Comparisons of the Added Value of Dynamical Downscaling of ECMWF EPS and NCEP GEFS for Wind Forecast in the Complex Terrain of Sichuan and Yunnan in China<br />
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Zifen Han; Bolin Zhang; Jianmei Zhang; Jie Long; Xiaohui Zhong

doi:doi:10.11648/j.ijeee.20230805.11

Research Article |

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Comparisons of the Added Value of Dynamical Downscaling of ECMWF EPS and NCEP GEFS for Wind Forecast in the Complex Terrain of Sichuan and Yunnan in China

Zifen Han, Bolin Zhang, Jianmei Zhang, Jie Long, Xiaohui Zhong

Published in International Journal of Economy, Energy and Environment (Volume 8, Issue 5)

Received: 15 September 2023 Accepted: 24 October 2023 Published: 28 October 2023

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Abstract

Numerical weather prediction (NWP) models are commonly used for wind power forecasts, but NWP forecasts are uncertain due to uncertainties in the initial conditions, approximate model physics, and the chaotic nature of the atmosphere. Ensemble prediction systems (EPS), which simulate multiple possible futures, thus provide valuable information about forecast uncertainties. However, the spatial resolution of global ensemble forecasts from the European Centre for Medium-range Weather Forecast (ECMWF) and the National Centers for Environmental Prediction (NCEP) is relatively coarse and insufficient for many wind power farms built in complex terrain. This work proposes using the Weather and Research Forecasting model (WRF) to downscale ECMWF EPS and NCEP global ensemble forecast system (GEFS) to determine and compare the added values of downscaling different global EPS forecasts for wind forecasts in the complex terrain of Sichuan and Yunnan in China. A total of 366 days of day-ahead forecasts (28 to 51 hours) for wind speed at 80 meters are evaluated. The results demonstrate that the ensemble average of the higher resolution WRF downscaled forecast is considerably better than that of the global EPS forecast, and downscaled forecast of ECMWF EPS achieves the best performance. Also, a selective ensemble average (SEA) method is proposed and applied for the ultra-short (10 to 13 hours) forecast. Verification results demonstrate that the SEA method outperforms the ensemble mean.

Published in	International Journal of Economy, Energy and Environment (Volume 8, Issue 5)
DOI	10.11648/j.ijeee.20230805.11
Page(s)	104-112
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

Keywords

Wind Forecast, Ensemble, WRF, Downscaling

References

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APA Style

Zifen Han, Bolin Zhang, Jianmei Zhang, Jie Long, Xiaohui Zhong. (2023). Comparisons of the Added Value of Dynamical Downscaling of ECMWF EPS and NCEP GEFS for Wind Forecast in the Complex Terrain of Sichuan and Yunnan in China . International Journal of Economy, Energy and Environment, 8(5), 104-112. https://doi.org/10.11648/j.ijeee.20230805.11

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ACS Style

Zifen Han; Bolin Zhang; Jianmei Zhang; Jie Long; Xiaohui Zhong. Comparisons of the Added Value of Dynamical Downscaling of ECMWF EPS and NCEP GEFS for Wind Forecast in the Complex Terrain of Sichuan and Yunnan in China . Int. J. Econ. Energy Environ. 2023, 8(5), 104-112. doi: 10.11648/j.ijeee.20230805.11

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AMA Style

Zifen Han, Bolin Zhang, Jianmei Zhang, Jie Long, Xiaohui Zhong. Comparisons of the Added Value of Dynamical Downscaling of ECMWF EPS and NCEP GEFS for Wind Forecast in the Complex Terrain of Sichuan and Yunnan in China . Int J Econ Energy Environ. 2023;8(5):104-112. doi: 10.11648/j.ijeee.20230805.11

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@article{10.11648/j.ijeee.20230805.11,
  author = {Zifen Han and Bolin Zhang and Jianmei Zhang and Jie Long and Xiaohui Zhong},
  title = {Comparisons of the Added Value of Dynamical Downscaling of ECMWF EPS and NCEP GEFS for Wind Forecast in the Complex Terrain of Sichuan and Yunnan in China

