Moving beyond the Aerosol Climatology of WRF-Solar: A Case Study over the North China Plain

Wenting Wang aSchool of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin, Heilongjiang, China

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Hongrong Shi bKey Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Disong Fu bKey Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Mengqi Liu cKey Laboratory of Atmospheric Sounding, Chengdu University of Information Technology, Chengdu, China

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Jiawei Li dKey Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Yunpeng Shan eAtmospheric Science and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington

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Tao Hong fDepartment of Systems Engineering and Engineering Management, University of North Carolina at Charlotte, Charlotte, North Carolina

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Dazhi Yang aSchool of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin, Heilongjiang, China

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Xiang’ao Xia bKey Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Abstract

Numerical weather prediction (NWP), when accessible, is a crucial input to short-term solar power forecasting. WRF-Solar, the first NWP model specifically designed for solar energy applications, has shown promising predictive capability. Nevertheless, few attempts have been made to investigate its performance under high aerosol loading, which attenuates incoming radiation significantly. The North China Plain is a polluted region due to industrialization, which constitutes a proper testbed for such investigation. In this paper, aerosol direct radiative effect (DRE) on three surface shortwave radiation components (i.e., global, beam, and diffuse) during five heavy pollution episodes is studied within the WRF-Solar framework. Results show that WRF-Solar overestimates instantaneous beam radiation up to 795.3 W m−2 when the aerosol DRE is not considered. Although such overestimation can be partially offset by an underestimation of the diffuse radiation of about 194.5 W m−2, the overestimation of the global radiation still reaches 160.2 W m−2. This undesirable bias can be reduced when WRF-Solar is powered by Copernicus Atmosphere Monitoring Service (CAMS) aerosol forecasts, which then translates to accuracy improvements in photovoltaic (PV) power forecasts. This work also compares the forecast performance of the CAMS-powered WRF-Solar with that of the European Centre for Medium-Range Weather Forecasts model. Under high aerosol loading conditions, the irradiance forecast accuracy generated by WRF-Solar increased by 53.2% and the PV power forecast accuracy increased by 6.8%.

Significance Statement

Numerical weather prediction (NWP) is the “go-to” approach for achieving high-performance day-ahead solar power forecasting. Integrating time-varying aerosol forecasts into NWP models effectively captures aerosol direct radiation effects, thereby enhancing the accuracy of solar irradiance forecasts in heavily polluted regions. This work not only quantifies the aerosol effects on global, beam, and diffuse irradiance but also reveals the physical mechanisms of irradiance-to-power conversion by constructing a model chain. Using the North China Plain as a testbed, the performance of WRF-Solar on solar power forecasting on five severe pollution days is analyzed. This version of WRF-Solar can outperform the European Centre for Medium-Range Weather Forecasts model, confirming the need for generating high spatial–temporal NWP.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding authors: Hongrong Shi, shihrong@mail.iap.ac.cn; Dazhi Yang, yangdazhi.nus@gmail.com

Abstract

Numerical weather prediction (NWP), when accessible, is a crucial input to short-term solar power forecasting. WRF-Solar, the first NWP model specifically designed for solar energy applications, has shown promising predictive capability. Nevertheless, few attempts have been made to investigate its performance under high aerosol loading, which attenuates incoming radiation significantly. The North China Plain is a polluted region due to industrialization, which constitutes a proper testbed for such investigation. In this paper, aerosol direct radiative effect (DRE) on three surface shortwave radiation components (i.e., global, beam, and diffuse) during five heavy pollution episodes is studied within the WRF-Solar framework. Results show that WRF-Solar overestimates instantaneous beam radiation up to 795.3 W m−2 when the aerosol DRE is not considered. Although such overestimation can be partially offset by an underestimation of the diffuse radiation of about 194.5 W m−2, the overestimation of the global radiation still reaches 160.2 W m−2. This undesirable bias can be reduced when WRF-Solar is powered by Copernicus Atmosphere Monitoring Service (CAMS) aerosol forecasts, which then translates to accuracy improvements in photovoltaic (PV) power forecasts. This work also compares the forecast performance of the CAMS-powered WRF-Solar with that of the European Centre for Medium-Range Weather Forecasts model. Under high aerosol loading conditions, the irradiance forecast accuracy generated by WRF-Solar increased by 53.2% and the PV power forecast accuracy increased by 6.8%.

