• Cai, B., G. Pan, and F. Fu, 2020: Prediction of the postfire flexural capacity of RC beam using GA-BPNN machine learning. J. Perform. Constr. Facil., 34, 04020105, https://doi.org/10.1061/(ASCE)CF.1943-5509.0001514.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cao, J., Z. Li, and J. Li, 2019: Financial time series forecasting model based on CEEMDAN and LSTM. Physica A, 519, 127139, https://doi.org/10.1016/j.physa.2018.11.061.

    • Search Google Scholar
    • Export Citation
  • Chen, Y., and Coauthors, 2021: Short-term wind speed predicting framework based on EEMD-GA-LSTM method under large scaled wind history. Energy Convers. Manage., 227, 113559, https://doi.org/10.1016/j.enconman.2020.113559.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cheng, W., Y. Liu, A. J. Bourgeois, Y. Wu, and S. Haupt, 2017: Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation. Renewable Energy, 107, 340351, https://doi.org/10.1016/j.renene.2017.02.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deo, R., O. Kisi, and V. Singh, 2017: Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model. Atmos. Res., 184, 149175, https://doi.org/10.1016/j.atmosres.2016.10.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duan, J., H. Zuo, Y. Bai, J. Duan, M. Chang, and B. Chen, 2021: Short-term wind speed forecasting using recurrent neural networks with error correction. Energy, 217, 119397, https://doi.org/10.1016/j.energy.2020.119397.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • El-Fouly, T., E. El-Saadany, and M. Salama, 2006: Grey predictor for wind energy conversion systems output power forecasting. IEEE Trans. Power Syst., 21, 14501452, https://doi.org/10.1109/TPWRS.2006.879246.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, W., K. Zhang, K. Wang, B. Wen, P. Fang, and F. Zou, 2021: A hybrid approach for multi-step wind speed forecasting based on two-layer decomposition, improved hybrid DE-HHO optimization and KELM. Renewable Energy, 164, 211229, https://doi.org/10.1016/j.renene.2020.09.078.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guo, Z., W. Zhao, H. Lu, and J. Wang, 2012: Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model. Renewable Energy, 37, 241249, https://doi.org/10.1016/j.renene.2011.06.023.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holland, J., 1992: Genetic algorithms. Sci. Amer., 267, 6672, https://doi.org/10.1038/scientificamerican0792-66.

  • Hu, Y., and L. Chen, 2018: A nonlinear hybrid wind speed forecasting model using LSTM network, hysteretic ELM and differential evolution algorithm. Energy Convers. Manage., 173, 123142, https://doi.org/10.1016/j.enconman.2018.07.070.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, C., and P. Kuo, 2018: A short-term wind speed forecasting model by using artificial neural networks with stochastic optimization for renewable energy systems. Energies, 11, 2777, https://doi.org/10.3390/en11102777.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, N., and Coauthors, 1998: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Roy. Soc. London, 454A, 903–995, https://doi.org/10.1098/rspa.1998.0193.

