Articles | Volume 21, issue 22
https://doi.org/10.5194/acp-21-17003-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-17003-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Nov 2021
Research article |
| 24 Nov 2021
| 24 Nov 2021
Estimation of the vertical distribution of particle matter (PM2.5) concentration and its transport flux from lidar measurements based on machine learning algorithms
Yingying Ma
State Key Laboratory of Information Engineering in Surveying,
Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China
Yang Zhu
School of Computer Science and Technology, Wuhan University of
Science and Technology, Wuhan, China
Boming Liu
CORRESPONDING AUTHOR
State Key Laboratory of Information Engineering in Surveying,
Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China
Hui Li
State Key Laboratory of Information Engineering in Surveying,
Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China
Shikuan Jin
State Key Laboratory of Information Engineering in Surveying,
Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China
Yiqun Zhang
State Key Laboratory of Information Engineering in Surveying,
Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China
Ruonan Fan
State Key Laboratory of Information Engineering in Surveying,
Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China
Wei Gong
CORRESPONDING AUTHOR
School of Electronic Information, Wuhan University, Wuhan, China
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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.
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Short summary
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Aerosol parameter retrievals have always been a research focus. In this study, we used an advanced aerosol algorithms (GRASP, developed by Oleg Dubovik) to test the ability of DPC/Gaofen-5 (the first polarized multi-angle payload developed in China) images to obtain aerosol parameters. The results show that DPC/GRASP achieves good results (R > 0.9). This research will contribute to the development of hardware and algorithms for aerosols
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X. Xia, Z. Zhu, T. Zhang, G. Wei, and Y. Ji
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 545–550, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-545-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-545-2022, 2022
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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.
Hui Li, Boming Liu, Xin Ma, Shikuan Jin, Yingying Ma, Yuefeng Zhao, and Wei Gong
Atmos. Meas. Tech., 14, 5977–5986, https://doi.org/10.5194/amt-14-5977-2021, https://doi.org/10.5194/amt-14-5977-2021, 2021
Short summary
Short summary
Radiosonde (RS) is widely used to detect the vertical structures of the planetary boundary layer (PBL), and numerous methods have been proposed for retrieving PBL height (PBLH) from RS data. However, an algorithm that is suitable under all atmospheric conditions does not exist. This study evaluates the performance of four common PBLH algorithms under different thermodynamic stability conditions based on RS data.
Xin Lu, Feiyue Mao, Daniel Rosenfeld, Yannian Zhu, Zengxin Pan, and Wei Gong
Atmos. Chem. Phys., 21, 11979–12003, https://doi.org/10.5194/acp-21-11979-2021, https://doi.org/10.5194/acp-21-11979-2021, 2021
Short summary
Short summary
In this paper, a novel method for retrieving cloud base height and geometric thickness is developed and applied to produce a global climatology of boundary layer clouds with a high accuracy. The retrieval is based on the 333 m resolution low-level cloud distribution as obtained from the CALIPSO lidar data. The main part of the study describes the variability of cloud vertical geometrical properties in space, season, and time of the day. Resultant new insights are presented.
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.
Lianfa Lei, Zhenhui Wang, Jiang Qin, Lei Zhu, Rui Chen, Jianping Lu, and Yingying Ma
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-283, https://doi.org/10.5194/amt-2020-283, 2020
Revised manuscript not accepted
Short summary
Short summary
This paper proposes a new method of Multichannel Microwave Radiometer 3-D antenna pattern measurement by observing the sun. The antenna pattern derived from the solar observation was compared with the result of the far-field measurement with a point source in the microwave anechoic chamber at 30 GHz, the maximum error of the beamwidth is less than 0.1°, which showed that this pattern matched well to the pattern measurement using a point source in the microwave anechoic chamber.
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.
B. Chen, S. Shi, W. Gong, J. Sun, B. Chen, K. Guo, L. Du, J. Yang, Q. Xu, and S. Song
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 501–505, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-501-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-501-2020, 2020
B. Wang, S. Song, W. Gong, S. Shi, B. Chen, J. Yang, L. Du, and J. Sun
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 547–551, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-547-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-547-2020, 2020
S. Jin, Y. Ma, W. Gong, and M. Zhang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 807–812, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-807-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-807-2020, 2020
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Short summary
The vertical distribution of the aerosol extinction coefficient (EC) measured by lidar systems has been used to retrieve the profile of particle matter with a diameter of less than 2.5 μm (PM2.5). However, the traditional linear model cannot consider the influence of multiple meteorological variables sufficiently, which then causes low inversion accuracy. In this study, the machine learning algorithms which can input multiple features are used to solve this constraint.
The vertical distribution of the aerosol extinction coefficient (EC) measured by lidar systems...
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