Articles | Volume 14, issue 2
https://doi.org/10.5194/essd-14-907-2022
© Author(s) 2022. 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-14-907-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
24 Feb 2022
Data description paper |
| 24 Feb 2022
| 24 Feb 2022
LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion
Key Laboratory of Geographic Information Science (Ministry of
Education), School of Geographic Sciences, East China Normal University,
Shanghai 200241, China
Institute of Eco-Chongming, 20 Cuiniao Rd., Chongming, Shanghai
202162, China
Ke Li
Key Laboratory of Geographic Information Science (Ministry of
Education), School of Geographic Sciences, East China Normal University,
Shanghai 200241, China
Mingliang Ma
School of Surveying and Geo-Informatics, Shandong Jianzhu University,
Jinan 250101, China
Kaitao Li
State Environmental Protection Key Laboratory of Satellite Remote
Sensing, Aerospace Information Research Institute, Chinese Academy of
Sciences, Beijing 100101, China
Zhengqiang Li
State Environmental Protection Key Laboratory of Satellite Remote
Sensing, Aerospace Information Research Institute, Chinese Academy of
Sciences, Beijing 100101, China
State Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, Beijing 100081, China
Ni-Bin Chang
Department of Civil, Environmental, and Construction Engineering,
University of Central Florida, Orlando, FL 32816, USA
Zhuo Tan
Key Laboratory of Geographic Information Science (Ministry of
Education), School of Geographic Sciences, East China Normal University,
Shanghai 200241, China
Di Han
Key Laboratory of Geographic Information Science (Ministry of
Education), School of Geographic Sciences, East China Normal University,
Shanghai 200241, China
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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.
Cheng Fan, Zhengqiang Li, Ying Li, Jiantao Dong, Ronald van der A, and Gerrit de Leeuw
Atmos. Chem. Phys., 21, 7723–7748, https://doi.org/10.5194/acp-21-7723-2021, https://doi.org/10.5194/acp-21-7723-2021, 2021
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Emission control policy in China has resulted in the decrease of nitrogen dioxide concentrations, which however leveled off and stabilized in recent years, as shown from satellite data. The effects of the further emission reduction during the COVID-19 lockdown in 2020 resulted in an initial improvement of air quality, which, however, was offset by chemical and meteorological effects. The study shows the regional dependence over east China, and results have a wider application than China only.
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
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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.
Wenyuan Chang, Ying Zhang, Zhengqiang Li, Jie Chen, and Kaitao Li
Atmos. Chem. Phys., 21, 4403–4430, https://doi.org/10.5194/acp-21-4403-2021, https://doi.org/10.5194/acp-21-4403-2021, 2021
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Aerosol simulation in WRF-Chem often uses the MOSAIC aerosol mechanism. Still, we need variational data assimilation (DA) for the MOSAIC aerosols to blend aerosol optical measurements. This study provides a developed GSI variational DA system, with a tangent linear operator designed for multi-source and multi-wavelength aerosol optical measurements. We successfully applied the DA system in an aerosol field campaign to assimilate aerosol optical data in northwestern China.
Yang Zhang, Zhengqiang Li, Zhihong Liu, Yongqian Wang, Lili Qie, Yisong Xie, Weizhen Hou, and Lu Leng
Atmos. Meas. Tech., 14, 1655–1672, https://doi.org/10.5194/amt-14-1655-2021, https://doi.org/10.5194/amt-14-1655-2021, 2021
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The aerosol fine-mode fraction (FMF) is an important parameter reflecting the content of man-made aerosols. This study carried out the retrieval of FMF in China based on multi-angle polarization data and validated the results. The results of this study can contribute to the FMF retrieval algorithm of multi-angle polarization sensors. At the same time, a high-precision FMF dataset of China was obtained, which can provide basic data for atmospheric environment research.
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
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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.
Yuan Gao, Lili Yao, Ni-Bin Chang, and Dingbao Wang
Hydrol. Earth Syst. Sci., 25, 945–956, https://doi.org/10.5194/hess-25-945-2021, https://doi.org/10.5194/hess-25-945-2021, 2021
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Mean annual runoff prediction is of great interest but still poses a challenge in ungauged basins. The purpose of this study is to diagnose the data requirement for predicting mean annual runoff in ungauged basins based on a water balance model, in which the effects of climate variability are explicitly represented. The performance of predicting mean annual runoff can be improved by employing better estimation of soil water storage capacity including the effects of soil, topography, and bedrock.
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
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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
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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.
Qiaoyun Hu, Haofei Wang, Philippe Goloub, Zhengqiang Li, Igor Veselovskii, Thierry Podvin, Kaitao Li, and Mikhail Korenskiy
Atmos. Chem. Phys., 20, 13817–13834, https://doi.org/10.5194/acp-20-13817-2020, https://doi.org/10.5194/acp-20-13817-2020, 2020
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This study presents the characteristics of Taklamakan dust particles derived from lidar measurements collected in the dust aerosol observation field campaign. It provides comprehensive parameters for Taklamakan dust properties and vertical distributions of Taklamakan dust. This paper also points out the importance of polluted dust which was frequently observed in the field campaign. The results contribute to improving knowledge about dust and reducing uncertainties in the climatic model.
Ying Zhang, Zhengqiang Li, Yu Chen, Gerrit de Leeuw, Chi Zhang, Yisong Xie, and Kaitao Li
Atmos. Chem. Phys., 20, 12795–12811, https://doi.org/10.5194/acp-20-12795-2020, https://doi.org/10.5194/acp-20-12795-2020, 2020
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Observation of atmospheric aerosol components plays an important role in reducing uncertainty in climate assessment. In this study, an improved remote sensing method which can better distinguish scattering components is developed, and the aerosol components in the atmospheric column over China are retrieved based on the Sun–sky radiometer Observation NETwork (SONET). The component distribution shows there could be a sea salt component in northwest China from a paleomarine source in desert land.
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
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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
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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.
Li Li, Zhengqiang Li, Wenyuan Chang, Yang Ou, Philippe Goloub, Chengzhe Li, Kaitao Li, Qiaoyun Hu, Jianping Wang, and Manfred Wendisch
Atmos. Chem. Phys., 20, 10845–10864, https://doi.org/10.5194/acp-20-10845-2020, https://doi.org/10.5194/acp-20-10845-2020, 2020
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Dust Aerosol Observation-Kashi (DAO-K) campaign was conducted near the Taklimakan Desert in April 2019 to obtain comprehensive aerosol, atmosphere, and surface parameters. Estimations of aerosol solar radiative forcing by a radiative transfer (RT) model were improved based on the measured aerosol parameters, additionally considering atmospheric profiles and diurnal variations of surface albedo. RT simulations agree well with simultaneous irradiance observations, even in dust-polluted conditions.
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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.
The Long-term Gap-free High-resolution Air Pollutant concentration dataset, providing gap-free...
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