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), 2024. Published by Science Publishing Group

Keywords

Wind Forecast, Ensemble, WRF, Downscaling

References

[1]	Q. Tu, R. Betz, J. Mo, Y. Fan, Y. Liu, Achieving grid parity of wind power in China – Present levelized cost of electricity and future evolution, Applied Energy. 250 (2019) 1053–1064. https://doi.org/10.1016/j.apenergy.2019.05.039.
[2]	X. Xu, D. Niu, B. Xiao, X. Guo, L. Zhang, K. Wang, Policy analysis for grid parity of wind power generation in China, Energy Policy. 138 (2020) 111225. https://doi.org/10.1016/j.enpol.2019.111225.
[3]	J. Zhao, Y. Guo, X. Xiao, J. Wang, D. Chi, Z. Guo, Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method, Applied Energy. 197 (2017) 183–202. https://doi.org/10.1016/j.apenergy.2017.04.017.
[4]	M. Marquis, J. Wilczak, M. Ahlstrom, J. Sharp, A. Stern, J. C. Smith, S. Calvert, Forecasting the Wind to Reach Significant Penetration Levels of Wind Energy, Bulletin of the American Meteorological Society. 92 (2011) 1159–1171. https://doi.org/10.1175/2011BAMS3033.1.
[5]	A. M. Foley, P. G. Leahy, A. Marvuglia, E. J. McKeogh, Current methods and advances in forecasting of wind power generation, Renewable Energy. 37 (2012) 1–8. https://doi.org/10.1016/j.renene.2011.05.033.
[6]	W. Y. Y. Cheng, Y. Liu, A. J. Bourgeois, Y. Wu, S. E. Haupt, Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation, Renewable Energy. 107 (2017) 340–351. https://doi.org/10.1016/j.renene.2017.02.014.
[7]	J. B. Olson, J. S. Kenyon, I. Djalalova, L. Bianco, D. D. Turner, Y. Pichugina, A. Choukulkar, M. D. Toy, J. M. Brown, W. M. Angevine, E. Akish, J.-W. Bao, P. Jimenez, B. Kosovic, K. A. Lundquist, C. Draxl, J. K. Lundquist, J. McCaa, K. McCaffrey, K. Lantz, C. Long, J. Wilczak, R. Banta, M. Marquis, S. Redfern, L. K. Berg, W. Shaw, J. Cline, Improving Wind Energy Forecasting through Numerical Weather Prediction Model Development, Bulletin of the American Meteorological Society. 100 (2019) 2201–2220. https://doi.org/10.1175/BAMS-D-18-0040.1.
[8]	E. N. Lorenz, A study of the predictability of a 28-variable atmospheric model, Tellus. 17 (1965) 321–333. https://doi.org/10.3402/tellusa.v17i3.9076.
[9]	Wind Power Density Forecasting Using Ensemble Predictions and Time Series Models, (n.d.). https://ieeexplore.ieee.org/abstract/document/5224014/ (accessed September 11, 2023).
[10]	W. J. Shaw, L. K. Berg, J. Cline, C. Draxl, I. Djalalova, E. P. Grimit, J. K. Lundquist, M. Marquis, J. McCaa, J. B. Olson, C. Sivaraman, J. Sharp, J. M. Wilczak, The Second Wind Forecast Improvement Project (WFIP2): General Overview, Bulletin of the American Meteorological Society. 100 (2019) 1687–1699. https://doi.org/10.1175/BAMS-D-18-0036.1.
[11]	Z. Pu, E. Kalnay, Numerical Weather Prediction Basics: Models, Numerical Methods, and Data Assimilation, in: Q. Duan, F. Pappenberger, J. Thielen, A. Wood, H. L. Cloke, J. C. Schaake (Eds.), Handbook of Hydrometeorological Ensemble Forecasting, Springer, Berlin, Heidelberg, 2018: pp. 1–31. https://doi.org/10.1007/978-3-642-40457-3_11-1.
[12]	T. Palmer, The primacy of doubt: Evolution of numerical weather prediction from determinism to probability, Journal of Advances in Modeling Earth Systems. 9 (2017) 730–734. https://doi.org/10.1002/2017MS000999.
