Solar irradiance plays a critical role in Earth's energy balance and climate. Accurate sub-seasonal forecasts of surface solar irradiance are essential for various applications, including renewable energy planning and regional climate research. This study evaluates ensemble forecasts of surface solar irradiance using the ECMWF dataset (EC-ENS) with a 6-hourly time-step. We compare these forecasts with gridded observations from the China Meteorological Agency (CMA) over the Indo-China peninsular region. Solar irradiance, as Earth's primary energy source, is influenced by atmospheric conditions, and even minor fluctuations in the sun's energy output can significantly impact the climate. Hence, understanding and predicting solar irradiance variations are crucial. For the analysis, we utilize the EC-ENS model data and gridded observation data available from June 2021 to May 2022, with hourly and 6-hourly intervals. Performance evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE), and mean bias error (MBE), are employed to assess the accuracy of the EC-ENS model against observations. Results show an RMSE of approximately 414.43 W/m², an MAE of 380.95 W/m², and an MBE of -309.72 W/m², providing insights into forecast deviations. Furthermore, this study focuses on capturing regional variations in solar irradiance. The spatially continuous hourly estimates derived from ensemble forecasts effectively reconstruct sub-seasonal patterns on smaller scales. This precise knowledge is crucial for applications such as site selection for solar power plants and understanding regional climate changes. Accurate assessment of solar irradiance enables informed decision-making for renewable energy planning and enhances our understanding of regional climate dynamics. In summary, performance evaluation metrics provide insights into forecast accuracy. Additionally, spatially continuous estimates capture regional variations, enabling precise predictions for renewable energy planning and climate research. Advancing our understanding of solar irradiance patterns contributes to sustainable energy strategies and enhances knowledge of regional climate dynamics.
| Published in | International Journal of Science, Technology and Society (Volume 11, Issue 3) |
| DOI | 10.11648/j.ijsts.20231103.16 |
| Page(s) | 130-134 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2023. Published by Science Publishing Group |
Solar Irradiance, Sub-Seasonal Variability, Forecasting, ECMWF Ensemble Forecast System
| [1] | Weaver SJ, Wang W, Chen M, Kumar A (2011) Representation of MJO variability in the NCEP climate forecast system. J Clim 24: 4676–4694. |
| [2] | Vitart F (2014) Evolution of ECMWF sub-seasonal forecast skill scores. Q J R Meteorol Soc 140: 1889–1899. |
| [3] | Robertson AW, Kumar A, Peña M, Vitart F (2015) Improving and promoting subseasonal to seasonal prediction. Bull Am Meteorol Soc 96: ES49–ES53. |
| [4] | Liang P, Ding YH (2012). Extended range forecast experiment based on intraseasonal oscillation. Chinese J Atmos Sci (in Chinese) 36: 102–116. |
| [5] | Gao J, Lin H, You L, Chen S (2016) Monitoring early-flood season intraseasonal oscillations and persistent heavy rainfall in South China. Clim Dyn. doi: 10.1007/s00382-016-3045-3. |
| [6] | Lin H (2013) Monitoring and predicting the intraseasonal variability of the East Asian–Western North Pacific summer monsoon. Mon Weather Rev 141: 1124–1138. |
| [7] | Kleissl, J. (2013) Solar Energy Forecasting and Resource Assessment. Oxford: Academic Press. |
| [8] | Soubdhan, T., Emilion, R. and Calif, R. (2009) Classification of daily solar radiation distributions using a mixture of Dirichlet distributions. Solar Energy, 83 (7), 1056–1063. https://doi.org/10.1016/j.solener.2009.01.010. |
| [9] | Yu, L., Zhang, M., Wang, L., Lu, Y. and Li, J. (2021) Effects of aerosols and water vapour on spatial-temporal variations of the clear-sky surface solar radiation in China. Atmospheric Research, 248, 105162. https://doi.org/10.1016/j.atmosres.2020. 105162. |
| [10] | Song, G., Huva, R., & Zhao, Y. (2022). Combination model for day-ahead solar forecasting using local and global model input. Journal of Renewable and Sustainable Energy, 14 (3), 036102. |
| [11] | Browell, J., Drew, D. R., and Philippopoulos, K. (2018). Improved very short-term spatio-temporal wind forecasting using atmospheric regimes, Wind Energy, 21, 968–979. |
| [12] | Stanger, J., Finney, I., Weisheimer, A., & Palmer, T. (2019). Optimising the use of ensemble information in numerical weather forecasts of wind power generation. Environmental Research Letters, 14 (12), 124086. |
| [13] | Bruno Soares, M., & Dessai, S. (2016). Barriers and enablers to the use of seasonal climate forecasts amongst organisations in Europe. Climatic Change, 137, 89-103. |