	
},
  journal = {International Journal of Economy, Energy and Environment},
  volume = {8},
  number = {5},
  pages = {104-112},
  doi = {10.11648/j.ijeee.20230805.11},
  url = {https://doi.org/10.11648/j.ijeee.20230805.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijeee.20230805.11},
  abstract = {Numerical weather prediction (NWP) models are commonly used for wind power forecasts, but NWP forecasts are uncertain due to uncertainties in the initial conditions, approximate model physics, and the chaotic nature of the atmosphere. Ensemble prediction systems (EPS), which simulate multiple possible futures, thus provide valuable information about forecast uncertainties. However, the spatial resolution of global ensemble forecasts from the European Centre for Medium-range Weather Forecast (ECMWF) and the National Centers for Environmental Prediction (NCEP) is relatively coarse and insufficient for many wind power farms built in complex terrain. This work proposes using the Weather and Research Forecasting model (WRF) to downscale ECMWF EPS and NCEP global ensemble forecast system (GEFS) to determine and compare the added values of downscaling different global EPS forecasts for wind forecasts in the complex terrain of Sichuan and Yunnan in China. A total of 366 days of day-ahead forecasts (28 to 51 hours) for wind speed at 80 meters are evaluated. The results demonstrate that the ensemble average of the higher resolution WRF downscaled forecast is considerably better than that of the global EPS forecast, and downscaled forecast of ECMWF EPS achieves the best performance. Also, a selective ensemble average (SEA) method is proposed and applied for the ultra-short (10 to 13 hours) forecast. Verification results demonstrate that the SEA method outperforms the ensemble mean.
},
 year = {2023}
}

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TY  - JOUR
T1  - Comparisons of the Added Value of Dynamical Downscaling of ECMWF EPS and NCEP GEFS for Wind Forecast in the Complex Terrain of Sichuan and Yunnan in China

	

AU  - Zifen Han
AU  - Bolin Zhang
AU  - Jianmei Zhang
AU  - Jie Long
AU  - Xiaohui Zhong
Y1  - 2023/10/28
PY  - 2023
N1  - https://doi.org/10.11648/j.ijeee.20230805.11
DO  - 10.11648/j.ijeee.20230805.11
T2  - International Journal of Economy, Energy and Environment
JF  - International Journal of Economy, Energy and Environment
JO  - International Journal of Economy, Energy and Environment
SP  - 104
EP  - 112
PB  - Science Publishing Group
SN  - 2575-5021
UR  - https://doi.org/10.11648/j.ijeee.20230805.11
AB  - Numerical weather prediction (NWP) models are commonly used for wind power forecasts, but NWP forecasts are uncertain due to uncertainties in the initial conditions, approximate model physics, and the chaotic nature of the atmosphere. Ensemble prediction systems (EPS), which simulate multiple possible futures, thus provide valuable information about forecast uncertainties. However, the spatial resolution of global ensemble forecasts from the European Centre for Medium-range Weather Forecast (ECMWF) and the National Centers for Environmental Prediction (NCEP) is relatively coarse and insufficient for many wind power farms built in complex terrain. This work proposes using the Weather and Research Forecasting model (WRF) to downscale ECMWF EPS and NCEP global ensemble forecast system (GEFS) to determine and compare the added values of downscaling different global EPS forecasts for wind forecasts in the complex terrain of Sichuan and Yunnan in China. A total of 366 days of day-ahead forecasts (28 to 51 hours) for wind speed at 80 meters are evaluated. The results demonstrate that the ensemble average of the higher resolution WRF downscaled forecast is considerably better than that of the global EPS forecast, and downscaled forecast of ECMWF EPS achieves the best performance. Also, a selective ensemble average (SEA) method is proposed and applied for the ultra-short (10 to 13 hours) forecast. Verification results demonstrate that the SEA method outperforms the ensemble mean.

VL  - 8
IS  - 5
ER  -

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Author Information

Zifen Han

State Grid Gansu Electric Power Company, Lanzhou, China
Bolin Zhang

State Grid Gansu Electric Power Company, Lanzhou, China
Jianmei Zhang

School of Information Science and Engineering, Lanzhou University, Lanzhou, China
Jie Long

State Grid Gansu Electric Power Company, Lanzhou, China
Xiaohui Zhong

Envision Digital International Pte Ltd, Singapore

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Plain Text BibTeX RIS

APA Style

Zifen Han, Bolin Zhang, Jianmei Zhang, Jie Long, Xiaohui Zhong. (2023). Comparisons of the Added Value of Dynamical Downscaling of ECMWF EPS and NCEP GEFS for Wind Forecast in the Complex Terrain of Sichuan and Yunnan in China . International Journal of Economy, Energy and Environment, 8(5), 104-112. https://doi.org/10.11648/j.ijeee.20230805.11

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ACS Style

Zifen Han; Bolin Zhang; Jianmei Zhang; Jie Long; Xiaohui Zhong. Comparisons of the Added Value of Dynamical Downscaling of ECMWF EPS and NCEP GEFS for Wind Forecast in the Complex Terrain of Sichuan and Yunnan in China . Int. J. Econ. Energy Environ. 2023, 8(5), 104-112. doi: 10.11648/j.ijeee.20230805.11

