Articles | Volume 13, issue 5
https://doi.org/10.5194/essd-13-2259-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-2259-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
26 May 2021
Data description paper |
| 26 May 2021
| 26 May 2021
Sub-seasonal forecasts of demand and wind power and solar power generation for 28 European countries
Hannah C. Bloomfield
CORRESPONDING AUTHOR
University of Reading, Reading, UK
David J. Brayshaw
University of Reading, Reading, UK
National Centre for Atmospheric Science, Reading, UK
Paula L. M. Gonzalez
University of Reading, Reading, UK
National Centre for Atmospheric Science, Reading, UK
International Research Institute for Climate and Society, The Earth Institute, Columbia University, Palisades, New York, USA
Andrew Charlton-Perez
University of Reading, Reading, UK
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Cited articles
Arnal, L., Cloke, H. L., Stephens, E., Wetterhall, F., Prudhomme, C., Neumann, J., Krzeminski, B., and Pappenberger, F.: Skilful seasonal forecasts of streamflow over Europe?, Hydrol. Earth Syst. Sci., 22, 2057–2072, https://doi.org/10.5194/hess-22-2057-2018, 2018. a
Beerli, R., Wernli, H., and Grams, C. M.: Does the lower stratosphere provide
predictability for month-ahead wind electricity generation in Europe?,
Q. J. Roy. Meteor. Soc., 143, 3025–3036, 2017. a
Bessec, M. and Fouquau, J.: The non-linear link between electricity consumption
and temperature in Europe: A threshold panel approach, Energy Economics, 30,
2705–2721, 2008. a
Bett, P., Thornton, H. E., Troccoli, A., De Felice, M., Suckling, E., Dubus,
L., Saint-Drenan, Y.-M., and Brayshaw, D. J.: A simplified seasonal
forecasting strategy, applied to wind and solar power in Europe, EarthArXiv, https://doi.org/10.31223/osf.io/kzwqx, 2019. a, b
Bloomfield, H. C., Brayshaw, D. D., and Charlton-Perez, A.: ERA5 derived time
series of hourly European country-aggregate electricity demand, wind power
generation and solar power generation, University of Reading Research Data Archive,
https://doi.org/10.17864/1947.273, 2020a. a, b, c
Bloomfield, H. C., Brayshaw, D. J., Shaffrey, L. C., Coker, P. J., and
Thornton, H.: Quantifying the increasing sensitivity of power systems to
climate variability, Environ. Res. Lett., 11, 124025, https://doi.org/10.1088/1748-9326/11/12/124025/, 2016. a
Bloomfield, H. C., Brayshaw, D. J., and Charlton-Perez, A. J.: Characterizing
the winter meteorological drivers of the European electricity system using
targeted circulation types, Meteorol. Appl., 27, 1–18, https://doi.org/10.1002/met.1858,
2020b. a, b, c, d
Bossavy, A., Girard, R., and Kariniotakis, G.: Forecasting ramps of wind power
production with numerical weather prediction ensembles, Wind Energy, 16,
51–63, https://doi.org/10.1002/we.526, 2013. a
Browell, J., Drew, D. R., and Philippopoulos, K.: Improved very short-term
spatio-temporal wind forecasting using atmospheric regimes, Wind Energy, 21,
968–979, 2018. a
Brown, T. A.: Admissible scoring systems for continuous distributions, The Rand Corporation, Santa Monica, California, USA, 1–27, 1974. a
Copernicus Climate Data Store,
available at: https://cds.climate.copernicus.eu/cdsapp#!/home, last access: 1 October 2020. a
Clark, R. T., Bett, P. E., Thornton, H. E., and Scaife, A. A.: Skilful seasonal
predictions for the European energy industry, Environ. Res. Lett.,
12, 024002, https://doi.org/10.1088/1748-9326/aa57ab, 2017. a
Coelho, C. A., Brown, B., Wilson, L., Mittermaier, M., and Casati, B.: Forecast
Verification for S2S Timescales, in: Sub-Seasonal to Seasonal Prediction,
Elsevier, 337–361, 2019. a
De Felice, M., Alessandri, A., and Catalano, F.: Seasonal climate forecasts for
medium-term electricity demand forecasting, Appl. Energ., 137, 435–444,
2015. a
Dorrington, J., Finney, I., Palmer, T., and Weisheimer, A.: Beyond skill
scores: exploring sub-seasonal forecast value through a case study of French
month-ahead energy prediction, arXiv [preprint], arXiv:2002.01728, 2020. a
Drew, D. R., Cannon, D. J., Barlow, J. F., Coker, P. J., and Frame, T. H.: The
importance of forecasting regional wind power ramping: A case study for the
UK, Renew. Energ., 114, 1201–1208, 2017. a
Epstein, E. S.: A scoring system for probability forecasts of ranked
categories, J. Appl. Meteorol., 8, 985–987, 1969. a
Ferro, C. A., Richardson, D. S., and Weigel, A. P.: On the effect of ensemble
size on the discrete and continuous ranked probability scores, Meteorol.
