Articles | Volume 23, issue 5
https://doi.org/10.5194/acp-23-3181-2023
© Author(s) 2023. 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-23-3181-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Mar 2023
Research article |
| 10 Mar 2023
| 10 Mar 2023
Estimating hub-height wind speed based on a machine learning algorithm: implications for wind energy assessment
Boming Liu
State Key Laboratory of Information Engineering in Surveying, Mapping
and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China
Xin Ma
State Key Laboratory of Information Engineering in Surveying, Mapping
and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China
State Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, Beijing 100081, China
Hui Li
State Key Laboratory of Information Engineering in Surveying, Mapping
and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China
Shikuan Jin
State Key Laboratory of Information Engineering in Surveying, Mapping
and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China
Yingying Ma
State Key Laboratory of Information Engineering in Surveying, Mapping
and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China
Wei Gong
State Key Laboratory of Information Engineering in Surveying, Mapping
and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-215, https://doi.org/10.5194/essd-2022-215, 2022
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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
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
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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
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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
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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
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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.
Yingying Ma, Yang Zhu, Boming Liu, Hui Li, Shikuan Jin, Yiqun Zhang, Ruonan Fan, and Wei Gong
Atmos. Chem. Phys., 21, 17003–17016, https://doi.org/10.5194/acp-21-17003-2021, https://doi.org/10.5194/acp-21-17003-2021, 2021
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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.
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
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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
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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.
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
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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
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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.
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.
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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.
Wind energy is one of the most essential clean and renewable forms of energy in today’s world....
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