Significance Statement

Numerical weather prediction (NWP) is the “go-to” approach for achieving high-performance day-ahead solar power forecasting. Integrating time-varying aerosol forecasts into NWP models effectively captures aerosol direct radiation effects, thereby enhancing the accuracy of solar irradiance forecasts in heavily polluted regions. This work not only quantifies the aerosol effects on global, beam, and diffuse irradiance but also reveals the physical mechanisms of irradiance-to-power conversion by constructing a model chain. Using the North China Plain as a testbed, the performance of WRF-Solar on solar power forecasting on five severe pollution days is analyzed. This version of WRF-Solar can outperform the European Centre for Medium-Range Weather Forecasts model, confirming the need for generating high spatial–temporal NWP.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding authors: Hongrong Shi, shihrong@mail.iap.ac.cn; Dazhi Yang, yangdazhi.nus@gmail.com
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  • Antonanzas, J., D. Pozo-Vázquez, L. A. Fernandez-Jimenez, and F. J. M. de Pison, 2017: The value of day-ahead forecasting for photovoltaics in the Spanish electricity market. Sol. Energy, 158, 140146, https://doi.org/10.1016/j.solener.2017.09.043.

    • Search Google Scholar
    • Export Citation
  • Bacher, P., H. Madsen, and H. A. Nielsen, 2009: Online short-term solar power forecasting. Sol. Energy, 83, 17721783, https://doi.org/10.1016/j.solener.2009.05.016.

    • Search Google Scholar
    • Export Citation
  • Bauer, P., A. Thorpe, and G. Brunet, 2015: The quiet revolution of numerical weather prediction. Nature, 525, 4755, https://doi.org/10.1038/nature14956.

    • Search Google Scholar
    • Export Citation
  • Branch, O., T. Schwitalla, M. Temimi, R. Fonseca, N. Nelli, M. Weston, J. Milovac, and V. Wulfmeyer, 2021: Seasonal and diurnal performance of daily forecasts with WRF V3.8.1 over the United Arab Emirates. Geosci. Model Dev., 14, 16151637, https://doi.org/10.5194/gmd-14-1615-2021.

    • Search Google Scholar
    • Export Citation
  • Deng, A., B. J. Gaudet, J. Dudhia, and K. Alapaty, 2014: Implementation and evaluation of a new shallow convection scheme in WRF. 26th Conf. on Weather Analysis and Forecasting/22nd Conf. on Numerical Weather Prediction, Atlanta, GA, Amer. Meteor. Soc., 12.5, https://ams.confex.com/ams/94Annual/webprogram/Paper236925.html.

  • Dobos, A. P., 2014: PVWatts version 5 manual. NREL Tech. Rep. NREL/TP-6A20-62641, 20 pp., https://www.nrel.gov/docs/fy14osti/62641.pdf.

  • Duplyakin, D., S. Zisman, C. Phillips, and H. Tinnesand, 2021: Bias characterization, vertical interpolation, and horizontal interpolation for distributed wind siting using mesoscale wind resource estimates. NREL Tech. Rep. NREL/TP-2C00-78412, 50 pp., https://www.nrel.gov/docs/fy21osti/78412.pdf.

  • Faiman, D., 2008: Assessing the outdoor operating temperature of photovoltaic modules. Prog. Photovoltaics Res. Appl., 16, 307315, https://doi.org/10.1002/pip.813.

    • Search Google Scholar
    • Export Citation
  • Fu, D., and Coauthors, 2020: Mitigating MODIS AOD non-random sampling error on surface PM2.5 estimates by a combined use of Bayesian Maximum Entropy method and linear mixed-effects model. Atmos. Pollut. Res., 11, 482490, https://doi.org/10.1016/j.apr.2019.11.020.

    • Search Google Scholar
    • Export Citation
  • Gong, S. L., and X. Y. Zhang, 2008: CUACE/Dust – an integrated system of observation and modeling systems for operational dust forecasting in Asia. Atmos. Chem. Phys., 8, 23332340, https://doi.org/10.5194/acp-8-2333-2008.

    • Search Google Scholar
    • Export Citation
  • Grell, G. A., and S. R. Freitas, 2014: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. Atmos. Chem. Phys., 14, 52335250, https://doi.org/10.5194/acp-14-5233-2014.

    • Search Google Scholar
    • Export Citation
  • Gueymard, C. A., and D. Yang, 2020: Worldwide validation of CAMS and MERRA-2 reanalysis aerosol optical depth products using 15 years of AERONET observations. Atmos. Environ., 225, 117216, https://doi.org/10.1016/j.atmosenv.2019.117216.

