Relevance Of Era5 Reanalysis For Wind Energy Applications: Comparison With Sodar Observations

ERA5 reanalysis is one of the most trusted climate data sources for wind energy modeling. However, any reanalysis should be verified through comparison with observational data to detect biases before further use. For wind verification at heights close to typical wind turbine hub heights (i.e. about...

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Main Authors: Anna A. Shestakova, Ekaterina V. Fedotova, Vasily S. Lyulyukin
Other Authors: This study was supported by the Russian Science Foundation (project No. 18-79-10255) (reanalysis verification and correction, capacity factor estimation) and the state assignments of the A.M. Obukhov Institute of Atmospheric Physics RAS № FMWR-2022-0001 (preparation of reanalysis data, analysis of the daily course of reanalysis errors) and № FMWR2022-0017 (sodar data processing).
Format: Article in Journal/Newspaper
Language:English
Published: Russian Geographical Society 2024
Subjects:
Western Siberia
mire
drainage
wildfire
Arctic
Siberia
Online Access:https://ges.rgo.ru/jour/article/view/3328
https://doi.org/10.24057/2071-9388-2023-2782
id ftjges:oai:oai.gesj.elpub.ru:article/3328
record_format openpolar
institution Open Polar
collection Geography, Environment, Sustainability
op_collection_id ftjges
language English
topic Western Siberia
mire
drainage
wildfire
spellingShingle Western Siberia
mire
drainage
wildfire
Anna A. Shestakova
Ekaterina V. Fedotova
Vasily S. Lyulyukin
Relevance Of Era5 Reanalysis For Wind Energy Applications: Comparison With Sodar Observations
topic_facet Western Siberia
mire
drainage
wildfire
description ERA5 reanalysis is one of the most trusted climate data sources for wind energy modeling. However, any reanalysis should be verified through comparison with observational data to detect biases before further use. For wind verification at heights close to typical wind turbine hub heights (i.e. about 100 m), it is preferable to use either in-situ measurements from meteorological towers or remote sensing data like acoustic and laser vertical profilers, which remain independent of reanalysis. In this study, we validated the wind speed data from ERA5 at a height of 100 m using data from four sodars (acoustic profilers) located in different climatic and natural vegetation zones across European Russia. The assessments revealed a systematic error at most stations; in general, ERA5 tends to overestimate wind speed over forests and underestimate it over grasslands and deserts. As anticipated, the largest errors were observed at a station on the mountain coast, where the relative wind speed error reached 45%. We performed the bias correction which reduced absolute errors and eliminated the error dependence on the daily course, which was crucial for wind energy modeling. Without bias correction, the error in the wind power capacity factor ranged from 30 to 50%. Hence, it is strongly recommended to apply correction of ERA5 for energy calculations, at least in the areas under consideration.
author2 This study was supported by the Russian Science Foundation (project No. 18-79-10255) (reanalysis verification and correction
capacity factor estimation) and the state assignments of the A.M. Obukhov Institute of Atmospheric Physics RAS № FMWR-2022-0001 (preparation of reanalysis data
analysis of the daily course of reanalysis errors) and № FMWR2022-0017 (sodar data processing).