Appl., 15, 19–24, 2008. a
Füss, R., Mahringer, S., and Prokopczuk, M.: Electricity derivatives
pricing with forward-looking information, J. Econ. Dyn.
Control, 58, 34–57, 2015. a
Gonzalez, P. L., Bloomfield, H. C., Brayshaw, D. J., and Charlton-Perez, A.:
Sub-seasonal forecasts of European electricity demand, wind power and solar
power generation, University of Reading Research Data Archive, https://doi.org/10.17864/1947.275, 2020. a, b, c
Goodess, C., Troccoli, A., Acton, C., Añel, J., Bett, P., Brayshaw, D.,
De Felice, M., Dorling, S., Dubus, L., Penny, L., Percy, B., Ranchin, T., Thomas, C., Trolliet, M., and Wald, L.: Advancing climate
services for the European renewable energy sector through capacity building
and user engagement, Climate services, 16, 100139, https://doi.org/10.1016/j.cliser.2019.100193, 2019. a
GWA: The Global Wind Atlas, available at: https://globalwindatlas.info/ (last access: 27 November 2018),
2018. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy.
Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Li, S. and Robertson, A. W.: Evaluation of submonthly precipitation forecast
skill from global ensemble prediction systems, Monthly Weather Review, 143,
2871–2889, 2015. a
Lledó, L. and Doblas-Reyes, F. J.: Predicting daily mean wind speed in
Europe weeks ahead from MJO status, Mon. Weather Rev., 148, 3413–3426,
2020. a
Lynch, K. J.: Subseasonal weather forecasting for the energy sector, PhD
thesis, University of Reading, 2017. a
Lynch, K. J., Brayshaw, D. J., and Charlton-Perez, A.: Verification of European
subseasonal wind speed forecasts, Mon. Weather Rev., 142, 2978–2990,
2014. a
Monhart, S., Spirig, C., Bhend, J., Bogner, K., Schär, C., and Liniger,
M. A.: Skill of subseasonal forecasts in Europe: Effect of bias correction
and downscaling using surface observations, J. Geophys. Re.-Atmos., 123, 7999–8016, 2018. a
Prior, J. and Kendon, M.: The UK winter of 2009/2010 compared with severe
winters of the last 100 years, Weather, 66, 4–10, https://doi.org/10.1002/wea.735, 2011. a
Roberts, J. F., Champion, A. J., Dawkins, L. C., Hodges, K. I., Shaffrey, L. C., Stephenson, D. B., Stringer, M. A., Thornton, H. E., and Youngman, B. D.: The XWS open access catalogue of extreme European windstorms from 1979 to 2012, Nat. Hazards Earth Syst. Sci., 14, 2487–2501, https://doi.org/10.5194/nhess-14-2487-2014, 2014.
a
Sharp, E., Dodds, P., Barrett, M., and Spataru, C.: Evaluating the accuracy of
CFSR reanalysis hourly wind speed forecasts for the UK, using in situ
measurements and geographical information, Renew. Energ., 77, 527–538,
2015. a
Soret, A., Torralba, V., Cortesi, N., Christel, I., Palma, L.,
Manrique-Suñén, A., Lledó, L., González-Reviriego, N., and
Doblas-Reyes, F. J.: Sub-seasonal to seasonal climate predictions for wind
energy forecasting, in: Journal of Physics: Conference Series, vol. 1222, p.
