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), 2024. Published by Science Publishing Group |
Solar Irradiance, Sub-Seasonal Variability, Forecasting, ECMWF Ensemble Forecast System
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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
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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 -
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