format Article in Journal/Newspaper
author Anna A. Shestakova
Ekaterina V. Fedotova
Vasily S. Lyulyukin
author_facet Anna A. Shestakova
Ekaterina V. Fedotova
Vasily S. Lyulyukin
author_sort Anna A. Shestakova
title Relevance Of Era5 Reanalysis For Wind Energy Applications: Comparison With Sodar Observations
title_short Relevance Of Era5 Reanalysis For Wind Energy Applications: Comparison With Sodar Observations
title_full Relevance Of Era5 Reanalysis For Wind Energy Applications: Comparison With Sodar Observations
title_fullStr Relevance Of Era5 Reanalysis For Wind Energy Applications: Comparison With Sodar Observations
title_full_unstemmed Relevance Of Era5 Reanalysis For Wind Energy Applications: Comparison With Sodar Observations
title_sort relevance of era5 reanalysis for wind energy applications: comparison with sodar observations
publisher Russian Geographical Society
publishDate 2024
url https://ges.rgo.ru/jour/article/view/3328
https://doi.org/10.24057/2071-9388-2023-2782
genre Arctic
Siberia
genre_facet Arctic
Siberia
op_source GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY; Vol 17, No 1 (2024); 54-66
2542-1565
2071-9388
op_relation https://ges.rgo.ru/jour/article/view/3328/761
Akperov M.G., Eliseev A.V., Mokhov I.I., Semenov V.A., Parfenova M., Koenigk T. (2022). Wind energy potential in the arctic and subarctic regions and its projected change in the 21st century according to regional climate model simulations. Russian Meteorology and Hydrology, 47(6), 428-426, DOI:10.52002/0130-2906-2022-6-18-29
Akperov M., Eliseev A.V., Rinke A., Mokhov I.I., Semenov V.A., Dembitskaya M., et al. (2023). Future projections of wind energy potentials in the arctic for the 21st century under the RCP8. 5 scenario from regional climate models (Arctic-CORDEX). Anthropocene, V. 44, 100402, ISSN 2213-3054, https://doi.org/10.1016/j.ancene.2023.100402.
Andresen G. B., Søndergaard A. A., and Greiner M. (2015). Validation of Danish wind time series from a new global renewable energy atlas for energy system analysis. Energy, 93, 1074-1088, DOI:10.1016/j.energy.2015.09.071.
Bokde N., Feijoo A., Villanueva D. (2018). Wind turbine power curves based on the Weibull cumulative distribution function. Applied Sciences, 8(10), 1757, DOI:10.3390/app8101757.
Boretti A. and Castelletto S. (2020). Cost of wind energy generation should include energy storage allowance. Scientific Reports, 10, 2978, DOI:10.1038/s41598-020-59936-x.
Çalışır E., Soran M.B., and Akpınar A. (2021). Quality of the ERA5 and CFSR winds and their contribution to wave modelling performance in a semi-closed sea. Journal of Operational Oceanography, 1-25, DOI:10.1080/1755876X.2021.1911126.
Craig M. T., Wohland J., Stoop L. P., Kies A., Pickering B., Bloomfield H. C., et al. (2022). Overcoming the disconnect between energy system and climate modeling. Joule, 6(7), 1405-1417, DOI:10.1016/j.joule.2022.05.010.
Dörenkämper M., Olsen B.T., Witha B., Hahmann A.N., Davis N.N., Barcons J., et al. (2020). The making of the new european wind atlas– part 2: Production and evaluation. Geoscientific model development, 13(10), 5079-5102, DOI:10.5194/gmd-13-5079-2020.
Gualtieri G. (2021). Reliability of ERA5 reanalysis data for wind resource assessment: A comparison against tall towers. Energies, 14(14), 4169, DOI:10.3390/en14144169.
Haas R., Pinto J.G., Born K. (2014). Can dynamically downscaled windstorm footprints be improved by observations through a probabilistic approach? Journal of Geophysical Research: Atmospheres, 119, 713–725, DOI:10.1002/2013JD020882.
Hersbach H., Bell B., Berrisford P., Hirahara S., Horányi A., Muñoz-Sabater J., et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999-2049, DOI:10.1002/qj.3803.
Jourdier B. (2020). Evaluation of ERA5, MERRA-2, COSMO-REA6, NEWA and AROME to simulate wind power production over France. Advances in Science and Research, 17, 63–77, DOI:10.5194/asr-17-63-2020.
Jung C. and Schindler D. (2022). Development of onshore wind turbine fleet counteracts climate change-induced reduction in global capacity factor. Nature Energy, 7, 608–619, DOI:10.1038/s41560-022-01056-z.
Kiseleva S.V., Shestakova A.A., Toropov P.A., and Myslenkov S.A. (2016). Evaluation of wind energy potential of the Black Sea coast using CFSR. Alternative Energy and Ecology (ISJAEE), 15-18, 75-85 (In Russian with English summary), DOI:10.15518/isjaee.2016.15-18.075-085
Kubik M.L., Brayshaw D.J., Coker P.J., and Barlow J.F. (2013). Exploring the role of reanalysis data in simulating regional wind generation variability over Northern Ireland. Renewable energy, 57, 558-561, DOI:10.1016/j.renene.2013.02.012.
