DOI QR코드

DOI QR Code

Advanced Forecasting Approach to Improve Uncertainty of Solar Irradiance Associated with Aerosol Direct Effects

  • Kim, Dong Hyeok ;
  • Yoo, Jung Woo ;
  • Lee, Hwa Woon ;
  • Park, Soon Young ;
  • Kim, Hyun Goo
  • Received : 2017.10.18
  • Accepted : 2017.10.27
  • Published : 2017.10.31

Abstract

Numerical Weather Prediction (NWP) models such as the Weather Research and Forecasting (WRF) model are essential for forecasting one-day-ahead solar irradiance. In order to evaluate the performance of the WRF in forecasting solar irradiance over the Korean Peninsula, we compared WRF prediction data from 2008 to 2010 corresponding to weather observation data (OBS) from the Korean Meteorological Administration (KMA). The WRF model showed poor performance at polluted regions such as Seoul and Suwon where the relative Root Mean Square Error (rRMSE) is over 30%. Predictions by the WRF model alone had a large amount of potential error because of the lack of actual aerosol radiative feedbacks. For the purpose of reducing this error induced by atmospheric particles, i.e., aerosols, the WRF model was coupled with the Community Multiscale Air Quality (CMAQ) model. The coupled system makes it possible to estimate the radiative feedbacks of aerosols on the solar irradiance. As a result, the solar irradiance estimated by the coupled system showed a strong dependence on both the aerosol spatial distributions and the associated optical properties. In the NF (No Feedback) case, which refers to the WRF-only stimulated system without aerosol feedbacks, the GHI was overestimated by $50-200W\;m^{-2}$ compared with OBS derived values at each site. In the YF (Yes Feedback) case, in contrast, which refers to the WRF-CMAQ two-way coupled system, the rRMSE was significantly improved by 3.1-3.7% at Suwon and Seoul where the Particulate Matter (PM) concentrations, specifically, those related to the $PM_{10}$ size fraction, were over $100{\mu}g\;m^{-3}$. Thus, the coupled system showed promise for acquiring more accurate solar irradiance forecasts.

Keywords

Solar irradiance forecasting;Numerical Weather Prediction (NWP);WRF-CMAQ two-way coupled system;Aerosol direct effect