    • Search Google Scholar
    • Export Citation
  • Haupt, S. E., and Coauthors, 2018: Building the Sun4Cast system: Improvements in solar power forecasting. Bull. Amer. Meteor. Soc., 99, 121136, https://doi.org/10.1175/BAMS-D-16-0221.1.

    • Search Google Scholar
    • Export Citation
  • Holben, B., and Coauthors, 1998: Aeronet—A federated instrument network and data archive for aerosol characterization. Remote Sens. Environ., 66, 116, https://doi.org/10.1016/S0034-4257(98)00031-5.

    • Search Google Scholar
    • Export Citation
  • Holmgren, W. F., C. W. Hansen, and M. A. Mikofski, 2018: pvlib python: A python package for modeling solar energy systems. J. Open Source Software, 3, 884, https://doi.org/10.21105/joss.00884.

    • Search Google Scholar
    • Export Citation
  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944.

    • Search Google Scholar
    • Export Citation
  • Jimenez, P. A., and Coauthors, 2016: WRF-solar: Description and clear-sky assessment of an augmented NWP model for solar power prediction. Bull. Amer. Meteor. Soc., 97, 12491264, https://doi.org/10.1175/BAMS-D-14-00279.1.

    • Search Google Scholar
    • Export Citation
  • Juliano, T. W., and Coauthors, 2022: Smoke from 2020 United States wildfires responsible for substantial solar energy forecast errors. Environ. Res. Lett., 17, 034010, https://doi.org/10.1088/1748-9326/ac5143.

    • Search Google Scholar
    • Export Citation
  • Lee, J. A., P. A. Jiménez, J. Dudhia, and Y.-M. Saint-Drenan, 2023: Impacts of the aerosol representation in WRF-Solar clear-sky irradiance forecasts over CONUS. J. Appl. Meteor. Climatol., 62, 227250, https://doi.org/10.1175/JAMC-D-22-0059.1.

    • Search Google Scholar
    • Export Citation
  • Li, Z., and Coauthors, 2007: Aerosol optical properties and their radiative effects in northern China. J. Geophys. Res., 112, D22S01, https://doi.org/10.1029/2006JD007382.

    • Search Google Scholar
    • Export Citation
  • Liu, J., X. Xia, P. Wang, Z. Li, Y. Zheng, M. Cribb, and H. Chen, 2007: Significant aerosol direct radiative effects during a pollution episode in northern China. Geophys. Res. Lett., 34, L23808, https://doi.org/10.1029/2007GL030953.

    • Search Google Scholar
    • Export Citation
  • Liu, M., X. Fan, X. Xia, J. Zhang, and J. Li, 2023: Value-added products derived from 15 years of high-quality surface solar radiation measurements at Xianghe, a suburban site in the North China Plain. Adv. Atmos. Sci., 40, 11321141, https://doi.org/10.1007/s00376-022-2205-0.

    • Search Google Scholar
    • Export Citation
  • Liu, Y., Y. Qian, S. Feng, L. K. Berg, T. W. Juliano, P. A. Jiménez, E. Grimit, and Y. Liu, 2022: Calibration of cloud and aerosol related parameters for solar irradiance forecasts in WRF-Solar. Sol. Energy, 241, 112, https://doi.org/10.1016/j.solener.2022.05.064.

    • Search Google Scholar
    • Export Citation
  • Mayer, M. J., 2021: Influence of design data availability on the accuracy of physical photovoltaic power forecasts. Sol. Energy, 227, 532540, https://doi.org/10.1016/j.solener.2021.09.044.

    • Search Google Scholar
    • Export Citation
  • Mayer, M. J., 2022: Impact of the tilt angle, inverter sizing factor and row spacing on the photovoltaic power forecast accuracy. Appl. Energy, 323, 119598, https://doi.org/10.1016/j.apenergy.2022.119598.

    • Search Google Scholar
    • Export Citation
  • Mayer, M. J., and G. Gróf, 2021: Extensive comparison of physical models for photovoltaic power forecasting. Appl. Energy, 283, 116239, https://doi.org/10.1016/j.apenergy.2020.116239.

    • Search Google Scholar
    • Export Citation
  • Müller, M., 1995: Equation of time—Problem in astronomy. Acta Phys. Pol., 88, S49S67.

  • Nakanishi, M., and H. Niino, 2006: An improved Mellor–Yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog. Bound.-Layer Meteor., 119, 397407, https://doi.org/10.1007/s10546-005-9030-8.

