Articles | Volume 19, issue 1
https://doi.org/10.5194/gmd-19-505-2026
© 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/gmd-19-505-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
15 Jan 2026
Model description paper |
| 15 Jan 2026
| 15 Jan 2026
Retrieving atmospheric thermodynamic and hydrometeor profiles using a thermodynamic-constrained Kalman filter 1D-Var framework based on ground-based microwave radiometer
Qi Zhang
Key Open Laboratory of Intelligent Meteorological Observation Technology, China Meteorological Administration, Beijing, 100081, China
Engineering Technology Research and Development Center, China Huayun Meteorological Technology Group Co., Ltd., 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
State Key Laboratory of Severe Weather Meteorological Science and Technology & Specialized Meteorological Support Technology Research Center, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Yu Wu
Hainan Provincial Meteorological Observatory, Hainan, 570203, China
Bin Deng
Jiangxi Weather Modification Center, Nanchang, 330000, China
Junjie Yan
Key Open Laboratory of Intelligent Meteorological Observation Technology, China Meteorological Administration, Beijing, 100081, China
Engineering Technology Research and Development Center, China Huayun Meteorological Technology Group Co., Ltd., Beijing 100081, China
Related authors
No articles found.
Ning Li, Yongguang Zheng, Hui Xu, Tianmeng Chen, Xiaoran Guo, Linxi Zhao, Yuping Sun, Xuewei Cai, Honglong Yang, Liping Zeng, and Jianping Guo
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-866, https://doi.org/10.5194/essd-2026-866, 2026
Preprint under review for ESSD
Short summary
Short summary
To better understand how winds change before rainfall, we created a unique dataset from 28 radar wind profiler stations across China. It captures wind shear every six minutes during the two hours before rainfall. Stronger rainfall is preceded by a rapid increase and vertical shift in shear, while lighter rainfall shows steadier conditions. The data reveal changes missed by widely used weather analyses and may help improve short-term rainfall forecasts.
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
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
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.
Ning Li, Jianping Guo, Xiaoran Guo, Tianmeng Chen, Zhen Zhang, Na Tang, Yifei Wang, Honglong Yang, Yongguang Zheng, and Yongshui Zhou
Atmos. Chem. Phys., 26, 3339–3356, https://doi.org/10.5194/acp-26-3339-2026, https://doi.org/10.5194/acp-26-3339-2026, 2026
Short summary
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.
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.
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.
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.
Cited articles
Adler, B., Wilczak, J. M., Bianco, L., Djalalova, I., Duncan Jr., J. B., and Turner, D. D.: Observational case study of a persistent cold air pool and gap flow in the Columbia River Basin, Journal of Applied Meteorology and Climatology, 60, 1071–1090, https://doi.org/10.1175/JAMC-D-21-0013.1, 2021.
Barrera-Verdejo, M., Crewell, S., Löhnert, U., Orlandi, E., and Di Girolamo, P.: Ground-based lidar and microwave radiometry synergy for high vertical resolution absolute humidity profiling, Atmos. Meas. Tech., 9, 4013–4028, https://doi.org/10.5194/amt-9-4013-2016, 2016.
Bell, A., Martinet, P., Caumont, O., Vié, B., Delanoë, J., Dupont, J.-C., and Borderies, M.: W-band radar observations for fog forecast improvement: an analysis of model and forward operator errors, Atmos. Meas. Tech., 14, 4929–4946, https://doi.org/10.5194/amt-14-4929-2021, 2021a.
Bell, B., Hersbach, H., Simmons, A., Berrisford, P., Dahlgren, P., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Radu, R., Schepers, D., and Soci, C.: The ERA5 global reanalysis: Preliminary extension to 1950, Q. J. Roy. Meteor. Soc., 147, 4186–4227, https://doi.org/10.1002/qj.4174, 2021b.
Benjamin, S. G., James, E. P., Hu, M., Alexander, C. R., Ladwig, T. T., Brown, J. M., Weygandt, S. S., Turner, D. D., Minnis, P., and Smith Jr., W. L.: Stratiform cloud-hydrometeor assimilation for HRRR and RAP model short-range weather prediction, Monthly Weather Review, 149, 2673–2694, https://doi.org/10.1175/MWR-D-20-0385.1, 2021.
Byrd, R. H., Lu, P., and Nocedal, J.: A Limited Memory Algorithm for Bound-Constrained Optimization, SIAM J. Sci. Stat. Comput., 16, 1190–1208, 1995.