Kuznetsov R. D. (2007). Sodar LATAN-3 for atmospheric boundary layer research. Optics of Atmosphere and Ocean, 20(8), 684-687 (in Russian).
Lokoshchenko M.A. (2014). Wind regime in the lower atmosphere over Moscow from the long-term acoustic sounding data. Russian Meteorology and Hydrology, 39, 218–227, DOI:10.3103/S1068373914040025
Li D., Feng J., Xu Z., Yin B., Shi H., and Qi J. (2019). Statistical bias correction for simulated wind speeds over CORDEX-East Asia. Earth and Space Science, 6, 200–211, DOI:10.1029/2018EA000493.
Molina M.O., Gutiérrez C., and Sánchez E. (2021). Comparison of ERA5 surface wind speed climatologies over Europe with observations from the HadISD dataset. International Journal of Climatology, 41(10), 4864-4878, DOI:10.1002/joc.7103.
Olauson J. (2018). ERA5: The new champion of wind power modelling? Renewable energy, 126, 322-331, DOI:10.1016/j.renene.2018.03.056.
Ramon J., Lledó L., Pérez-Zanón N., Soret A., and Doblas-Reyes F. J. (2020). The Tall Tower Dataset: a unique initiative to boost wind energy research. Earth System Science Data, 12(1), 429-439, DOI:10.5194/essd-12-429-2020.
Ramon J., Lledó L., Torralba V., Soret A., and Doblas-Reyes F.J. (2019). What global reanalysis best represents near-surface winds? Quarterly Journal of the Royal Meteorological Society, 145(724), 3236-3251, DOI:10.1002/qj.3616.
Santos J., Sakagami Y., Haas R., Passos J., Machuca M., Radünz W., et al. (2019). Wind speed evaluation of MERRA-2, ERA-interim and ERA5 reanalysis data at a wind farm located in Brazil. In: Proceedings of the ISES Solar World Congress (pp. 1-10), DOI:10.18086/swc.2019.45.10. Available online at http://proceedings.ises.org.
Semenov O.E. (2000). On the flow acceleration during strong sand and dust storms. Hydrometeorology and Ecology, 3-4, 23-48 (in Russian).
Semenov O. E. (2020). Introduction to experimental meteorology and climatology of sandstorms. Dolgoprudny: Fizmatkniga Publishing House (in Russian).
Spravochnik po resursam vozobnovlyaemyh istochnikov energii Rossii i mestnym vidam topliva (pokazateli po territoriyam) (2007).- – M.: «IAC Energiya», 272 p. (in Russian).
Shestakova A.A., Toropov P.A., Stepanenko V.M., Sergeev D.E., and Repina I.A. (2018). Observations and modelling of downslope windstorm in Novorossiysk. Dynamics of Atmospheres and Oceans, 83, 83-99, DOI:10.1016/j.dynatmoce.2018.07.001.
so-ups.ru (2005). System Operator Database. [online] Available at: https://www.so-ups.ru [Accessed 31 Aug. 2022]
Staffell I. and Pfenninger S. (2016). Using bias-corrected reanalysis to simulate current and future wind power output. Energy, 114, 12241239, DOI:10.1016/j.energy.2016.08.068.
talltowers.bsc.es (2023). The Tall Tower Dataset website. [online] Available at https://talltowers.bsc.es/ [Accessed 21 Feb.2023]
Thomas S. R., Nicolau S., Martínez-Alvarado O., Drew D. J., and Bloomfield H. C. (2021). How well do atmospheric reanalyses reproduce observed winds in coastal regions of Mexico. Meteorological Applications, 28(5), e2023, DOI:10.1002/met.2023.
Zilitinkevich S.S. (1972). The dynamics of the atmospheric boundary layer. National Lending Library for Science and Technology
https://ges.rgo.ru/jour/article/view/3328
doi:10.24057/2071-9388-2023-2782
op_rights Authors who publish with this journal agree to the following terms:Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.Authors can enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).The information and opinions presented in the Journal reflect the views of the authors and not of the Journal or its Editorial Board or the Publisher. The GES Journal has used its best endeavors to ensure that the information is correct and current at the time of publication but takes no responsibility for any error, omission, or defect therein.