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AMA Style

Zifen Han, Bolin Zhang, Jianmei Zhang, Jie Long, Xiaohui Zhong. Comparisons of the Added Value of Dynamical Downscaling of ECMWF EPS and NCEP GEFS for Wind Forecast in the Complex Terrain of Sichuan and Yunnan in China . Int J Econ Energy Environ. 2023;8(5):104-112. doi: 10.11648/j.ijeee.20230805.11

Copy | Download

@article{10.11648/j.ijeee.20230805.11,
  author = {Zifen Han and Bolin Zhang and Jianmei Zhang and Jie Long and Xiaohui Zhong},
  title = {Comparisons of the Added Value of Dynamical Downscaling of ECMWF EPS and NCEP GEFS for Wind Forecast in the Complex Terrain of Sichuan and Yunnan in China

	
},
  journal = {International Journal of Economy, Energy and Environment},
  volume = {8},
  number = {5},
  pages = {104-112},
  doi = {10.11648/j.ijeee.20230805.11},
  url = {https://doi.org/10.11648/j.ijeee.20230805.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijeee.20230805.11},
  abstract = {Numerical weather prediction (NWP) models are commonly used for wind power forecasts, but NWP forecasts are uncertain due to uncertainties in the initial conditions, approximate model physics, and the chaotic nature of the atmosphere. Ensemble prediction systems (EPS), which simulate multiple possible futures, thus provide valuable information about forecast uncertainties. However, the spatial resolution of global ensemble forecasts from the European Centre for Medium-range Weather Forecast (ECMWF) and the National Centers for Environmental Prediction (NCEP) is relatively coarse and insufficient for many wind power farms built in complex terrain. This work proposes using the Weather and Research Forecasting model (WRF) to downscale ECMWF EPS and NCEP global ensemble forecast system (GEFS) to determine and compare the added values of downscaling different global EPS forecasts for wind forecasts in the complex terrain of Sichuan and Yunnan in China. A total of 366 days of day-ahead forecasts (28 to 51 hours) for wind speed at 80 meters are evaluated. The results demonstrate that the ensemble average of the higher resolution WRF downscaled forecast is considerably better than that of the global EPS forecast, and downscaled forecast of ECMWF EPS achieves the best performance. Also, a selective ensemble average (SEA) method is proposed and applied for the ultra-short (10 to 13 hours) forecast. Verification results demonstrate that the SEA method outperforms the ensemble mean.
},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Comparisons of the Added Value of Dynamical Downscaling of ECMWF EPS and NCEP GEFS for Wind Forecast in the Complex Terrain of Sichuan and Yunnan in China

	

AU  - Zifen Han
AU  - Bolin Zhang
AU  - Jianmei Zhang
AU  - Jie Long
AU  - Xiaohui Zhong
Y1  - 2023/10/28
PY  - 2023
N1  - https://doi.org/10.11648/j.ijeee.20230805.11
DO  - 10.11648/j.ijeee.20230805.11
T2  - International Journal of Economy, Energy and Environment
JF  - International Journal of Economy, Energy and Environment
JO  - International Journal of Economy, Energy and Environment
SP  - 104
EP  - 112
PB  - Science Publishing Group
SN  - 2575-5021
UR  - https://doi.org/10.11648/j.ijeee.20230805.11
AB  - Numerical weather prediction (NWP) models are commonly used for wind power forecasts, but NWP forecasts are uncertain due to uncertainties in the initial conditions, approximate model physics, and the chaotic nature of the atmosphere. Ensemble prediction systems (EPS), which simulate multiple possible futures, thus provide valuable information about forecast uncertainties. However, the spatial resolution of global ensemble forecasts from the European Centre for Medium-range Weather Forecast (ECMWF) and the National Centers for Environmental Prediction (NCEP) is relatively coarse and insufficient for many wind power farms built in complex terrain. This work proposes using the Weather and Research Forecasting model (WRF) to downscale ECMWF EPS and NCEP global ensemble forecast system (GEFS) to determine and compare the added values of downscaling different global EPS forecasts for wind forecasts in the complex terrain of Sichuan and Yunnan in China. A total of 366 days of day-ahead forecasts (28 to 51 hours) for wind speed at 80 meters are evaluated. The results demonstrate that the ensemble average of the higher resolution WRF downscaled forecast is considerably better than that of the global EPS forecast, and downscaled forecast of ECMWF EPS achieves the best performance. Also, a selective ensemble average (SEA) method is proposed and applied for the ultra-short (10 to 13 hours) forecast. Verification results demonstrate that the SEA method outperforms the ensemble mean.

VL  - 8
IS  - 5
ER  -

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