Carminati, F.: A channel selection for the assimilation of CrIS and HIRAS instruments at full spectral resolution, Quarterly Journal of the Royal Meteorological Society, 148, 1092–1112, https://doi.org/10.1002/qj.4248, 2022.
Christofilakis, V., Tatsis, G., Chronopoulos, S. K., Sakkas, A., Skrivanos, A. G., Peppas, K. P., Nistazakis, H. E., Baldoumas, G., and Kostarakis, P.: Earth-to-earth microwave rain attenuation measurements: A survey on the recent literature, Symmetry, 12, 1440, https://doi.org/10.3390/sym12091440, 2020.
Cimini, D., Hocking, J., De Angelis, F., Cersosimo, A., Di Paola, F., Gallucci, D., Gentile, S., Geraldi, E., Larosa, S., Nilo, S., Romano, F., Ricciardelli, E., Ripepi, E., Viggiano, M., Luini, L., Riva, C., Marzano, F. S., Martinet, P., Song, Y. Y., Ahn, M. H., and Rosenkranz, P. W.: RTTOV-gb v1.0 – updates on sensors, absorption models, uncertainty, and availability, Geosci. Model Dev., 12, 1833–1845, https://doi.org/10.5194/gmd-12-1833-2019, 2019.
De Angelis, F., Cimini, D., Hocking, J., Martinet, P., and Kneifel, S.: RTTOV-gb – adapting the fast radiative transfer model RTTOV for the assimilation of ground-based microwave radiometer observations, Geosci. Model Dev., 9, 2721–2739, https://doi.org/10.5194/gmd-9-2721-2016, 2016.
de Haan, S. and van der Veen, S. H.: Cloud initialization in the Rapid Update Cycle of HIRLAM, Weather and Forecasting, 29, 1120–1133, https://doi.org/10.1175/WAF-D-13-00071.1, 2014.
Donovan, D. P., Barker, H. W., Hogan, R. J., Wehr, T., Eisinger, M., Lajas, D., Lefebvre, A., and EarthCARE Phase-B ESA/JAXA Mission Advisory Group: Scientific aspects of the Earth Clouds, Aerosols, and Radiation Explorer (EarthCARE) mission, AIP Conf. Proc., 1531, 444–447, https://doi.org/10.1063/1.4804793, 2013.
Ebell, K., Löhnert, U., Päschke, E., Orlandi, E., Schween, J. H., and CrewellS, S.: A 1-D variational retrieval of temperature, humidity, and liquid cloud properties: performance under idealized and real conditions, Journal of Geophysical Research: Atmospheres, 122, 1746–1766, https://doi.org/10.1002/2016JD025945, 2017.
Esri, DeLorme, HERE, TomTom, Intermap, increment P Corp., GEBCO, USGS, FAO, NPS, NRCAN, GeoBase, IGN, Kadaster NL, Ordnance Survey, Esri Japan, METI, Esri China (Hong Kong), swisstopo, MapmyIndia, and the GIS User Community: World Imagery, https://www.arcgis.com/home/item.html?id=10df2279f9684e4a9f6a7f08febac2a9 (last access: 14 January 2026), 2025.
European Space Agency: EarthCARE CPR CLD Level 2A (version AB), European Space Agency [data set], https://doi.org/10.57780/eca-e6471c5, 2025.
Foth, A. and Pospichal, B.: Optimal estimation of water vapour profiles using a combination of Raman lidar and microwave radiometer, Atmos. Meas. Tech., 10, 3325–3344, https://doi.org/10.5194/amt-10-3325-2017, 2017.
Gamage, S. M., Sica, R. J., Martucci, G., and Haefele, A.: A 1D-Var retrieval of relative humidity using the ERA5 dataset for the assimilation of Raman lidar measurements, Journal of Atmospheric and Oceanic Technology, 37, 2051–2064, https://doi.org/10.1175/JTECH-D-19-0170.1, 2020.
Garcia-Carreras, L., Parker, D. J., Taylor, C. M., Reeves, C. E., and Murphy, J. G.: Impact of mesoscale vegetation heterogeneities on the dynamical and thermodynamic properties of the planetary boundary layer, J. Geophys. Res., 115, D03102, https://doi.org/10.1029/2009JD012811, 2010.