[13]	R. Buizza, T. N. Palmer, The Singular-Vector Structure of the Atmospheric Global Circulation, J. Atmos. Sci. 52 (1995) 1434–1456. https://doi.org/10.1175/1520-0469(1995)052<1434:TSVSOT>2.0.CO;2.
[14]	F. Molteni, R. Buizza, T. N. Palmer, T. Petroliagis, The ECMWF Ensemble Prediction System: Methodology and validation, Quarterly Journal of the Royal Meteorological Society. 122 (1996) 73–119. https://doi.org/10.1002/qj.49712252905.
[15]	Z. Toth, E. Kalnay, Ensemble Forecasting at NMC: The Generation of Perturbations, Bulletin of the American Meteorological Society. 74 (1993) 2317–2330. https://doi.org/10.1175/1520-0477(1993)074<2317:EFANTG>2.0.CO;2.
[16]	Z. Toth, E. Kalnay, Ensemble Forecasting at NCEP and the Breeding Method, Monthly Weather Review. 125 (1997) 3297–3319. https://doi.org/10.1175/1520-0493(1997)125<3297:EFANAT>2.0.CO;2.
[17]	R. Buizza, P. L. Houtekamer, G. Pellerin, Z. Toth, Y. Zhu, M. Wei, A Comparison of the ECMWF, MSC, and NCEP Global Ensemble Prediction Systems, Monthly Weather Review. 133 (2005) 1076–1097. https://doi.org/10.1175/MWR2905.1.
[18]	L. Magnusson, J.-R. Bidlot, M. Bonavita, A. R. Brown, P. A. Browne, G. D. Chiara, M. Dahoui, S. T. K. Lang, T. McNally, K. S. Mogensen, F. Pappenberger, F. Prates, F. Rabier, D. S. Richardson, F. Vitart, S. Malardel, ECMWF Activities for Improved Hurricane Forecasts, Bulletin of the American Meteorological Society. 100 (2019) 445–458. https://doi.org/10.1175/BAMS-D-18-0044.1.
[19]	X. Zhou, Y. Zhu, D. Hou, Y. Luo, J. Peng, R. Wobus, Performance of the New NCEP Global Ensemble Forecast System in a Parallel Experiment, Weather and Forecasting. 32 (2017) 1989–2004. https://doi.org/10.1175/WAF-D-17-0023.1.
[20]	K. Horvath, A. Bajić, S. Ivatek-Šahdan, Dynamical Downscaling of Wind Speed in Complex Terrain Prone To Bora-Type Flows, Journal of Applied Meteorology and Climatology. 50 (2011) 1676–1691. https://doi.org/10.1175/2011JAMC2638.1.
[21]	N. Marjanovic, S. Wharton, F. K. Chow, Investigation of model parameters for high-resolution wind energy forecasting: Case studies over simple and complex terrain, Journal of Wind Engineering and Industrial Aerodynamics. 134 (2014) 10–24. https://doi.org/10.1016/j.jweia.2014.08.007.
[22]	P. A. Jiménez, J. Dudhia, Improving the Representation of Resolved and Unresolved Topographic Effects on Surface Wind in the WRF Model, Journal of Applied Meteorology and Climatology. 51 (2012) 300–316. https://doi.org/10.1175/JAMC-D-11-084.1.
[23]	F. Weidle, Y. Wang, G. Smet, On the Impact of the Choice of Global Ensemble in Forcing a Regional Ensemble System, Weather and Forecasting. 31 (2016) 515–530. https://doi.org/10.1175/WAF-D-15-0102.1.
[24]	Č. Branković, B. Matjačić, S. Ivatek-Šahdan, R. Buizza, Downscaling of ECMWF Ensemble Forecasts for Cases of Severe Weather: Ensemble Statistics and Cluster Analysis, Monthly Weather Review. 136 (2008) 3323–3342. https://doi.org/10.1175/2008MWR2322.1.
[25]	H. Zhang, M. Chen, S. Fan, Study on the Construction of Initial Condition Perturbations for the Regional Ensemble Prediction System of North China, Atmosphere. 10 (2019) 87. https://doi.org/10.3390/atmos10020087.
[26]	P. A. Jiménez, J. F. González-Rouco, E. García-Bustamante, J. Navarro, J. P. Montávez, J. V.-G. de Arellano, J. Dudhia, A. Muñoz-Roldan, Surface Wind Regionalization over Complex Terrain: Evaluation and Analysis of a High-Resolution WRF Simulation, Journal of Applied Meteorology and Climatology. 49 (2010) 268–287. https://doi.org/10.1175/2009JAMC2175.1.