References

  1. Byun, H. R., Lee, D. K., 2002, Defining three rainy seasons and the hydrological summer monsoon in Korea using available water resources index, Korean Journal of Atmospheric Sciences, 2, 80(1), 33-44.
  2. Cao, J. C., Cao, S. H., 2006, Study of forecasting solar irradiance using neural networks with preprocessing sample data by wavelet analysis, Energy, 31, 3435-3445. https://doi.org/10.1016/j.energy.2006.04.001
  3. Chapman, E. G., Gustafson, Jr. W. I., Easter, R. C., Barnard, J. C., Ghan, S. J., Pekour, M. S., Fast, J. D., 2009, Coupling aerosol-cloud-radiative processes in the WRF-Chem model: Investigating the radiative impact of elevated point sources, Atmospheric Chemistry and Physics, 9(3), 945-964. https://doi.org/10.5194/acp-9-945-2009
  4. Chylek, P., Videen, G., Ngo, D., Pinnick, R. G., Klett, J. D., 1995, Effect of black carbon on the optical properties and climate forcing of sulfate aerosols, Journal of Geophysical Research: Atmospheres (1984-2012), 100(D8), 16325-16332. https://doi.org/10.1029/95JD01465
  5. Diagne, M., David, M., Lauret, P., Boland, J., Schmutz, N., 2013, Review of solar irradiance forecasting methods and a proposition for small-scale insular grids, Renewable and Sustainable Energy Reviews, 27, 65-76. https://doi.org/10.1016/j.rser.2013.06.042
  6. Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Alsdorf, D., 2007, The shuttle radar topography mission, Reviews of Geophysics, 45(2).
  7. Hammer, A., Heinemann, D., Lorenz, E., Luckehe, B., 1999, Short-term forecasting of solar radiation: A Statistical approach using satellite data, Solar Energy, 67(1), 139-150. https://doi.org/10.1016/S0038-092X(00)00038-4
  8. Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., Collins, W. D., 2008, Radiative forcing by long lived greenhouse gases: Calculations with the AER radiative transfer models, Journal of Geophysical Research: Atmospheres (1984-2012), 113(D13).
  9. IEA, 2015, Medium-term renewable energy market report 2015: Market analysis and forecasts to 2020, OECD/IEA, Paris, France.
  10. Jeon, W. B., Lee, H. W., Lee, S. H., Park, J. H., Kim, H. G., 2014, Numerical study on the characteristics of high PM2. 5 episodes in Anmyeondo area in 2009, Journal of Environmental Science International, 23(2), 249-259. https://doi.org/10.5322/JESI.2014.23.2.249
  11. Kim, D. H., Lee, H. W., Park, S. Y., Yu, J. W., Park, C., Park, J. H., 2014, Correction of surface characteristics to diagnostic wind modeling and its impact on potentials of wind power density, Journal of Renewable and Sustainable Energy, 6(4), 042012. https://doi.org/10.1063/1.4893415
  12. Kim, N. K., Kim, Y. Y., Kang, C. H., 2011, Long-term trend of aerosol composition and direct radiative forcing due to aerosols over Gosan: TSP, $PM_{10}$, and $PM_{2.5}$ data between 1992 and 2008, Atmospheric Environment, 45(34), 6107-6115. https://doi.org/10.1016/j.atmosenv.2011.08.051
  13. Lacis, A. A., Hansen, J., 1974, A Parameterization for the absorption of solar radiation in the earth's atmosphere, Journal of the Atmospheric Sciences, 31(1), 118-133. https://doi.org/10.1175/1520-0469(1974)031<0118:APFTAO>2.0.CO;2
  14. Lara-Fanego, V., Ruiz-Arias, J. A., Pozo-Vazquez, D., Santos-Alamillos, F. J., Tovar-Pescador, J., 2012, Evaluation of the WRF model solar irradiance forecasts in Andalusia (southern Spain), Solar Energy, 86(8), 2200-2217. https://doi.org/10.1016/j.solener.2011.02.014
  15. Martin, L., Zarzalejo, L. F., Polo, J., Navarro, A., Marchante, R., Cony, M., 2010, Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning, Solar Energy, 84(10), 1772-1781. https://doi.org/10.1016/j.solener.2010.07.002
  16. Mathiesen, P., Collier, C., Kleissl, J., 2013. A High-resolution, cloud-assimilating numerical weather prediction model for solar irradiance forecasting, Solar Energy, 92, 47-61. https://doi.org/10.1016/j.solener.2013.02.018
  17. Mathiesen, P., Jan, K., 2011, Evaluation of numerical weather prediction for intra-day solar forecasting in the continental United States, Solar Energy, 85(5), 967-977. https://doi.org/10.1016/j.solener.2011.02.013
  18. Ministry of Trade, Industry and Energy, 2014, Fourth basic plan for technology development, Application and Development of New & Renewable Energy (2014-2015), Seoul, Korea, 20.
  19. NCAR, 2015. ARW version 3 modeling system user's guide.
  20. Nemesure, S., Wagener, R., Schwartz, S. E., 1995, Direct shortwave forcing of climate by the anthropogenic sulfate aerosol: Sensitivity to particle size, composition, and relative humidity, Journal of Geophysical Research-all Series, 100, 26-105.
  21. Park, S. K., Lee, E., 2007, Synoptic features of orographically enhanced heavy rainfall on the east coast of Korea associated with Typhoon Rusa (2002), Geophysical Research Letters, 34(2).
  22. Pelland, S., Remund, J., Kleissl, J., Oozeki, T., De Brabandere, K., 2013, Photovoltaic and solar forecasting: State of the art, IEA PVPS, Task 14.
  23. Penner, J. E., Andreae, M. O., Annegarn, H., Barrie, L., Feichter, J., Hegg, D., Pitari, G., 2001, Aerosols, their direct and indirect effects, In climate change 2001: The scientific basis, Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 289-348.
  24. Perez, R., Kivalov, S., Schlemmer, J., Hemker, K., Renne, D., Hoff, T. E., 2010, Validation of short and medium term operational solar radiation forecasts in the US, Solar Energy, 84(12), 2161-2172. https://doi.org/10.1016/j.solener.2010.08.014
  25. Ramanathan, V., Crutzen, P. J., Kiehl, J. T., Rosenfeld, D., 2001, Aerosols, climate, and the hydrological cycle, Science, 294(5549), 2119-2124. https://doi.org/10.1126/science.1064034
  26. Solomon, S. (Ed.), 2007, Climate change 2007-the physical science basis, Working Group I Contribution to the Fourth Assessment Report of the IPCC, 4, Cambridge University Press.
  27. Wong, D. C., Pleim, J., Mathur, R., Binkowski, F., Otte, T., Gilliam, R., Kang, D., 2012, WRF-CMAQ two-way coupled system with aerosol feedback: Software development and preliminary results, Geoscientific Model Development, 5(2), 299-312. https://doi.org/10.5194/gmd-5-299-2012
  28. Yu, H., Kaufman, Y. J., Chin, M., Feingold, G., Remer, L. A., Anderson, T. L., Balkanski, Y., Bellouin, N., Boucher, O., Christopher, S., DeCola, P., Kahn, R., Koch, D., Loeb, N., Reddy, M. S., Schulz, M., Takemura, T., Zhou, M., 2006, A Review of measurement-based assessments of the aerosol direct radiative effect and forcing, Atmos. Chem. Phys., 6, 613-666. https://doi.org/10.5194/acp-6-613-2006
  29. Zhang, Q., Streets, D. G., Carmichael, G. R., He, K. B., Huo, H., Kannari, A., Yao, Z. L., 2009, Asian emissions in 2006 for the NASA INTEX-B mission, Atmospheric Chemistry and Physics, 9(14), 5131-5153. https://doi.org/10.5194/acp-9-5131-2009

Acknowledgement

Supported by : National Research Foundation of Korea