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.
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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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
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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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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
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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.
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.
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.
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.
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
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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.
Cited articles
Abbes, M. and Belhadj, J.: Wind resource estimation and wind park design in
El-Kef region, Tunisia. Energy, 40, 348–357, https://doi.org/10.1016/j.energy.2012.01.061, 2012.
Akpinar, E. K. and Akpinar, S.: An assessment on seasonal analysis of wind energy
characteristics and wind turbine characteristics, Energy Convers. Manage.,
46, 1848–1867, https://doi.org/10.1016/j.enconman.2004.08.012, 2005.
Ali, S., Lee, S. M., and Jang, C. M.: Statistical analysis of wind
characteristics using Weibull and Rayleigh distributions in Deokjeok-do
Island–Incheon, South Korea, Renew. Energ., 123, 652–663, https://doi.org/10.1016/j.renene.2018.02.087, 2018.
Allabakash, S., Lim, S., Yasodha, P., Kim, H., and Lee, G.: Intermittent
clutter suppression method based on adaptive harmonic wavelet transform for
L-band radar wind profiler, IEEE T. Geosci. Remote, 57, 8546–8556, 2019.
Banuelos-Ruedas, F., Angeles-Camacho, C., and Rios-Marcuello, S.: Analysis and
validation of the methodology used in the extrapolation of wind speed data
at different heights, Renew. Sustain. Energy Rev., 14, 2383–2391,
https://doi.org/10.1016/j.rser.2010.05.001, 2010.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001.
Brümmer, B.: Wind shear at tilted inversions, Bound.-Lay. Meteorol.
57, 295–308, 1991.
Chang, T. P.: Performance comparison of six numerical methods in estimating
Weibull parameters for wind energy application, Appl. Energ., 88,
272–282, https://doi.org/10.1016/j.apenergy.2010.06.018, 2011.
Chen, B., Tan, J., Wang, W., Dai, W., Ao, M., and Chen, C.: Tomographic
Reconstruction of Water Vapor Density Fields From the Integration of GNSS
Observations and Fengyun-4A Products, IEEE T. Geosci.
Remote, 61, 1–12, https://doi.org/10.1109/TGRS.2023.3239392, 2023.
Chi, Z., Haikun, W., Tingting, Z., Kanjian, Z., and Tianhong, L.: Comparison of two
multi-step ahead forecasting mechanisms for wind speed based on machine
learning models, in: 2015 34th Chinese control Conference (CCC), IEEE, Hangzhou, China, 28–30 July 2015, 8183–8187, 2015.
Coleman, T. A., Knupp K. R., and Pangle P. T.: The effects of heterogeneous
surface roughness on boundary-layer kinematics and wind shear, Electronic J.
Severe Storms Meteor., 16, 1–29, 2021.
De Arruda Moreira, G., Sánchez-Hernández, G., Guerrero-Rascado, J.
L., Cazorla, A., and Alados-Arboledas, L.: Estimating the urban atmospheric
boundary layer height from remote sensing applying machine learning
techniques, Atmos. Res., 266, 105962, https://doi.org/10.1016/j.atmosres.2021.105962, 2022.
Debnath, M., Doubrawa, P., Optis, M., Hawbecker, P., and Bodini, N.: Extreme wind shear events in US offshore wind energy areas and the role of induced stratification, Wind Energ. Sci., 6, 1043–1059, https://doi.org/10.5194/wes-6-1043-2021, 2021.
Durisic, Z. and Mikulovic, J.: Assessment of the wind energy resource in the
South Banat region, Serbia, Renew. Sust. Energ. Rev., 16, 3014–3023,
https://doi.org/10.1016/j.rser.2012.02.026, 2012.
ECMWF: ERA5 hourly data on single levels from 1959 to present, ECMWF [data set], https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview (last access: 7 March 2023), 2023.
Fagbenle, R. O., Katende, J., Ajayi, O. O., and Okeniyi, J. O.: Assessment of wind
energy potential of two sites in North-East, Nigeria, Renew Energ.,
36, 1277–1283, https://doi.org/10.1016/j.renene.2010.10.003,
2011.
Gaertner, E., Rinker, J., Sethuraman, L., Zahle, F., Anderson, B., Barter,
G., Abbas, N., Meng, F., Bortolotti, P., Skrzypiński, W. R., Scott, G.,
Feil, R., Bredmose, H., Dykes, K., Shields, M., Allen, C., and Viselli, A.:
Definition of the IEA 15-Megawatt Offshore Reference Wind Turbine, Golden,
CO: National Renewable Energy Laboratory, NREL/TP-5000-75698,
https://www.nrel.gov/docs/fy20osti/75698.pdf (last access: 15 November 2022), 2020.