Geerts, B., Parsons, D., Ziegler C., Weckwerth, T., Biggerstaff, M., Clark, R., Coniglio, M., Demoz, B., Ferrare, R., Gallus Jr., W., Haghi, K., Hanesiak, J., Klein, P., Knupp, K., Kosiba, K., McFarquhar, G., Moore, J., Nehrir, A., Parker, M., Pinto, J., Rauber, R., Schumacher, R., Turner, D., Wang, Q., Wang, X., Wang Z., and Wurman, J.: The 2015 Plains Elevated Convection At Night field project. Bulletin of the American Meteorological Society, 98, 767–786, https://doi.org/10.1175/BAMS-D-15-00257.1, 2017.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., McNally, A., Miranda, P., Monge-Sanz, B. M., Morcrette, J.-J., Müller, M., Penney, S., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hewison, T. J.: 1D-VAR retrieval of temperature and humidity profiles from a ground-based microwave radiometer, IEEE Trans. Geosci. Remote Sens., 45, 2163–2168, https://doi.org/10.1109/TGRS.2007.898091, 2007.
Hewison, T. J. and Gaffard, C.: 1D-Var retrieval of temperature and humidity profiles from ground-based microwave radiometers, 2006 IEEE MicroRad, San Juan, Puerto Rico, USA, 28 February–3 March 2006, 235–240, https://doi.org/10.1109/MICRAD.2006.1677095, 2006.
Hoffmann, L., Günther, G., Li, D., Stein, O., Wu, X., Griessbach, S., Heng, Y., Konopka, P., Müller, R., Vogel, B., and Wright, J. S.: From ERA-Interim to ERA5: the considerable impact of ECMWF's next-generation reanalysis on Lagrangian transport simulations, Atmos. Chem. Phys., 19, 3097–3124, https://doi.org/10.5194/acp-19-3097-2019, 2019.
Hong, S.-Y., Dudhia, J., and Chen, S.-H.: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation, Mon. Weather Rev., 132, 103–120, https://doi.org/10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2, 2004.
Hu, J., N. Yussouf, D. D. Turner, T. A. Jones, and X. Wang: Impact of ground-based remote sensing boundary layer observations on short-term probabilistic forecasts of a tornadic supercell event, Weather and Forecasting, 34, 1453–1476, https://doi.org/10.1175/WAF-D-18-0200.1, 2019.
Hélière, A., Wallace, K., Pereira Do Carmo, J., and Lefebvre, A.: Earth cloud, aerosol, and radiation explorer optical payload development status, Proc. SPIE, Sensors, Systems, and Next-Generation Satellites XXI, 10423, 8–25, https://doi.org/10.1117/12.2274704, 2017.
Imura, Y., Tomita, E., Nio, T., Okada, K., Maruyama, K., Nakatsuka, H., Tomiyama, N., Aida, Y., Haze, K., Ochiai, S., Konoue,K., Kubota, T., Tanaka, T., Muto, M., Aoki, S., Horie, H., Ohno, Y., and Sato, K.: EarthCARE/CPR current conditions and preliminary results from scientific views, Proc. SPIE, Remote Sensing of the Atmosphere, Clouds, and Precipitation VIII, 13262, 1326202, https://doi.org/10.1117/12.3045833, 2025.
Kimura, T., Kondo, K., Kumagai, H., Kuroiwa, H., Ishida, C., Oki, R., and Nakajima, T.: EarthCARE – Earth Clouds, Aerosol, and Radiation Explorer: its objectives and Japanese sensor designs, Proc. SPIE, Remote Sensing of Clouds and the Atmosphere VII, 4882, 510–519, https://doi.org/10.1117/12.469725, 2003.
Kummerow, C., Olson, W. S., and Giglio, L.: A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors, IEEE Trans. Geosci. Remote Sens., 34, 1213–1232, https://doi.org/10.1109/36.536538, 2022.
Larosa, S., Cimini, D., Gallucci, D., Nilo, S. T., and Romano, F.: PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations, Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024, 2024.
Li, Q., Wei, M., Wang, Z., and Chu, Y.: Evaluation and improvement of the quality of ground-based microwave radiometer clear-sky data, Atmosphere, 12, 435, https://doi.org/10.3390/atmos12040435, 2021.
Liu, D. and Nocedal, C. J.: On the Limited Memory Method for Large Scale Optimization, Math. Program., 45B, 503–528, 1989.