    • Crossref
    • Export Citation
  • Ismail, S., A. Shabri, and R. Samsudin, 2011: A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting. Expert Syst. Appl., 38, 10 57410 578, https://doi.org/10.1016/j.eswa.2011.02.107.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jahangir, H., M. Golkar, F. Alhameli, A. Mazouz, A. Ahmadian, and A. Elkamel, 2020: Short-term wind speed forecasting framework based on stacked denoising auto-encoders with rough ANN. Sustainable Energy Technol. Assess., 38, 100601, https://doi.org/10.1016/j.seta.2019.100601.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, P., H. Yang, and J. Heng, 2019: A hybrid forecasting system based on fuzzy time series and multi-objective optimization for wind speed forecasting. Appl. Energy, 235, 786801, https://doi.org/10.1016/j.apenergy.2018.11.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kamath, P., and K. Senapati, 2020: Short-term wind speed forecasting using S-transform with compactly supported kernel. Wind Energy, 24, 260274, https://doi.org/10.1002/we.2571.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kaytez, F., 2020: A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption. Energy, 197, 117200, https://doi.org/10.1016/j.energy.2020.117200.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Y., H. Wu, and H. Liu, 2018: Multi-step wind speed forecasting using EWT decomposition, LSTM principal computing, RELM subordinate computing and IEWT reconstruction. Energy Convers. Manage., 167, 203219, https://doi.org/10.1016/j.enconman.2018.04.082.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, H., X. Mi, and Y. Li, 2018: Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network. Energy Convers. Manage., 156, 498514, https://doi.org/10.1016/j.enconman.2017.11.053.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, H., R. Yang, and Z. Duan, 2020: Wind speed forecasting using a new multi-factor fusion and multi-resolution ensemble model with real-time decomposition and adaptive error correction. Energy Convers. Manage., 217, 112995, https://doi.org/10.1016/j.enconman.2020.112995.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, M., Z. Cao, J. Zhang, L. Wang, C. Huang, and X. Luo, 2020: Short-term wind speed forecasting based on the Jaya-SVM model. Int. J. Electr. Power Energy Syst., 121, 106056, https://doi.org/10.1016/j.ijepes.2020.106056.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Z., P. Jiang, L. Zhang, and X. Niu, 2020: A combined forecasting model for time series: Application to short-term wind speed forecasting. Appl. Energy, 259, 114137, https://doi.org/10.1016/j.apenergy.2019.114137.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, Z., H. Chen, J. Wang, X. Yang, R. Yan, J. Jia, and W. Xu, 2020: Application of hybrid model based on double decomposition, error correction and deep learning in short-term wind speed prediction. Energy Convers. Manage., 205, 112345, https://doi.org/10.1016/j.enconman.2019.112345.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niu, D., Y. Liang, and W. Hong, 2017: Wind speed forecasting based on EMD and GRNN optimized by FOA. Energies, 10, 2001, https://doi.org/10.3390/en10122001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qiu, X., L. Zhang, P. Suganthan, and G. Amaratunga, 2017: Oblique random forest ensemble via least square estimation for time series forecasting. Info. Sci., 420, 249262, https://doi.org/10.1016/j.ins.2017.08.060.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ruiz-Aguilar, J., I. Turias, J. González-Enrique, D. Urda, and D. Elizondo, 2020: A permutation entropy-based EMD–ANN forecasting ensemble approach for wind speed prediction. Neural Comput. Appl., 33, 23692391, https://doi.org/10.1007/s00521-020-05141-w.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rumelhart, D., G. Hinton, and R. Williams, 1986: Learning representations by back-propagating errors. Nature, 323, 533536, https://doi.org/10.1038/323533a0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sahu, B., 2018: Wind energy developments and policies in China: A short review. Renewable Sustainable Energy Rev., 81, 13931405, https://doi.org/10.1016/j.rser.2017.05.183.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saroha, S., and S. Aggarwal, 2020: Wind power forecasting using wavelet transform and general regression neural network for Ontario electricity market. Recent Adv. Electr. Electron. Eng., 13, 1626, https://doi.org/10.2174/2352096512666190118160604.

    • Search Google Scholar
    • Export Citation
  • Shahid, F., A. Khan, A. Zameer, J. Arshad, and K. Safdar, 2020: Wind power prediction using a three stage genetic ensemble and auxiliary predictor. Appl. Soft Comput., 90, 106151, https://doi.org/10.1016/j.asoc.2020.106151.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Singh, S., and A. Mohapatra, 2019: Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting. Renewable Energy, 136, 758768, https://doi.org/10.1016/j.renene.2019.01.031.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Soman, S., H. Zareipour, O. Malik, and P. Mandal, 2010: A review of wind power and wind speed forecasting methods with different time horizons. North American Power Symp. 2010, Arlington, TX, Institute of Electrical and Electronics Engineers, 18, https://doi.org/10.1109/NAPS.2010.5619586.

    • Search Google Scholar
    • Export Citation
  • Sun, W., Ye, M., and Y. Xu, 2016: Study of carbon dioxide emissions prediction in Hebei province, China using a BPNN based on GA. J. Renewable Sustainable Energy, 8, 043101, https://doi.org/10.1063/1.4959236.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torres, M., M. Colominas, G. Schlotthauer, and P. Flandrin, 2011: A complete ensemble empirical mode decomposition with adaptive noise. 2011 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, Institute of Electrical and Electronics Engineers, 41444147, https://doi.org/10.1109/ICASSP.2011.5947265.