Gryning, S. E., Batchvarova, E., Brümmer, B., Jrgensen, H., and Larsen,
S.: On the extension of the wind profile over homogeneous terrain beyond the
surface boundary layer, Bound.-Lay. Meteorol., 124, 251–268, 2007.
Gualtieri, G.: Reliability of era5 reanalysis data for wind resource
assessment: a comparison against tall towers, Energies, 14, 4169, https://doi.org/10.3390/en14144169, 2021.
Guo, J., Chen, X., Su, T., Liu, L., Zheng, Y., Chen, D., Li, J., Xu, H., Lv, Y., He,
B., Li, Y., Hu, X., Ding, A., and Zhai, P.: The climatology of lower
tropospheric temperature inversions in China from radiosonde measurements:
roles of black carbon, local meteorology, and large-scale subsidence,
J. Climate, 33, 9327–9350, https://doi.org/10.1175/JCLI-D-19-0278.1,
2020.
Guo, J., Liu, B., Gong, W., Shi, L., Zhang, Y., Ma, Y., Zhang, J., Chen, T., Bai, K., Stoffelen, A., de Leeuw, G., and Xu, X.: Technical note: First comparison of wind observations from ESA's satellite mission Aeolus and ground-based radar wind profiler network of China, Atmos. Chem. Phys., 21, 2945–2958, https://doi.org/10.5194/acp-21-2945-2021, 2021a.
Guo, J., Zhang, J., Yang, K., Liao, H., Zhang, S., Huang, K., Lv, Y., Shao, J., Yu, T., Tong, B., Li, J., Su, T., Yim, S. H. L., Stoffelen, A., Zhai, P., and Xu, X.: Investigation of near-global daytime boundary layer height using high-resolution radiosondes: first results and comparison with ERA5, MERRA-2, JRA-55, and NCEP-2 reanalyses, Atmos. Chem. Phys., 21, 17079–17097, https://doi.org/10.5194/acp-21-17079-2021, 2021b.
Hellmann, G.: Über die Bewegung der Luft in den untersten Schichten der
Atmosphare: Kgl. Akademie der Wissenschaften, Reimer, 1914.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horanyi, A., and Munoz-Sabater,
J.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc.,
146, 1999–2049, 2020.
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, L. X. and Moller, B.: Feasibility study of China's offshore wind target
by 2020, Energy, 48, 268–277,
https://doi.org/10.1016/j.energy.2012.03.016, 2012.
Jung, C. and Schindler, D.: Development of a statistical bivariate wind speed-wind
shear model (WSWS) to quantify the height-dependent wind resource, Energ.
Convers. Manag., 149, 303–317, 2017.
Jung, C. and Schindler, D.: The role of the power law exponent in wind
energy assessment: A global analysis, Int. J. Energ.
Res., 45.6, 8484–8496, 2021.
Khatib, H.: IEA World Energy Outlook 2011-A comment, Energ. Policy,
48, 737–743, 2012.
Lahouar, A. and Slama, J. B. H.: Hour-ahead wind power forecast based on random
forests, Renew. Energy, 109, 529–541, 2017.
Laurila, T. K., Sinclair, V. A., and Gregow, H.: Climatology, variability,
and trends in near-surface wind speeds over the North Atlantic and Europe
during 1979–2018 based on ERA5, Int. J. Climatol.,
41, 2253–2278, 2021.
Leung, D. Y. C. and Yang, Y.: Wind energy development and its environmental
impact: A review, Renew. Sust. Energ. Rev., 16, 1031–1039, https://doi.org/10.1016/j.rser.2011.09.024, 2012.
Li, J., Guo, J., Xu, H., Li, J., and Lv, Y.: Assessing the surface-layer
stability over china using long-term wind-tower network observations,
Bound.-Lay. Meteorol., 180, 155–171, 2021.
Li, J. L. and Yu, X.: Onshore and offshore wind energy potential assessment
near Lake Erie shoreline: A spatial and temporal analysis, Energy, 147,
1092–1107, https://doi.org/10.1016/j.energy.2018.01.118, 2018.
Li, Y., Huang, X., Tee, K. F., Li, Q., and Wu, X. P.: Comparative study of
onshore and offshore wind characteristics and wind energy potentials: A case
study for southeast coastal region of China, Sustainable Energy Technologies
and Assessments, 39, 100711, https://doi.org/10.1016/j.seta.2020.100711, 2020.