    • Search Google Scholar
    • Export Citation
  • Niu, T., S. L. Gong, G. F. Zhu, H. L. Liu, X. Q. Hu, C. H. Zhou, and Y. Q. Wang, 2008: Data assimilation of dust aerosol observations for the CUACE/dust forecasting system. Atmos. Chem. Phys., 8, 34733482, https://doi.org/10.5194/acp-8-3473-2008.

    • Search Google Scholar
    • Export Citation
  • Perez, R., P. Ineichen, R. Seals, J. Michalsky, and R. Stewart, 1990: Modeling daylight availability and irradiance components from direct and global irradiance. Sol. Energy, 44, 271289, https://doi.org/10.1016/0038-092X(90)90055-H.

    • Search Google Scholar
    • Export Citation
  • Prasad, A. A., and M. Kay, 2020: Assessment of simulated solar irradiance on days of high intermittency using WRF-Solar. Energies, 13, 385, https://doi.org/10.3390/en13020385.

    • Search Google Scholar
    • Export Citation
  • Ruiz-Arias, J. A., J. Dudhia, and C. A. Gueymard, 2014: A simple parameterization of the short-wave aerosol optical properties for surface direct and diffuse irradiances assessment in a numerical weather model. Geosci. Model Dev., 7, 11591174, https://doi.org/10.5194/gmd-7-1159-2014.

    • Search Google Scholar
    • Export Citation
  • Schroedter-Homscheidt, M., A. Oumbe, A. Benedetti, and J.-J. Morcrette, 2013: Aerosols for concentrating solar electricity production forecasts: Requirement quantification and ECMWF/MACC aerosol forecast assessment. Bull. Amer. Meteor. Soc., 94, 903914, https://doi.org/10.1175/BAMS-D-11-00259.1.

    • Search Google Scholar
    • Export Citation
  • Shan, Y., and Coauthors, 2022: Revealing bias of cloud radiative effect in WRF simulation: Bias quantification and source attribution. J. Geophys. Res. Atmos., 127, e2021JD036319, https://doi.org/10.1029/2021JD036319.

    • Search Google Scholar
    • Export Citation
  • Shi, H., and Coauthors, 2019: Modeling study of the air quality impact of record-breaking southern California wildfires in December 2017. J. Geophys. Res. Atmos., 124, 65546570, https://doi.org/10.1029/2019JD030472.

    • Search Google Scholar
    • Export Citation
  • Shi, H., and Coauthors, 2021: Surface brightening in eastern and central China since the implementation of the clean air action in 2013: Causes and implications. Geophys. Res. Lett., 48, e2020GL091105, https://doi.org/10.1029/2020GL091105.

    • Search Google Scholar
    • Export Citation
  • Shi, H., and Coauthors, 2023: First estimation of high-resolution solar photovoltaic resource maps over China with Fengyun-4A satellite and machine learning. Renewable Sustainable Energy Rev., 184, 113549, https://doi.org/10.1016/j.rser.2023.113549.

    • Search Google Scholar
    • Export Citation
  • Simmons, A. J., and A. Hollingsworth, 2002: Some aspects of the improvement in skill of numerical weather prediction. Quart. J. Roy. Meteor. Soc., 128, 647677, https://doi.org/10.1256/003590002321042135.

    • Search Google Scholar
    • Export Citation
  • Theocharides, S., G. Makrides, A. Livera, M. Theristis, P. Kaimakis, and G. E. Georghiou, 2020: Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing. Appl. Energy, 268, 115023, https://doi.org/10.1016/j.apenergy.2020.115023.

    • Search Google Scholar
    • Export Citation
  • Thompson, G., and T. Eidhammer, 2014: A study of aerosol impacts on clouds and precipitation development in a large winter cyclone. J. Atmos. Sci., 71, 36363658, https://doi.org/10.1175/JAS-D-13-0305.1.

    • Search Google Scholar
    • Export Citation
  • Wang, K., X. Qi, and H. Liu, 2019: A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network. Appl. Energy, 251, 113315, https://doi.org/10.1016/j.apenergy.2019.113315.

    • Search Google Scholar
    • Export Citation
  • Wang, S., T. Dai, C. Li, Y. Cheng, G. Huang, and G. Shi, 2022a: Improving clear-sky solar power prediction over China by assimilating Himawari-8 aerosol optical depth with WRF-Chem-Solar. Remote Sens., 14, 4990, https://doi.org/10.3390/rs14194990.

    • Search Google Scholar
    • Export Citation
  • Wang, W., D. Yang, T. Hong, and J. Kleissl, 2022b: An archived dataset from the ECMWF ensemble prediction system for probabilistic solar power forecasting. Sol. Energy, 248, 6475, https://doi.org/10.1016/j.solener.2022.10.062.

