., Chen, D. D., Han, Y., Guo, X. R., Xu, H., Liu, L., Solanki, R., and Huang, G.: Spatiotemporal characteristics of atmospheric turbulence over China estimated using operational high-resolution soundings, Environ. Res. Lett., 16, 054050, https://doi.org/10.1088/1748-9326/abf461, 2021.
Marquis, J. N., Varble, A. C., Robinson, P., Nelson, T. C., and Friedrich, K.: Low-level mesoscale and cloud-scale interactions promoting deep convection initiation, Mon. Weather Rev., 149, 2473–2495, https://doi.org/10.1175/mwr-d-20-0391.1, 2021.
Muñoz-Esparza, D., Sharman, R. D., and Lundquist, J. K.: Turbulence dissipation rate in the atmospheric boundary layer: observations and WRF mesoscale modeling during the XPIA field campaign, Mon. Weather Rev., 146, 351–371, https://doi.org/10.1175/mwr-d-17-0186.1, 2018.
Namboodiri, K. V. S., Dileep, P. K., Mammen, K., Ramkumar, G., Kumar, N., Sreenivasan, S., Kumar, B. S., and Manchanda, R. K.: Effects of annular solar eclipse of 15 January 2010 on meteorological parameters in the 0 to 65 km region over Thumba, India, Meteorol. Z., 20, 635–647, https://doi.org/10.1127/0941-2948/2011/0253, 2011.
Nicholls, S.: The dynamics of stratocumulus Aircraft observations and comparisons with a mixed layer model, Q. J. Roy. Meteor. Soc., 110, 783–820, https://doi.org/10.1002/qj.49711046603, 1984.
Nowak, J. L., Siebert, H., Szodry, K.-E., and Malinowski, S. P.: Coupled and decoupled stratocumulus-topped boundary layers: turbulence properties, Atmos. Chem. Phys., 21, 10965–10991, https://doi.org/10.5194/acp-21-10965-2021, 2021.
Meng, D. and Guo, J.: A low-level turbulence-related parameters dataset derived from the radar wind profiler and radiosonde in China during 2023, Zenodo [data set], https://doi.org/10.5281/zenodo.14959025, 2025.
Meng, D., Guo, J., Guo, X., Wang, Y., Li, N., Sun, Y., Zhang, Z., Tang, N., Li, H., Zhang, F., Tong, B., Xu, H., and Chen, T.: Elucidating the boundary layer turbulence dissipation rate using high-resolution measurements from a radar wind profiler network over the Tibetan Plateau, Atmos. Chem. Phys., 24, 8703–8720, https://doi.org/10.5194/acp-24-8703-2024, 2024.
Nastrom, G. D.: Doppler radar spectral width broadening due to beamwidth and wind shear, Ann. Geophys., 15, 786–796, https://doi.org/10.1007/s00585-997-0786-7, 1997.
Nastrom, G. D. and Eaton, F. D.: A brief climatology of eddy diffusivities over White Sands Missile Range, New Mexico, J. Geophys. Res.-Atmos., 102, 29819–29826, https://doi.org/10.1029/97jd02208, 1997.
Rajput, A., Singh, N., Singh, J., and Rastogi, S.: Investigation of atmospheric turbulence and scale lengths using radiosonde measurements of GVAX-campaign over central Himalayan region, J. Atmos. Sol.-Terr. Phy., 235, 16, https://doi.org/10.1016/j.jastp.2022.105895, 2022.
Satheesan, K. and Murthy, B. V. K.: Turbulence parameters in the tropical troposphere and lower stratosphere, J. Geophys. Res.-Atmos., 107, Pages ACL 2-1–ACl 2-13, https://doi.org/10.1029/2000jd000146, 2002.
Sato, T. and Woodman, R. F.: Fine altitude resolution observations of stratospheric turbulent layers by the Arecibo 430-MHz radar, J. Atmos. Sci., 39, 2546–2552, https://doi.org/10.1175/1520-0469(1982)039<2546:Faroos>2.0.Co;2, 1982.
Shelekhov, A. P., Afanasiev, A. L., Shelekhova, E. A., Kobzev, A. A., Tel'minov, A. E., Molchunov, A. N., and Poplevina, O. N.: Using small unmanned aerial vehicles for turbulence measurements in the atmosphere, Izv. Atmos. Ocean. Phy+, 57, 533–545, https://doi.org/10.1134/s0001433821050133, 2021.
Solanki, R., Guo, J. P., Lv, Y. M., Zhang, J., Wu, J. Y., Tong, B., and Li, J.: Elucidating the atmospheric boundary layer turbulence by combining UHF radar wind profiler and radiosonde measurements over urban area of Beijing, Urban Clim., 43, 13, https://doi.org/10.1016/j.uclim.2022.101151, 2022.
Sun, Y., Guo J., Chen T., Li N., Guo X., Xu H., Zhang Z., Shi Y., Zeng L., Chen J., and Meng, D.: Long-term high-resolution radiosonde measurements reveal more intensified and frequent turbulence at cruising altitude in China, Geophys. Res. Lett., 52, e2024GL114076, https://doi.org/10.1029/2024GL114076, 2025.
Thorpe, S. A.: Turbulence and mixing in a Scottish Loch, Philos. T. R. Soc. A, 286, 125–181, https://doi.org/10.1098/rsta.1977.0112, 1977.
Weinstock, J.: Vertical turbulent diffusion in a stably stratified fluid, J. Atmos. Sci., 35, 1022–1027, https://doi.org/10.1175/1520-0469(1978)035<1022:Vtdias>2.0.Co;2, 1978.
Weinstock, J.: Using radar to estimate dissipation rates in thin layers of turbulence, Radio Sci., 16, 1401–1406, https://doi.org/10.1029/RS016i006p01401, 1981a.
Weinstock, J.: Vertical turbulence diffusivity for weak or strong stable stratification, J. Geophys. Res.-Oceans, 86, 9925–9928, https://doi.org/10.1029/JC086iC10p09925, 1981b.
Wilson, R.: Turbulent diffusivity in the free atmosphere inferred from MST radar measurements: a review, Ann. Geophys., 22, 3869–3887, https://doi.org/10.5194/angeo-22-3869-2004, 2004.
Wilson, R., Luce, H., Hashiguchi, H., Nishi, N., and Yabuki, Y.: Energetics of persistent turbulent layers underneath mid-level clouds estimated from concurrent radar and radiosonde data, J. Atmos. Sol.-Terr. Phy., 118, 78–89, https://doi.org/10.1016/j.jastp.2014.01.005, 2014.
Wu, J. Y., Guo, J. P., Yun, Y. X., Yang, R. F., Guo, X. R., Meng, D. L., Sun, Y. P., Zhang, Z., Xu, H., and Chen, T. M.: Can ERA5 reanalysis data characterize the pre-storm environment?, Atmos. Res., 297, 107108, https://doi.org/10.1016/j.atmosres.2023.107108, 2024.
Xu, Z. Q., Chen, H. S., Guo, J. P., and Zhang, W. C.: Contrasting effect of soil moisture on the daytime boundary layer under different thermodynamic conditions in summer over China, Geophys. Res. Lett., 48, e2020GL090989, https://doi.org/10.1029/2020gl090989, 2021.
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.
This study provides a high-resolution dataset of low-level atmospheric turbulence across China,...
Altmetrics
Final-revised paper
Preprint