Liu, B., Ma, Y., Guo, J., Gong, W., Zhang, Y., Mao, F., Li, J., Guo, X., and
Shi, Y.: Boundary layer heights as derived from ground-based Radar wind
profiler in Beijing, IEEE Tr. Geosci. Remote, 57, 8095–8104,
https://doi.org/10.1109/TGRS.2019.2918301, 2019.
Liu, B., Guo, J., Gong, W., Shi, L., Zhang, Y., and Ma, Y.: Characteristics and performance of wind profiles as observed by the radar wind profiler network of China, Atmos. Meas. Tech., 13, 4589–4600, https://doi.org/10.5194/amt-13-4589-2020, 2020.
Liu, B., Ma, X., Ma, Y., Li, H., Jin, S., Fan, R., and Gong, W.: The
relationship between atmospheric boundary layer and temperature inversion
layer and their aerosol capture capabilities, Atmos. Res., 271, 106121,
https://doi.org/10.1016/j.atmosres.2022.106121, 2022.
Liu, F., Sun, F., Liu, W., Wang, T., Wang, H., Wang, X., and Lim, W. H.: On
wind speed pattern and energy potential in China, Appl. Energ., 236,
867–876, 2019.
Liu, J., Gao, C. Y., Ren, J., Gao, Z., Liang, H., and Wang, L.: Wind
resource potential assessment using a long term tower measurement approach:
A case study of Beijing in China, Journal of Cleaner Production, 174,
917–926, 2018.
Liu, R., Liu, S., Yang, X., Lu, H., Pan, X., and Xu, Z.: Wind dynamics over a
highly heterogeneous oasis area: An experimental and numerical
study, J. Geophys. Res.-Atmos., 123, 8418–8440, https://doi.org/10.1029/2018JD028397, 2018.
Liu, Y., Xiao, L. Y., Wang, H. F., Dai, S. T., and Qi, Z. P.: Analysis on the
hourly spatiotemporal complementarities between China's solar and wind
energy resources spreading in a wide area, Sci. China. Technol. Sc., 56,
683–692, https://doi.org/10.1007/s11431-012-5105-1, 2013.
Lolli, S.: Is the air too polluted for outdoor activities? Check by using
your photovoltaic system as an air-quality monitoring device, Sensors,
21, 6342, https://doi.org/10.3390/s21196342, 2021.
Lolli, S., Sauvage, L., Loaec, S., and Lardier, M.: EZ Lidar™: A new compact autonomous eye-safe scanning aerosol Lidar for extinction
measurements and PBL height detection. Validation of the performances
against other instruments and intercomparison campaigns, Optica pura y
Aplicada, 44, 33–41, 2011.
Luo, B., Yang, J., Song, S., Shi, S., Gong, W., Wang, A., and Du, L.: Target
Classification of Similar Spatial Characteristics in Complex Urban Areas by
Using Multispectral LiDAR, Remote Sens., 14, 238,
https://doi.org/10.3390/rs14010238, 2022.
Ma, Y., Zhu, Y., Liu, B., Li, H., Jin, S., Zhang, Y., Fan, R., and Gong, W.: Estimation of the vertical distribution of particle matter (PM2.5) concentration and its transport flux from lidar measurements based on machine learning algorithms, Atmos. Chem. Phys., 21, 17003–17016, https://doi.org/10.5194/acp-21-17003-2021, 2021.
Magazzino, C., Mele, M., and Schneider, N.: A machine learning approach on
the relationship among solar and wind energy production, coal consumption,
GDP, and CO2 emissions, Renew. Energ., 167, 99–115, 2021.
May, P. T. and Strauch, R. G.: Reducing the effect of ground clutter on
wind profiler velocity measurements, J. Atmos. Ocean.
Tech., 15, 579–586, 1998.
Mo, H. M., Hong, H. P., and Fan, F.: Estimating the extreme wind speed for
regions in China using surface wind observations and reanalysis data,
Jo. Wind Eng. Ind. Aerod., 143, 19–33, 2015.
National Meteorological Science Data Center: Surface meteorological observation data, China Meteorological Administration [data set], http://www.nmic.cn/data/cdcdetail/dataCode/A.0012.0001.html (last access: 7 March 2023), 2023a.
National Meteorological Science Data Center: Radiosonde observation data, China Meteorological Administration [data set], http://www.nmic.cn/data/cdcdetail/dataCode/B.0011.0001C.html (last access: 7 March 2023), 2023b.
Oh, K. Y., Kim, J. Y., Lee, J. K., Ryu, M. S., and Lee, J. S.: An assessment
of wind energy potential at the demonstration offshore wind farm in Korea,
Energy, 46, 555–563, 2012.