Löhnert, U. and Maier, O.: Operational profiling of temperature using ground-based microwave radiometry at Payerne: prospects and challenges, Atmos. Meas. Tech., 5, 1121–1134, https://doi.org/10.5194/amt-5-1121-2012, 2012.
Martinet, P., Cimini, D., De Angelis, F., Canut, G., Unger, V., Guillot, R., Tzanos, D., and Paci, A.: Combining ground-based microwave radiometer and the AROME convective scale model through 1DVAR retrievals in complex terrain: an Alpine valley case study, Atmos. Meas. Tech., 10, 3385–3402, https://doi.org/10.5194/amt-10-3385-2017, 2017.
Mason, S. L., Barker, H. W., Cole, J. N. S., Docter, N., Donovan, D. P., Hogan, R. J., Hünerbein, A., Kollias, P., Puigdomènech Treserras, B., Qu, Z., Wandinger, U., and van Zadelhoff, G.-J.: An intercomparison of EarthCARE cloud, aerosol, and precipitation retrieval products, Atmos. Meas. Tech., 17, 875–898, https://doi.org/10.5194/amt-17-875-2024, 2024.
Mroz, K., Treserras, B. P., Battaglia, A., Kollias, P., Tatarevic, A., and Tridon, F.: Cloud and precipitation microphysical retrievals from the EarthCARE Cloud Profiling Radar: the C-CLD product, Atmos. Meas. Tech., 16, 2865–2888, https://doi.org/10.5194/amt-16-2865-2023, 2023.
Noh, Y., Huang, H., and Goldberg, M. D.: Refinement of CrIS channel selection for global data assimilation and its impact on the global weather forecast, Weather and Forecasting, 36, 1405–1429, https://doi.org/10.1175/WAF-D-21-0002.1, 2021.
Que, L. J., Que, W. L., and Feng, J. M.: Intercomparison of different physics schemes in the WRF model over the Asian summer monsoon region, Atmos. Oceanic Sci. Lett., 9, 169–177, https://doi.org/10.1080/16742834.2016.1158618, 2016.
Rodgers, C. D.: Inverse methods for atmospheric sounding: Theory and practice. Vol. 2. World Scientific, ISBN 981022740X, 2000.
Taylor, C. M., Parker, D. J., and Harris, P. P.: An observational case study of mesoscale atmospheric circulations induced by soil moisture, Geophys. Res. Lett., 34, L15801, https://doi.org/10.1029/2007GL030572, 2007.
Teixeira, J., Piepmeier, J. R., Nehrir, A. R., Ao, C. O., Chen, S. S., Clayson, C. A., Fridlind, A. M., Lebsock, M., McCarty, W., Salmun, H., Santanello, J. A., Turner, D. D., Wang, Z., and Zeng, X.: Toward a global planetary boundary layer observing system: A summary, Bulletin of the American Meteorological Society, https://doi.org/10.1175/BAMS-D-23-0228.1, 2025.
Thelen, J.-C., Havemann, S., Newman, S. M. and Taylor, J. P.: Hyperspectral retrieval of land surface emissivities using ARIES, Q. J. Roy. Meteor. Soc., 135, 2110–2124, https://doi.org/10.1002/qj.520, 2009.
Turner, D. D. and Löhnert, U.: Ground-based temperature and humidity profiling: combining active and passive remote sensors, Atmos. Meas. Tech., 14, 3033–3048, https://doi.org/10.5194/amt-14-3033-2021, 2021.
U.S. Committee on Extension to the Standard Atmosphere: U.S. Standard Atmosphere, 1976, NOAA–S/T 76-1562, National Oceanic and Atmospheric Administration, Washington, D.C., USA, https://www.ngdc.noaa.gov/stp/space-weather/online-publications/miscellaneous/us-standard-atmosphere-1976/us-standard-atmosphere_st76-1562_noaa.pdf (last access: 14 January 2026), 1976.
Viggiano, M., Cimini, D., De Natale, M. P., Di Paola, F., Gallucci, D., Larosa, S., and Romano, F.: Combining passive infrared and microwave satellite observations to investigate cloud microphysical properties: A review, Remote Sens., 17, 337, https://doi.org/10.3390/rs17020337, 2025.