Авторы, публикующие в данном журнале, соглашаются со следующим:Авторы сохраняют за собой авторские права на работу и предоставляют журналу право первой публикации работы на условиях лицензии Creative Commons Attribution License, которая позволяет другим распространять данную работу с обязательным сохранением ссылок на авторов оригинальной работы и оригинальную публикацию в этом журнале.Авторы сохраняют право заключать отдельные контрактные договорённости, касающиеся не-эксклюзивного распространения версии работы в опубликованном здесь виде (например, размещение ее в институтском хранилище, публикацию в книге), со ссылкой на ее оригинальную публикацию в этом журнале.Авторы имеют право размещать их работу
op_doi https://doi.org/10.24057/2071-9388-2023-278210.52002/0130-2906-2022-6-18-2910.1016/j.ancene.2023.10040210.1016/j.energy.2015.09.07110.3390/app810175710.1038/s41598-020-59936-x10.1080/1755876X.2021.191112610.1016/j.joule.2022.05.01010.5194/gmd-13-5079-2020
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spelling ftjges:oai:oai.gesj.elpub.ru:article/3328 2024-04-28T08:05:10+00:00 Relevance Of Era5 Reanalysis For Wind Energy Applications: Comparison With Sodar Observations Anna A. Shestakova Ekaterina V. Fedotova Vasily S. Lyulyukin This study was supported by the Russian Science Foundation (project No. 18-79-10255) (reanalysis verification and correction capacity factor estimation) and the state assignments of the A.M. Obukhov Institute of Atmospheric Physics RAS № FMWR-2022-0001 (preparation of reanalysis data analysis of the daily course of reanalysis errors) and № FMWR2022-0017 (sodar data processing). 2024-04-03 application/pdf https://ges.rgo.ru/jour/article/view/3328 https://doi.org/10.24057/2071-9388-2023-2782 eng eng Russian Geographical Society https://ges.rgo.ru/jour/article/view/3328/761 Akperov M.G., Eliseev A.V., Mokhov I.I., Semenov V.A., Parfenova M., Koenigk T. (2022). Wind energy potential in the arctic and subarctic regions and its projected change in the 21st century according to regional climate model simulations. Russian Meteorology and Hydrology, 47(6), 428-426, DOI:10.52002/0130-2906-2022-6-18-29 Akperov M., Eliseev A.V., Rinke A., Mokhov I.I., Semenov V.A., Dembitskaya M., et al. (2023). Future projections of wind energy potentials in the arctic for the 21st century under the RCP8. 5 scenario from regional climate models (Arctic-CORDEX). Anthropocene, V. 44, 100402, ISSN 2213-3054, https://doi.org/10.1016/j.ancene.2023.100402. Andresen G. B., Søndergaard A. A., and Greiner M. (2015). Validation of Danish wind time series from a new global renewable energy atlas for energy system analysis. Energy, 93, 1074-1088, DOI:10.1016/j.energy.2015.09.071. Bokde N., Feijoo A., Villanueva D. (2018). Wind turbine power curves based on the Weibull cumulative distribution function. Applied Sciences, 8(10), 1757, DOI:10.3390/app8101757. Boretti A. and Castelletto S. (2020). Cost of wind energy generation should include energy storage allowance. Scientific Reports, 10, 2978, DOI:10.1038/s41598-020-59936-x. Çalışır E., Soran M.B., and Akpınar A. (2021). Quality of the ERA5 and CFSR winds and their contribution to wave modelling performance in a semi-closed sea. Journal of Operational Oceanography, 1-25, DOI:10.1080/1755876X.2021.1911126. Craig M. T., Wohland J., Stoop L. P., Kies A., Pickering B., Bloomfield H. C., et al. (2022). Overcoming the disconnect between energy system and climate modeling. Joule, 6(7), 1405-1417, DOI:10.1016/j.joule.2022.05.010. Dörenkämper M., Olsen B.T., Witha B., Hahmann A.N., Davis N.N., Barcons J., et al. (2020). The making of the new european wind atlas– part 2: Production and evaluation. Geoscientific model development, 13(10), 5079-5102, DOI:10.5194/gmd-13-5079-2020. Gualtieri G. (2021). Reliability of ERA5 reanalysis data for wind resource assessment: A comparison against tall towers. Energies, 14(14), 