[27]	P. A. Jiménez, J. Dudhia, On the Ability of the WRF Model to Reproduce the Surface Wind Direction over Complex Terrain, Journal of Applied Meteorology and Climatology. 52 (2013) 1610–1617. https://doi.org/10.1175/JAMC-D-12-0266.1.
[28]	J. Stanger, I. Finney, A. Weisheimer, T. Palmer, Optimising the use of ensemble information in numerical weather forecasts of wind power generation, Environ. Res. Lett. 14 (2019) 124086. https://doi.org/10.1088/1748-9326/ab5e54.
[29]	L. Qi, H. Yu, P. Chen, Selective ensemble-mean technique for tropical cyclone track forecast by using ensemble prediction systems, Quarterly Journal of the Royal Meteorological Society. 140 (2014) 805–813. https://doi.org/10.1002/qj.2196.
[30]	R. Kikuchi, T. Misaka, S. Obayashi, H. Inokuchi, H. Oikawa, A. Misumi, Nowcasting algorithm for wind fields using ensemble forecasting and aircraft flight data, Meteorological Applications. 25 (2018) 365–375. https://doi.org/10.1002/met.1704.
[31]	R. Huva, G. Song, X. Zhong, Y. Zhao, Comprehensive physics testing and adaptive weather research and forecasting physics for day-ahead solar forecasting, Meteorological Applications. 28 (2021) e2017. https://doi.org/10.1002/met.2017.
[32]	S.-Y. Hong, Hongandlim-JKMS-2006, Journal of the Korean Meteorological Society. 42 (2006) 129–151.
[33]	S.-Y. Hong, H.-L. Pan, Nonlocal Boundary Layer Vertical Diffusion in a Medium-Range Forecast Model, Monthly Weather Review. 124 (1996) 2322–2339. https://doi.org/10.1175/1520-0493(1996)124<2322:NBLVDI>2.0.CO;2
[34]	J. E. Pleim, A. Xiu, Development and Testing of a Surface Flux and Planetary Boundary Layer Model for Application in Mesoscale Models, Journal of Applied Meteorology (1988-2005). 34 (1995) 16–32.
[35]	A. Xiu, J. Pleim, Development of a Land Surface Model. Part I: Application in a Mesoscale Meteorological Model, Journal of Applied Meteorology - J APPL METEOROL. 40 (2001) 192–209. https://doi.org/10.1175/1520-0450(2001)040<0192:DOALSM>2.0.CO;2.
[36]	J. S. Kain, The Kain–Fritsch Convective Parameterization: An Update, Journal of Applied Meteorology and Climatology. 43 (2004) 170–181. https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.
[37]	M.-D. Chou, M. J. Suarez, A Solar Radiation Parameterization for Atmospheric Studies, 1999. https://ntrs.nasa.gov/citations/19990060930 (accessed September 8, 2023).
[38]	H. Hersbach, Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems, Weather and Forecasting. 15 (2000) 559–570. https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2.
[39]	J. M. Sloughter, T. Gneiting, A. E. Raftery, Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging, Journal of the American Statistical Association. 105 (2010) 25–35.
[40]	T. M. Hamill, Interpretation of Rank Histograms for Verifying Ensemble Forecasts, Monthly Weather Review. 129 (2001) 550–560. https://doi.org/10.1175/1520-0493(2001)129<0550:IORHFV>2.0.CO;2
[41]	D. S. Wilks, Statistical methods in the atmospheric sciences, Academic Press. (2011).

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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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