Wagner, T. J., Klein, P. M., and Turner, D. D.: A new generation of ground-based mobile platforms for active and passive profiling of the boundary layer, Bulletin of the American Meteorological Society, 100, 137–153, https://doi.org/10.1175/BAMS-D-17-0165.1, 2019.
Wang, K.-N., Ao, C. O., Morris, M. G., Hajj, G. A., Kurowski, M. J., Turk, F. J., and Moore, A. W.: Joint 1DVar retrievals of tropospheric temperature and water vapor from Global Navigation Satellite System radio occultation (GNSS-RO) and microwave radiometer observations, Atmos. Meas. Tech., 17, 583–599, https://doi.org/10.5194/amt-17-583-2024, 2024.
Weisz, E., Smith Sr., W. L., and Smith, N.: Advances in simultaneous atmospheric profile and cloud parameter regression-based retrieval from high-spectral resolution radiance measurements, Journal of Geophysical Research: Atmospheres, 118, 6433–6443, https://doi.org/10.1002/jgrd.50521, 2013.
Welch, G. and Bishop, G.: An Introduction to the Kalman Filter, TR 95-041, Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, https://www.cs.unc.edu/~welch/media/pdf/kalman_intro.pdf (last access: 14 January 2026), 2006.
World Meteorological Organization (WMO): WMO Technical Document No. 1390, World Meteorological Organization, Geneva, Switzerland, https://library.wmo.int/viewer/40422/?offset=#page=1&viewer=picture&o=bookmark&n=0&q= (last access: 14 January 2026), 2007.
Wulfmeyer, V., Hardesty, R. M., Turner, D. D., Behrendt, A., Cadeddu, M., Di Girolamo, P., Schluessel, P., van Baelen, J., and Zus, F.: A review of the remote sensing of lower-tropospheric thermodynamic profiles and its indispensable role for the understanding and simulation of water and energy cycles, Reviews of Geophysics, 53, 819–895, https://doi.org/10.1002/2014RG000476, 2015.
Xu, G.: A review of remote sensing of atmospheric profiles and cloud properties by ground-based microwave radiometers in central China, Remote Sensing, 16, 966, https://doi.org/10.3390/rs16060966, 2024.
Yao, L., Shen, D., Sun, X., Wang, D., Cao, X., Wang, J., Wang, D., Zhang, C., and Guo, Q.: The Beidou Navigation Radiosonde Observation Experiment and Data Evaluation, SSRN [preprint], https://doi.org/10.2139/ssrn.5085235, 2025.
Zhang, L., Ma, Y., Lei, L., Wang, Y., Jin, S., and Gong, W.: Improving atmospheric temperature and relative humidity profiles retrieval based on ground-based multichannel microwave radiometer and millimeter-wave cloud radar, Atmosphere, 15, 1064, https://doi.org/10.3390/atmos15091064, 2024.
Zhang, Q.: Input Data for TCKF1D-Var framework, Zenodo [data set], https://doi.org/10.5281/zenodo.17296305, 2025a.
Zhang, Q.: TCKF1D-Var framework, Zenodo [code], https://doi.org/10.5281/zenodo.17293102, 2025b.
Zhang, Q.: TCKF1D-Var thermodynamic and hydrometeor profile dataset, Zenodo [data set], https://doi.org/10.5281/zenodo.17083973, 2025c.
Zhang, Q., Smith Sr., W., and Shao, M.: The potential of monitoring carbon dioxide emission in a geostationary view with the GIIRS meteorological hyperspectral infrared sounder, Remote Sensing, 15, 886, https://doi.org/10.3390/rs15040886, 2023.
Zhang, Q., Deng, B., Wang, S., Dong, F., and Shao, M.: Multi-Source Retrieval of Thermodynamic Profiles from an Integrated Ground-Based Remote Sensing System Using an EnKF1D-Var Framework, Remote Sensing, 17, 3133, https://doi.org/10.3390/rs17183133, 2025.
Zhang, Y., Chen, S., Tan, W., Chen, S., Chen, H., Guo, P., Sun, Z., Hu, R., Xu, Q., Zhang, M., Hao, W., and Bu, Z.: Retrieval of Water Cloud Optical and Microphysical Properties from Combined Multiwavelength Lidar and Radar Data, Remote Sens., 13, 4396, https://doi.org/10.3390/rs13214396, 2021.
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.
We propose TCKF1D-Var, a thermodynamic-constrained variational framework for ground-based...
Altmetrics
Final-revised paper
Preprint