Patel, M. R.: Wind and solar power systems: design, analysis, and operation,
CRC press, https://doi.org/10.1201/9781420039924-9, 2005.
Pérez, I. A., García, M. A., Sánchez, M. L., and De Torre, B.:
Analysis and parameterisation of wind profiles in the low
atmosphere, Solar Energy, 78, 809–821, 2005.
Pishgar-Komleh, S. H., Keyhani, A., and Sefeedpari, P.: Wind speed and power
density analysis based on Weibull and Rayleigh distributions a case study:
Firouzkooh county of Iran, Renew. Sust. Energ. Rev., 42, 313–322,
https://doi.org/10.1016/j.rser.2014.10.028, 2015.
Rocha, P. A. C., de Sousa, R. C., de Andrade, C. F., and da Silva, M. E. V.:
Comparison of seven numerical methods for determining Weibull parameters for
wind energy generation in the northeast region of Brazil, Appl. Energ.,
89, 395–400, 2012.
Saleh, H., Aly, A. A., and Abdel-Hady, S.: Assessment of different methods used to
estimate Weibull distribution parameters for wind speed in Zafarana wind
farm, Suez Gulf, Egypt, Energy, 44, 710–719,
https://doi.org/10.1016/j.energy.2012.05.021, 2012.
Sen, Z., Altunkaynak, A., and Erdik, T.: Wind velocity vertical extrapolation by
extended power law, Adv Meteorol., 2012, 178623, https://doi.org/10.1155/2012/178623, 2012.
Shi, T., Han, G., Ma, X., Mao, H., Chen, C., Han, Z., and Gong, W.:
Quantifying factory-scale CO2/CH4 emission based on mobile measurements and
EMISSION-PARTITION model: cases in China, Environ. Res. Lett., 18, 034028,
https://doi.org/10.1088/1748-9326/acbce7,
2023.
Solanki, R., Guo, J., Lv, Y., Zhang, J., Wu, J., 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
Climate, 43, 101151, https://doi.org/10.1016/j.uclim.2022.101151, 2022.
Stull, R. B.: An Introduction to Boundary Layer Meteorology, Kluwer Academic
Publishers, Dordrecht, https://doi.org/10.1007/978-94-009-3027-8, 1988.
Su, X., Wang, L., Gui, X., Yang, L., Li, L., Zhang, M., and Wang, L.:
Retrieval of total and fine mode aerosol optical depth by an improved MODIS
Dark Target algorithm, Environ. Int., 166, 107343, https://doi.org/10.1016/j.envint.2022.107343, 2022a.
Su, X., Wei, Y., Wang, L., Zhang, M., Jiang, D., and Feng, L.: Accuracy,
stability, and continuity of AVHRR, SeaWiFS, MODIS, and VIIRS deep blue
long-term land aerosol retrieval in Asia, Sci. Total Environ.,
832, 155048, https://doi.org/10.1016/j.scitotenv.2022.155048, 2022b.
Tieleman, H. W.: Wind characteristics in the surface layer over
heterogeneous terrain, J. Wind Eng. Ind. Aerod., 41, 329–340, 1992.
Veers, P., Dykes, K., Lantz, E., Barth, S., Bottasso, C. L., Carlson, O.,
and Wiser, R.: Grand challenges in the science of wind energy, Science,
366, eaau2027, https://doi.org/10.3389/fenrg.2020.624646, 2019.
Yu, L., Zhong, S., Bian, X., and Heilman, W. E.: Climatology and trend of
wind power resources in China and its surrounding regions: A revisit using
Climate Forecast System Reanalysis data, Int. J.
Climatol., 36, 2173–2188, 2016.
Yu, S. and Vautard, R.: A transfer method to estimate hub-height wind
speed from 10 meters wind speed based on machine learning, Renew.
Sustain. Energ. Rev., 169, 112897, https://doi.org/10.1016/j.rser.2022.112897, 2022.
Yuan, J.: Wind energy in China: Estimating the potential, Nat. Energ.,
1, 1–2, 2016.
Zhang, J., Zhang, M., Li, Y., Qin, J., Wei, K., and Song, L.: Analysis of
wind characteristics and wind energy potential in complex mountainous region
in southwest China, J. Clean. Prod., 274, 123036, https://doi.org/10.1016/j.jclepro.2020.123036, 2020.
Zheng, Z., Zhao, C., Lolli, S., Wang, X., Wang, Y., Ma, X., and Yang, Y.:
Diurnal variation of summer precipitation modulated by air pollution:
observational evidences in the beijing metropolitan area, Environ.
Res. Lett., 15, 094053, https://doi.org/10.1088/1748-9326/ab99fc, 2020.
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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