| [14] | Thornton, H. E., Scaife, A., Hoskins, B., Brayshaw, D., Smith, D., Dunstone, N., Stringer, N., and Bett, P. E. (2019). Skilful seasonal prediction of winter gas demand, Environ. Res. Lett., 14, 024009, https://doi.org/10.1088/1748-9326/aaf338. |
| [15] | Soret, A., Torralba, V., Cortesi, N., Christel, I., Palma, L., Manrique-Suñén, A.,... & Doblas-Reyes, F. J. (2019, May). Sub-seasonal to seasonal climate predictions for wind energy forecasting. In Journal of Physics: Conference Series (Vol. 1222, No. 1, p. 012009). IOP Publishing. |
| [16] | Bett, P., Thornton, H. E., Troccoli, A., De Felice, M., Suckling, E., Dubus, L., Saint-Drenan, Y.-M., and Brayshaw, D. J. (2019). A simplified seasonal forecasting strategy, applied to wind and solar power in Europe, EarthArXiv, https://doi.org/10.31223/osf.io/kzwqx. |
| [17] | Arnal, L., Cloke, H. L., Stephens, E., Wetterhall, F., Prudhomme, C., Neumann, J., Krzeminski, B. and Pappenberger, F. (2018). Skilful seasonal forecasts of streamflow over Europe?. Hydrology and Earth System Sciences, 22 (4), pp. 2057-2072. |
| [18] | Zagar, N., Buizza, R., & Tribbia, J. (2015). A three-dimensional multivariate modal analysis of atmospheric predictability with application to the ECMWF ensemble. Journal of the Atmospheric Sciences, 72 (11), 4423-4444. |
| [19] | Yang, D., Alessandrini, S., Antonanzas, J., Antonanzas-Torres, F., Badescu, V., Beyer, H. G., Blaga, R., Boland, J., Bright, J. M., Coimbra, C. F. M, David, M., Frimanek, Â., Gueymard, C. A., Hong, T., Kay, M. J., Killinger, S., Kleissl, J., Lauret, P., Lorenz, E., van der Meer, D., Paulescu, M., Perez, R., Perpiñán-Lamigueiro, O., Peters, I. M, Reikard, G., Renné, D., Saint-Drenan, Y-M., Shuai, Y., Urraca, R., Verbois, V., Vignola, F., Voyant, C., and Zhang, J. (2020). Verification of deterministic solar forecasts. Sol. Energy 210, 20–37. https://doi.org/10.1016/j.solener.2020.04.019. |
| [20] | Wang, K., Ma, Q., Wang, X., and Wild, M. (2014). Urban impacts on mean and trend of surface incident solar radiation. Geophysical Research Letters, 41 (13), 4664-4668. |
| [21] | Shamim, M. A., Remesan, R., Bray, M., and Han, D. (2015). An improved technique for global solar radiation estimation using numerical weather prediction. Journal of Atmospheric and Solar-Terrestrial Physics, 129, 13-22. |
| [22] | Yao, W., Li, Z., Xiu, T., Lu, Y., and Li, X. (2015). New decomposition models to estimate hourly global solar radiation from the daily value. Solar Energy, 120, 87-99. |
| [23] | Lu, N., Qin, J., Yang, K., and Sun, J. (2011). A simple and efficient algorithm to estimate daily global solar radiation from geostationary satellite data. Energy, 36 (5), 3179-3188. |
| [24] | Landeras, G., López, J. J., Kisi, O., and Shiri, J. (2012). Comparison of Gene Expression Programming with neuro-fuzzy and neural network computing techniques in estimating daily incoming solar radiation in the Basque Country (Northern Spain). Energy conversion and management, 62, 1-13. |
| [25] | Şenkal, O., and Kuleli, T. (2009). Estimation of solar radiation over Turkey using artificial neural network and satellite data. Applied energy, 86 (7-8), 1222-1228. |
APA Style
Junyu Cai, Bing Ding, Veeranjaneyulu Chinta, Hao Chen, Peng Wang, et al. (2023). Assessment of ECMWF Sub-Seasonal Solar Irradiance Forecast over Indo-China Peninsula. International Journal of Science, Technology and Society, 11(3), 130-134. https://doi.org/10.11648/j.ijsts.20231103.16
ACS Style
Junyu Cai; Bing Ding; Veeranjaneyulu Chinta; Hao Chen; Peng Wang, et al. Assessment of ECMWF Sub-Seasonal Solar Irradiance Forecast over Indo-China Peninsula. Int. J. Sci. Technol. Soc. 2023, 11(3), 130-134. doi: 10.11648/j.ijsts.20231103.16
AMA Style
Junyu Cai, Bing Ding, Veeranjaneyulu Chinta, Hao Chen, Peng Wang, et al. Assessment of ECMWF Sub-Seasonal Solar Irradiance Forecast over Indo-China Peninsula. Int J Sci Technol Soc. 2023;11(3):130-134. doi: 10.11648/j.ijsts.20231103.16
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ijsts.20231103.16,
author = {Junyu Cai and Bing Ding and Veeranjaneyulu Chinta and Hao Chen and Peng Wang and Jiangfeng Zhang and Mingbo Liu and Ning Ding and Chen Zeng and Wei Zhang and Guiting Song},
title = {Assessment of ECMWF Sub-Seasonal Solar Irradiance Forecast over Indo-China Peninsula},
journal = {International Journal of Science, Technology and Society},
volume = {11},
number = {3},
pages = {130-134},
doi = {10.11648/j.ijsts.20231103.16},
url = {https://doi.org/10.11648/j.ijsts.20231103.16},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsts.20231103.16},