012009, IOP Publishing, 2019. a, b, c
Spinoni, J., Vogt, J. V., Barbosa, P., Dosio, A., McCormick, N., Bigano, A.,
and Füssel, H.-M.: Changes of heating and cooling degree-days in Europe
from 1981 to 2100, Int. J. Climatol., 38, e191–e208,
2018. a
Stanger, J., Finney, I., Weisheimer, A., and Palmer, T.: Optimising the use of
ensemble information in numerical weather forecasts of wind power generation,
Environ. Res. Lett., 14, 124086, https://doi.org/10.1088/1748-9326/ab5e54, 2019. a
Thornton, H. E., Scaife, A., Hoskins, B., Brayshaw, D., Smith, D., Dunstone,
N., Stringer, N., and Bett, P. E.: Skilful seasonal prediction of winter gas
demand, Environ. Res. Lett., 14, 024009, https://doi.org/10.1088/1748-9326/aaf338, 2019. a
Vitart, F., Ardilouze, C., Bonet, A., Brookshaw, A., Chen, M., Codorean, C.,
Déqué, M., Ferranti, L., Fucile, E., Fuentes, M., Hendon, H., Hodgson, J., Kang, H.-S., Kumar, A., Lin, H., Liu, G., Liu, X., Malguzzi, P., Mallas, I., Manoussakis, M., Mastrangelo, D., MacLachlan, C., McLean, P., Minami, A., Mladek, R., Nakazawa, T., Najm, S., Nie, Y., Rixen, M., Robertson, A. W., Ruti, P., Sun, C., Takaya, Y., Tolstykh, M., Venuti, F., Waliser, D., Woolnough, S., Wu, T., Won, D.-J., Xiao, H., Zaripov, R., and Zhang, L.: The
subseasonal to seasonal (S2S) prediction project database, B.
Am. Meteorol. Soc., 98, 163–173, 2017. a, b
Weigel, A. P., Baggenstos, D., Liniger, M. A., Vitart, F., and Appenzeller, C.:
Probabilistic verification of monthly temperature forecasts, Mon. Weather
Rev., 136, 5162–5182, 2008. a
White, C. J., Carlsen, H., Robertson, A. W., Klein, R. J., Lazo, J. K., Kumar,
A., Vitart, F., Coughlan de Perez, E., Ray, A. J., Murray, V., Bharwani, S., MacLeod, D., James, R., Fleming, L., Morse, A. P., Eggen, B., Graham, R., Kjellström, E., Becker, E., Pegion, K. V., Holbrook, N. J., McEvoy, D., Depledge, M., Perkins‐Kirkpatrick, S., Brown, T. J., Street, R., Jones, L., Remenyi, T. A., Hodgson‐Johnston, I., Buontempo, C., Lamb, R., Meinke, H., Arheimer, B., and Zebiak, S. E.:
Potential applications of subseasonal-to-seasonal (S2S) predictions,
Meteorol. Appl., 24, 315–325, https://doi.org/10.1002/met.1654, 2017. a
Short summary
Energy systems are becoming more exposed to weather as more renewable generation is built. This means access to high-quality weather forecasts is becoming more important. This paper showcases past forecasts of electricity demand and wind power and solar power generation across 28 European countries. The timescale of interest is from 5 d out to 1 month ahead. This paper highlights the recent improvements in forecast skill and hopes to promote collaboration in the energy–meteorology community.
Energy systems are becoming more exposed to weather as more renewable generation is built. This...
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