    • Search Google Scholar
    • Export Citation
  • Veers, P., and Coauthors, 2019: Grand challenges in the science of wind energy. Science, 366, eaau2027, https://doi.org/10.1126/science.aau2027.

  • Wang, D., H. Luo, O. Grunder, and Y. Lin, 2017: Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction. Renewable Energy, 113, 13451358, https://doi.org/10.1016/j.renene.2017.06.095.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, J., C. Wu, and T. Niu, 2019: A novel system for wind speed forecasting based on multi-objective optimization and echo state network. Sustainability, 11, 526, https://doi.org/10.3390/su11020526.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, L., and C. Huang, 2018: A novel Elite Opposition-based Jaya algorithm for parameter estimation of photovoltaic cell models. Optik, 155, 351356, https://doi.org/10.1016/j.ijleo.2017.10.081.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, S., N. Zhang, L. Wu, and Y. Wang, 2016: Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method. Renewable Energy, 94, 629636, https://doi.org/10.1016/j.renene.2016.03.103.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, T., M. Zhang, Q. Yu, and H. Zhang, 2012: Comparing the applications of EMD and EEMD on time–frequency analysis of seismic signal. J. Appl. Geophys., 83, 2934, https://doi.org/10.1016/j.jappgeo.2012.05.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., S. Wang, and N. Zhang, 2013: A novel wind speed forecasting method based on ensemble empirical mode decomposition and GA-BP neural network. 2013 IEEE Power & Energy Society General Meeting, Vancouver, BC, Institute of Electrical and Electronics Engineers, 15, https://doi.org/10.1109/PESMG.2013.6672195.

    • Crossref
    • Export Citation
  • Wu, Y., R. Gao, and J. Yang, 2020: Prediction of coal and gas outburst: A method based on the BP neural network optimized by GASA. Process Saf. Environ. Prot., 133, 6472, https://doi.org/10.1016/j.psep.2019.10.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yeganeh, B., M. Motlagh, Y. Rashidi, and H. Kamalan, 2012: Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model. Atmos. Environ., 55, 357365, https://doi.org/10.1016/j.atmosenv.2012.02.092.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yi, X., 2015: Selection of initial weights and thresholds based on the Genetic Algorithm with the optimized Back-Propagation neural network. 12th Int. Conf. on Fuzzy Systems and Knowledge Discovery (FSKD), Zhangjiajie, China, Institute of Electrical and Electronics Engineers, 173177, https://doi.org/10.1109/FSKD.2015.7381935.

    • Crossref
    • Export Citation
  • Zhang, P., Y. Wang, L. Liang, X. Li, and Q. Duan, 2020: Short-term wind power predictionusing GA-BP neural network based on DBSCAN algorithm outlier identification. Processes, 8, 157, https://doi.org/10.3390/pr8020157.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, W., Z. Qu, K. Zhang, W. Mao, Y. Ma, and X. Fan, 2017: A combined model based on CEEMDAN and modified flower pollination algorithm for wind speed forecasting. Energy Convers. Manage., 136, 439451, https://doi.org/10.1016/j.enconman.2017.01.022.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Y., B. Chen, G. Pan, and Y. Zhao, 2019: A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting. Energy Convers. Manage., 195, 180197, https://doi.org/10.1016/j.enconman.2019.05.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, Y., T. Li, J. Shi, and Z. Qian, 2019: A CEEMDAN and XGBOOST-based approach to forecast crude oil prices. Complexity, 2019, 4392785, https://doi.org/10.1155/2019/4392785.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, Z., L. Lin, and S. Li, 2018: International stock market contagion: A CEEMDAN wavelet analysis. Econ. Model., 72, 333352, https://doi.org/10.1016/j.econmod.2018.02.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, C., J. Zhang, Y. Liu, D. Ma, M. Li, and B. Xiang, 2020: Comparison of GA-BP and PSO-BP neural network models with initial BP model for rainfall-induced landslides risk assessment in regional scale: A case study in Sichuan, China. Nat. Hazards, 100, 173204, https://doi.org/10.1007/s11069-019-03806-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zou, F., W. Fu, P. Fang, D. Xiong, and R. Wang, 2020: A hybrid model based on multi-round principal component extraction, GRU network and KELM for multi-step short-term wind speed forecasting. IEEE Access, 8, 222931222943, https://doi.org/10.1109/ACCESS.2020.3043812.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 466 387 34
Full Text Views 114 85 5
PDF Downloads 152 110 7