abstract = {Solar irradiance plays a critical role in Earth's energy balance and climate. Accurate sub-seasonal forecasts of surface solar irradiance are essential for various applications, including renewable energy planning and regional climate research. This study evaluates ensemble forecasts of surface solar irradiance using the ECMWF dataset (EC-ENS) with a 6-hourly time-step. We compare these forecasts with gridded observations from the China Meteorological Agency (CMA) over the Indo-China peninsular region. Solar irradiance, as Earth's primary energy source, is influenced by atmospheric conditions, and even minor fluctuations in the sun's energy output can significantly impact the climate. Hence, understanding and predicting solar irradiance variations are crucial. For the analysis, we utilize the EC-ENS model data and gridded observation data available from June 2021 to May 2022, with hourly and 6-hourly intervals. Performance evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE), and mean bias error (MBE), are employed to assess the accuracy of the EC-ENS model against observations. Results show an RMSE of approximately 414.43 W/m², an MAE of 380.95 W/m², and an MBE of -309.72 W/m², providing insights into forecast deviations. Furthermore, this study focuses on capturing regional variations in solar irradiance. The spatially continuous hourly estimates derived from ensemble forecasts effectively reconstruct sub-seasonal patterns on smaller scales. This precise knowledge is crucial for applications such as site selection for solar power plants and understanding regional climate changes. Accurate assessment of solar irradiance enables informed decision-making for renewable energy planning and enhances our understanding of regional climate dynamics. In summary, performance evaluation metrics provide insights into forecast accuracy. Additionally, spatially continuous estimates capture regional variations, enabling precise predictions for renewable energy planning and climate research. Advancing our understanding of solar irradiance patterns contributes to sustainable energy strategies and enhances knowledge of regional climate dynamics.},
year = {2023}
}
TY - JOUR T1 - Assessment of ECMWF Sub-Seasonal Solar Irradiance Forecast over Indo-China Peninsula AU - Junyu Cai AU - Bing Ding AU - Veeranjaneyulu Chinta AU - Hao Chen AU - Peng Wang AU - Jiangfeng Zhang AU - Mingbo Liu AU - Ning Ding AU - Chen Zeng AU - Wei Zhang AU - Guiting Song Y1 - 2023/06/05 PY - 2023 N1 - https://doi.org/10.11648/j.ijsts.20231103.16 DO - 10.11648/j.ijsts.20231103.16 T2 - International Journal of Science, Technology and Society JF - International Journal of Science, Technology and Society JO - International Journal of Science, Technology and Society SP - 130 EP - 134 PB - Science Publishing Group SN - 2330-7420 UR - https://doi.org/10.11648/j.ijsts.20231103.16 AB - Solar irradiance plays a critical role in Earth's energy balance and climate. Accurate sub-seasonal forecasts of surface solar irradiance are essential for various applications, including renewable energy planning and regional climate research. This study evaluates ensemble forecasts of surface solar irradiance using the ECMWF dataset (EC-ENS) with a 6-hourly time-step. We compare these forecasts with gridded observations from the China Meteorological Agency (CMA) over the Indo-China peninsular region. Solar irradiance, as Earth's primary energy source, is influenced by atmospheric conditions, and even minor fluctuations in the sun's energy output can significantly impact the climate. Hence, understanding and predicting solar irradiance variations are crucial. For the analysis, we utilize the EC-ENS model data and gridded observation data available from June 2021 to May 2022, with hourly and 6-hourly intervals. Performance evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE), and mean bias error (MBE), are employed to assess the accuracy of the EC-ENS model against observations. Results show an RMSE of approximately 414.43 W/m², an MAE of 380.95 W/m², and an MBE of -309.72 W/m², providing insights into forecast deviations. Furthermore, this study focuses on capturing regional variations in solar irradiance. The spatially continuous hourly estimates derived from ensemble forecasts effectively reconstruct sub-seasonal patterns on smaller scales. This precise knowledge is crucial for applications such as site selection for solar power plants and understanding regional climate changes. Accurate assessment of solar irradiance enables informed decision-making for renewable energy planning and enhances our understanding of regional climate dynamics. In summary, performance evaluation metrics provide insights into forecast accuracy. Additionally, spatially continuous estimates capture regional variations, enabling precise predictions for renewable energy planning and climate research. Advancing our understanding of solar irradiance patterns contributes to sustainable energy strategies and enhances knowledge of regional climate dynamics. VL - 11 IS - 3 ER -
Information
Email: