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Comparisons of next-day solar forecasting for Singapore using 3DVAR and 4DVAR data assimilation approaches with the WRF model

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  • Huva, Robert
  • Verbois, Hadrien
  • Walsh, Wilfred

Abstract

For tropical locations forecasting of solar irradiance at time horizons of 12 h, or longer, can only be achieved with the assistance of Numerical Weather Prediction (NWP) models. NWP models simulate the time evolution of atmospheric processes that are important for the prediction of solar irradiance. We use the Weather and Research Forecasting (WRF) model to simulate the atmosphere over Singapore down to 3-km resolution and for the years 2015–2016. However, by their nature the NWP models suffer from incomplete knowledge of atmospheric initial conditions. The process of Data Assimilation (DA) attempts to minimise the initial condition problem by incorporating observations into the model. DA utilises observations to constrain the state of the model either in a static (3DVAR) or time-evolving (4DVAR) manner. We compare hourly next-day forecasts using 3DVAR and 4DVAR intialisations of the WRF model with observations of surface irradiance across Singapore. Raw results show that 4DVAR has the lowest error for all time horizons and for all sky conditions except clear-sky hours. We then post-process the raw results using the random forest algorithm. Following post-processing, the 4DVAR initialised forecasts remain the best performing with relative RMSE of 37%. All models after post-processing out-perform persistence ensemble and climatological references.

Suggested Citation

  • Huva, Robert & Verbois, Hadrien & Walsh, Wilfred, 2020. "Comparisons of next-day solar forecasting for Singapore using 3DVAR and 4DVAR data assimilation approaches with the WRF model," Renewable Energy, Elsevier, vol. 147(P1), pages 663-671.
    • Handle: RePEc:eee:renene:v:147:y:2020:i:p1:p:663-671
    DOI: 10.1016/j.renene.2019.09.011
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    References listed on IDEAS

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    1. Zempila, Melina-Maria & Giannaros, Theodore M. & Bais, Alkiviadis & Melas, Dimitris & Kazantzidis, Andreas, 2016. "Evaluation of WRF shortwave radiation parameterizations in predicting Global Horizontal Irradiance in Greece," Renewable Energy, Elsevier, vol. 86(C), pages 831-840.
    2. Mohan Das & Md. Chowdhury & Someshwar Das & Sujit Debsarma & Samarendra Karmakar, 2015. "Assimilation of Doppler weather radar data and their impacts on the simulation of squall events during pre-monsoon season," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 77(2), pages 901-931, June.
    3. Larson, David P. & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest," Renewable Energy, Elsevier, vol. 91(C), pages 11-20.
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    Cited by:

    1. Zhao, Wei & Zhang, Haoran & Zheng, Jianqin & Dai, Yuanhao & Huang, Liqiao & Shang, Wenlong & Liang, Yongtu, 2021. "A point prediction method based automatic machine learning for day-ahead power output of multi-region photovoltaic plants," Energy, Elsevier, vol. 223(C).
    2. Prasad, Ramendra & Ali, Mumtaz & Xiang, Yong & Khan, Huma, 2020. "A double decomposition-based modelling approach to forecast weekly solar radiation," Renewable Energy, Elsevier, vol. 152(C), pages 9-22.

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