A Hybrid Ultra-Short-Term and Short-Term Wind Speed Forecasting Method Based on CEEMDAN and GA-BPNN

Yi ShangaSchool of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China

Search for other papers by Yi Shang in
Current site
Google Scholar
PubMed
Close
,
Lijuan MiaobSchool of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing, China
cLeibniz Institute of Agricultural Development in Transition Economies (IAMO), Halle, Germany

Search for other papers by Lijuan Miao in
Current site
Google Scholar
PubMed
Close
,
Yunpeng ShandEnvironment and Climate Sciences Department, Brookhaven National Lab, Upton, New York

Search for other papers by Yunpeng Shan in
Current site
Google Scholar
PubMed
Close
,
Kaushal Raj GnyawalieNatural Hazards Section, Himalayan Risk Research Institute, Bhaktapur, Nepal

Search for other papers by Kaushal Raj Gnyawali in
Current site
Google Scholar
PubMed
Close
,
Jing ZhangbSchool of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing, China

Search for other papers by Jing Zhang in
Current site
Google Scholar
PubMed
Close
, and
Giri KattelbSchool of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing, China
fWater and Agriculture Program (WEAP), Department of Infrastructure Engineering, University of Melbourne, Melbourne, Australia
gDepartment of Hydraulic Engineering, Tsinghua University, Beijing, China

Search for other papers by Giri Kattel in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-8348-6477
Restricted access

Abstract

Reliable ultra-short-term and short-term wind speed forecasting is pivotal for clean energy development and grid operation planning. During the wind forecasting process, decomposing the measured wind speed into data with different frequencies is a solution for overcoming the nonlinearity and the randomness of the natural wind. Existing forecasting methods, a hybrid method based on empirical mode decomposition and the back propagation neural network optimized by genetic algorithm (EMD-GA-BPNN), rely on partial decomposing the measured wind speed into data with different frequencies and subsequently achieving forecasting results from machine learning algorithms. However, such methods can roughly divide IMF signals in different frequency domains, but each frequency domain contains signals with multiple frequencies. The condition reflects that the method cannot fully distinguish wind speed into data with different frequencies and thus it compromises the forecasting accuracy. A complete decomposition of measured wind speed can reduce the complexity of machine learning algorithm, and has become a useful approach for precise simulations of wind speed. Here, we propose a novel hybrid method (CEEMDAN-GA-BPNN) based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) by completely decomposing the measured wind speed. The decomposition results are put into the back propagation neural network optimized by a genetic algorithm (GA-BPNN), and the final forecasting results are achieved by combining all the output values by GA-BPNN for each decomposition result from CEEMDAN. We benchmark the forecasting accuracy of the proposed hybrid method against EMD-GA-BPNN integrated by EMD and GA-BPNN. From a wind farm case in Yunnan Province, China, both for ultra-short-term forecasting (15 min) and short-term forecasting (1 h), the performance of the proposed method exceeds EMD-GA-BPNN in several criteria, including root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The forecasting accuracy in decomposed components of low frequencies outperform components of high and middle frequencies. Fine improvement of the error metric (in percentage) in ultra-short-term/short-term forecasting is found by the complete decomposition method CEEMDAN-GA-BPNN: RMSE (7.0% and 8.6%), MAE (7.41% and 7.9%), MAPE (11.0% and 8.7%), and R2 (2.2% and 11.0%), compared with the incomplete decomposing method EMD-GA-BPNN. Our result suggests that CEEMDAN-GA-BPNN could be an accurate wind speed forecasting tool for wind farms development and intelligent grid operations.