    • Search Google Scholar
    • Export Citation
  • Wang, W., D. Yang, N. Huang, C. Lyu, G. Zhang, and X. Han, 2022c: Irradiance-to-power conversion based on physical model chain: An application on the optimal configuration of multi-energy microgrid in cold climate. Renewable Sustainable Energy Rev., 161, 112356, https://doi.org/10.1016/j.rser.2022.112356.

    • Search Google Scholar
    • Export Citation
  • Xia, X., Z. Li, P. Wang, H. Chen, and M. Cribb, 2007: Estimation of aerosol effects on surface irradiance based on measurements and radiative transfer model simulations in northern China. J. Geophys. Res., 112, D22S10, https://doi.org/10.1029/2006JD008337.

    • Search Google Scholar
    • Export Citation
  • Xia, X., H. Che, H. Shi, H. Chen, X. Zhang, P. Wang, P. Goloub, and B. Holben, 2021: Advances in sunphotometer-measured aerosol optical properties and related topics in China: Impetus and perspectives. Atmos. Res., 249, 105286, https://doi.org/10.1016/j.atmosres.2020.105286.

    • Search Google Scholar
    • Export Citation
  • Xia, X. A., H. B. Chen, P. C. Wang, W. X. Zhang, P. Goloub, B. Chatenet, T. F. Eck, and B. N. Holben, 2006: Variation of column-integrated aerosol properties in a Chinese urban region. J. Geophys. Res., 111, D05204, https://doi.org/10.1029/2005JD006203.

    • Search Google Scholar
    • Export Citation
  • Xie, Y., M. Sengupta, A. Habte, and A. Andreas, 2022: The “Fresnel Equations” for diffuse radiation on inclined photovoltaic surfaces (FEDIS). Renewable Sustainable Energy Rev., 161, 112362, https://doi.org/10.1016/j.rser.2022.112362.

    • Search Google Scholar
    • Export Citation
  • Yang, D., 2018: A correct validation of the National Solar Radiation Data Base (NSRDB). Renewable Sustainable Energy Rev., 97, 152155, https://doi.org/10.1016/j.rser.2018.08.023.

    • Search Google Scholar
    • Export Citation
  • Yang, D., and J. Kleissl, 2023: Summarizing ensemble NWP forecasts for grid operators: Consistency, elicitability, and economic value. Int. J. Forecasting, 39, 16401654, https://doi.org/10.1016/j.ijforecast.2022.08.002.

    • Search Google Scholar
    • Export Citation
  • Yang, D., and J. Kleissl, 2024: Solar Irradiance and Photovoltaic Power Forecasting. CRC Press, 681 pp.

  • Yang, D., J. Kleissl, C. A. Gueymard, H. T. C. Pedro, and C. F. M. Coimbra, 2018: History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining. Sol. Energy, 168, 60101, https://doi.org/10.1016/j.solener.2017.11.023.

    • Search Google Scholar
    • Export Citation
  • Yang, D., and Coauthors, 2020: Verification of deterministic solar forecasts. Sol. Energy, 210, 2037, https://doi.org/10.1016/j.solener.2020.04.019.

    • Search Google Scholar
    • Export Citation
  • Yang, D., W. Wang, J. M. Bright, C. Voyant, G. Notton, G. Zhang, and C. Lyu, 2022a: Verifying operational intra-day solar forecasts from ECMWF and NOAA. Sol. Energy, 236, 743755, https://doi.org/10.1016/j.solener.2022.03.004.

    • Search Google Scholar
    • Export Citation
  • Yang, D., W. Wang, and T. Hong, 2022b: A historical weather forecast dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) for energy forecasting. Sol. Energy, 232, 263274, https://doi.org/10.1016/j.solener.2021.12.011.

    • Search Google Scholar
    • Export Citation
  • Yang, D., W. Wang, and X. Xia, 2022c: A concise overview on solar resource assessment and forecasting. Adv. Atmos. Sci., 39, 12391251, https://doi.org/10.1007/s00376-021-1372-8.

    • Search Google Scholar
    • Export Citation
  • Zhang, L., and Coauthors, 2022: Development and evaluation of the Aerosol Forecast Member in the National Center for Environment Prediction (NCEP)’s Global Ensemble Forecast System (GEFS–aerosols v1). Geosci. Model Dev., 15, 53375369, https://doi.org/10.5194/gmd-15-5337-2022.

    • Search Google Scholar
    • Export Citation
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