4169, DOI:10.3390/en14144169. Haas R., Pinto J.G., Born K. (2014). Can dynamically downscaled windstorm footprints be improved by observations through a probabilistic approach? Journal of Geophysical Research: Atmospheres, 119, 713–725, DOI:10.1002/2013JD020882. Hersbach H., Bell B., Berrisford P., Hirahara S., Horányi A., Muñoz-Sabater J., et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999-2049, DOI:10.1002/qj.3803. Jourdier B. (2020). Evaluation of ERA5, MERRA-2, COSMO-REA6, NEWA and AROME to simulate wind power production over France. Advances in Science and Research, 17, 63–77, DOI:10.5194/asr-17-63-2020. Jung C. and Schindler D. (2022). Development of onshore wind turbine fleet counteracts climate change-induced reduction in global capacity factor. Nature Energy, 7, 608–619, DOI:10.1038/s41560-022-01056-z. Kiseleva S.V., Shestakova A.A., Toropov P.A., and Myslenkov S.A. (2016). Evaluation of wind energy potential of the Black Sea coast using CFSR. Alternative Energy and Ecology (ISJAEE), 15-18, 75-85 (In Russian with English summary), DOI:10.15518/isjaee.2016.15-18.075-085 Kubik M.L., Brayshaw D.J., Coker P.J., and Barlow J.F. (2013). Exploring the role of reanalysis data in simulating regional wind generation variability over Northern Ireland. Renewable energy, 57, 558-561, DOI:10.1016/j.renene.2013.02.012. Kuznetsov R. D. (2007). Sodar LATAN-3 for atmospheric boundary layer research. Optics of Atmosphere and Ocean, 20(8), 684-687 (in Russian). Lokoshchenko M.A. (2014). Wind regime in the lower atmosphere over Moscow from the long-term acoustic sounding data. Russian Meteorology and Hydrology, 39, 218–227, DOI:10.3103/S1068373914040025 Li D., Feng J., Xu Z., Yin B., Shi H., and Qi J. (2019). Statistical bias correction for simulated wind speeds over CORDEX-East Asia. Earth and Space Science, 6, 200–211, DOI:10.1029/2018EA000493. Molina M.O., Gutiérrez C., and Sánchez E. (2021). Comparison of ERA5 surface wind speed climatologies over Europe with observations from the HadISD dataset. International Journal of Climatology, 41(10), 4864-4878, DOI:10.1002/joc.7103. Olauson J. (2018). ERA5: The new champion of wind power modelling? Renewable energy, 126, 322-331, DOI:10.1016/j.renene.2018.03.056. Ramon J., Lledó L., Pérez-Zanón N., Soret A., and Doblas-Reyes F. J. (2020). The Tall Tower Dataset: a unique initiative to boost wind energy research. Earth System Science Data, 12(1), 429-439, DOI:10.5194/essd-12-429-2020. Ramon J., Lledó L., Torralba V., Soret A., and Doblas-Reyes F.J. (2019). What global reanalysis best represents near-surface winds? Quarterly Journal of the Royal Meteorological Society, 145(724), 3236-3251, DOI:10.1002/qj.3616. Santos J., Sakagami Y., Haas R., Passos J., Machuca M., Radünz W., et al. (2019). Wind speed evaluation of MERRA-2, ERA-interim and ERA5 reanalysis data at a wind farm located in Brazil. In: Proceedings of the ISES Solar World Congress (pp. 1-10), DOI:10.18086/swc.2019.45.10. Available online at http://proceedings.ises.org. Semenov O.E. (2000). On the flow acceleration during strong sand and dust storms. Hydrometeorology and Ecology, 3-4, 23-48 (in Russian). Semenov O. E. (2020). Introduction to experimental meteorology and climatology of sandstorms. Dolgoprudny: Fizmatkniga Publishing House (in Russian). Spravochnik po resursam vozobnovlyaemyh istochnikov energii Rossii i mestnym vidam topliva (pokazateli po territoriyam) (2007).