Significance Statement

Nonlinearity and randomness of natural wind speed data are the limitations for short-term and ultra-short-term wind speed forecasting. By decreasing forecasting error in machine learning training process, data decomposition for the measured wind speed has become an effective method for overcoming this issue. Nonetheless, the normal incomplete decomposition method will compromise the extent of forecasting accuracy. We introduce a novel hybrid and complete decomposition method CEEMDAN-GA-BPNN (the complete decomposition method). Measured wind speed data from a wind farm in Yunnan Province, China, has been utilized. CEEMDAN-GA-BPNN outperforms EMD-GA-BPNN (the partial decomposition method) in forecasting accuracy both in the ultra-short-term and the short-term wind speed forecasting.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Kattel’s ORCID: 0000-0002-8348-6477.

Corresponding author: Lijuan Miao, miaolijuan1111@gmail.com

Abstract

Reliable ultra-short-term and short-term wind speed forecasting is pivotal for clean energy development and grid operation planning. During the wind forecasting process, decomposing the measured wind speed into data with different frequencies is a solution for overcoming the nonlinearity and the randomness of the natural wind. Existing forecasting methods, a hybrid method based on empirical mode decomposition and the back propagation neural network optimized by genetic algorithm (EMD-GA-BPNN), rely on partial decomposing the measured wind speed into data with different frequencies and subsequently achieving forecasting results from machine learning algorithms. However, such methods can roughly divide IMF signals in different frequency domains, but each frequency domain contains signals with multiple frequencies. The condition reflects that the method cannot fully distinguish wind speed into data with different frequencies and thus it compromises the forecasting accuracy. A complete decomposition of measured wind speed can reduce the complexity of machine learning algorithm, and has become a useful approach for precise simulations of wind speed. Here, we propose a novel hybrid method (CEEMDAN-GA-BPNN) based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) by completely decomposing the measured wind speed. The decomposition results are put into the back propagation neural network optimized by a genetic algorithm (GA-BPNN), and the final forecasting results are achieved by combining all the output values by GA-BPNN for each decomposition result from CEEMDAN. We benchmark the forecasting accuracy of the proposed hybrid method against EMD-GA-BPNN integrated by EMD and GA-BPNN. From a wind farm case in Yunnan Province, China, both for ultra-short-term forecasting (15 min) and short-term forecasting (1 h), the performance of the proposed method exceeds EMD-GA-BPNN in several criteria, including root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The forecasting accuracy in decomposed components of low frequencies outperform components of high and middle frequencies. Fine improvement of the error metric (in percentage) in ultra-short-term/short-term forecasting is found by the complete decomposition method CEEMDAN-GA-BPNN: RMSE (7.0% and 8.6%), MAE (7.41% and 7.9%), MAPE (11.0% and 8.7%), and R2 (2.2% and 11.0%), compared with the incomplete decomposing method EMD-GA-BPNN. Our result suggests that CEEMDAN-GA-BPNN could be an accurate wind speed forecasting tool for wind farms development and intelligent grid operations.

Significance Statement

Nonlinearity and randomness of natural wind speed data are the limitations for short-term and ultra-short-term wind speed forecasting. By decreasing forecasting error in machine learning training process, data decomposition for the measured wind speed has become an effective method for overcoming this issue. Nonetheless, the normal incomplete decomposition method will compromise the extent of forecasting accuracy. We introduce a novel hybrid and complete decomposition method CEEMDAN-GA-BPNN (the complete decomposition method). Measured wind speed data from a wind farm in Yunnan Province, China, has been utilized. CEEMDAN-GA-BPNN outperforms EMD-GA-BPNN (the partial decomposition method) in forecasting accuracy both in the ultra-short-term and the short-term wind speed forecasting.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Kattel’s ORCID: 0000-0002-8348-6477.

Corresponding author: Lijuan Miao, miaolijuan1111@gmail.com

Supplementary Materials

    • Supplemental Materials (ZIP 7.19 MB)
Save