- – M.: «IAC Energiya», 272 p. (in Russian). Shestakova A.A., Toropov P.A., Stepanenko V.M., Sergeev D.E., and Repina I.A. (2018). Observations and modelling of downslope windstorm in Novorossiysk. Dynamics of Atmospheres and Oceans, 83, 83-99, DOI:10.1016/j.dynatmoce.2018.07.001. so-ups.ru (2005). System Operator Database. [online] Available at: https://www.so-ups.ru [Accessed 31 Aug. 2022] Staffell I. and Pfenninger S. (2016). Using bias-corrected reanalysis to simulate current and future wind power output. Energy, 114, 12241239, DOI:10.1016/j.energy.2016.08.068. talltowers.bsc.es (2023). The Tall Tower Dataset website. [online] Available at https://talltowers.bsc.es/ [Accessed 21 Feb.2023] Thomas S. R., Nicolau S., Martínez-Alvarado O., Drew D. J., and Bloomfield H. C. (2021). How well do atmospheric reanalyses reproduce observed winds in coastal regions of Mexico. Meteorological Applications, 28(5), e2023, DOI:10.1002/met.2023. Zilitinkevich S.S. (1972). The dynamics of the atmospheric boundary layer. National Lending Library for Science and Technology https://ges.rgo.ru/jour/article/view/3328 doi:10.24057/2071-9388-2023-2782 Authors who publish with this journal agree to the following terms:Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.Authors can enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).The information and opinions presented in the Journal reflect the views of the authors and not of the Journal or its Editorial Board or the Publisher. The GES Journal has used its best endeavors to ensure that the information is correct and current at the time of publication but takes no responsibility for any error, omission, or defect therein. Авторы, публикующие в данном журнале, соглашаются со следующим:Авторы сохраняют за собой авторские права на работу и предоставляют журналу право первой публикации работы на условиях лицензии Creative Commons Attribution License, которая позволяет другим распространять данную работу с обязательным сохранением ссылок на авторов оригинальной работы и оригинальную публикацию в этом журнале.Авторы сохраняют право заключать отдельные контрактные договорённости, касающиеся не-эксклюзивного распространения версии работы в опубликованном здесь виде (например, размещение ее в институтском хранилище, публикацию в книге), со ссылкой на ее оригинальную публикацию в этом журнале.Авторы имеют право размещать их работу GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY; Vol 17, No 1 (2024); 54-66 2542-1565 2071-9388 Western Siberia mire drainage wildfire info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2024 ftjges https://doi.org/10.24057/2071-9388-2023-278210.52002/0130-2906-2022-6-18-2910.1016/j.ancene.2023.10040210.1016/j.energy.2015.09.07110.3390/app810175710.1038/s41598-020-59936-x10.1080/1755876X.2021.191112610.1016/j.joule.2022.05.01010.5194/gmd-13-5079-2020 2024-04-08T00:11:04Z ERA5 reanalysis is one of the most trusted climate data sources for wind energy modeling. However, any reanalysis should be verified through comparison with observational data to detect biases before further use. For wind verification at heights close to typical wind turbine hub heights (i.e. about 100 m), it is preferable to use either in-situ measurements from meteorological towers or remote sensing data like acoustic and laser vertical profilers, which remain independent of reanalysis. In this study, we validated the wind speed data from ERA5 at a height of 100 m using data from four sodars (acoustic profilers) located in different climatic and natural vegetation zones across European Russia. The assessments revealed a systematic error at most stations; in general, ERA5 tends to overestimate wind speed over forests and underestimate it over grasslands and deserts. As anticipated, the largest errors were observed at a station on the mountain coast, where the relative wind speed error reached 45%. We performed the bias correction which reduced absolute errors and eliminated the error dependence on the daily course, which was crucial for wind energy modeling. Without bias correction, the error in the wind power capacity factor ranged from 30 to 50%. Hence, it is strongly recommended to apply correction of ERA5 for energy calculations, at least in the areas under consideration. Article in Journal/Newspaper Arctic Siberia Geography, Environment, Sustainability

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