Articles | Volume 6, issue 5
https://doi.org/10.5194/wes-6-1205-2021
© 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/wes-6-1205-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Sep 2021
Research article |
| 16 Sep 2021
| 16 Sep 2021
The smoother the better? A comparison of six post-processing methods to improve short-term offshore wind power forecasts in the Baltic Sea
Christoffer Hallgren
CORRESPONDING AUTHOR
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Stefan Ivanell
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Heiner Körnich
Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
Ville Vakkari
Finnish Meteorological Institute, Helsinki, Finland
Atmospheric Chemistry Research Group, Chemical Resource Beneficiation, North-West University, Potchefstroom, South Africa
Erik Sahlée
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
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Measurements of the multi-wavelength Raman polarization lidar PollyXT and a Halo Photonics StreamLine Doppler lidar have been combined with measurements of pollen type and concentration using a traditional pollen trap at the rural forest site in Vehmasmäki, Finland. Depolarization ratios were measured at three wavelengths. High depolarization ratios were detected during an event with high birch and spruce pollen concentrations and a wavelength dependence of the depolarization ratio was observed.
Ville Vakkari, Holger Baars, Stephanie Bohlmann, Johannes Bühl, Mika Komppula, Rodanthi-Elisavet Mamouri, and Ewan James O'Connor
Atmos. Chem. Phys., 21, 5807–5820, https://doi.org/10.5194/acp-21-5807-2021, https://doi.org/10.5194/acp-21-5807-2021, 2021
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The depolarization ratio is a valuable parameter for aerosol categorization from remote sensing measurements. Here, we introduce particle depolarization ratio measurements at the 1565 nm wavelength, which is substantially longer than previously utilized wavelengths and enhances our capabilities to study the wavelength dependency of the particle depolarization ratio.
Evgenia Belova, Peter Voelger, Sheila Kirkwood, Susanna Hagelin, Magnus Lindskog, Heiner Körnich, Sourav Chatterjee, and Karathazhiyath Satheesan
Atmos. Meas. Tech., 14, 2813–2825, https://doi.org/10.5194/amt-14-2813-2021, https://doi.org/10.5194/amt-14-2813-2021, 2021
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We validate horizontal wind measurements at altitudes of 0.5–14 km made with atmospheric radars: ESRAD located near Kiruna in the Swedish Arctic and MARA at the Indian research station Maitri in Antarctica, by comparison with radiosondes, the regional model HARMONIE-AROME and the ECMWF ERA5 reanalysis. Good agreement was found in general, and radar bias and uncertainty were estimated. These radars are planned to be used for validation of winds measured by lidar by the ESA satellite Aeolus.
David Brus, Jani Gustafsson, Ville Vakkari, Osku Kemppinen, Gijs de Boer, and Anne Hirsikko
Atmos. Chem. Phys., 21, 517–533, https://doi.org/10.5194/acp-21-517-2021, https://doi.org/10.5194/acp-21-517-2021, 2021
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This paper summarizes Finnish Meteorological Institute and Kansas State University unmanned aerial vehicle measurements during the summer 2018 Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) campaign in the San Luis Valley, providing an overview of the rotorcraft deployed, payloads, scientific goals and flight strategies and presenting observations of atmospheric thermodynamics and aerosol and gas parameters in the vertical column.
Marta Wenta, David Brus, Konstantinos Doulgeris, Ville Vakkari, and Agnieszka Herman
Earth Syst. Sci. Data, 13, 33–42, https://doi.org/10.5194/essd-13-33-2021, https://doi.org/10.5194/essd-13-33-2021, 2021
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Representations of the atmospheric boundary layer over sea ice are a challenge for numerical weather prediction models. To increase our understanding of the relevant processes, a field campaign was carried out over the sea ice in the Baltic Sea from 27 February to 2 March 2020. Observations included 27 unmanned aerial vehicle flights, four photogrammetry missions, and shore-based automatic weather station and lidar wind measurements. The dataset obtained is used to validate model results.
Søren Juhl Andersen, Simon-Philippe Breton, Björn Witha, Stefan Ivanell, and Jens Nørkær Sørensen
Wind Energ. Sci., 5, 1689–1703, https://doi.org/10.5194/wes-5-1689-2020, https://doi.org/10.5194/wes-5-1689-2020, 2020
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The complexity of wind farm operation increases as the wind farms get larger and larger. Therefore, researchers from three universities have simulated numerous different large wind farms as part of an international benchmark. The study shows how simple engineering models can capture the general trends, but high-fidelity simulations are required in order to quantify the variability and uncertainty associated with power production of the wind farms and hence the potential profitability and risks.
Cited articles
Bengtsson, L., Andrae, U., Aspelien, T., Batrak, Y., Calvo, J., de Rooy, W., Gleeson, E., Hansen-Sass, B., Homleid, M., Hortal, M., Ivarsson, K.-I., Lenderink, G., Niemelä, S., Pagh Nielsen, K., Onvlee, J., Rontu, L., Samuelsson, P., Santos Muñoz, D., Subias, A., Tijm, S., Toll, V., Yang, X., and Ødegaard Køltzow, M.: The HARMONIE–AROME model configuration in the ALADIN–HIRLAM NWP
system, Mon. Weather Rev., 145, 1919–1935,
https://doi.org/10.1175/MWR-D-16-0417.1, 2017. a, b
Bishop, I. D. and Miller, D. R.: Visual assessment of off-shore wind turbines:
The influence of distance, contrast, movement and social variables, Renew. Energ., 32, 814–831, https://doi.org/10.1016/j.renene.2006.03.009, 2007. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001. a
Brodlie, K. and Butt, S.: Preserving convexity using piecewise cubic
interpolation, Comput. Graph., 15, 15–23,
https://doi.org/10.1016/0097-8493(91)90026-e, 1991. a
Butler, J. N.: Assessment of complex wind turbine wake flow using scanning wind
lidar measurements and numerical analysis techniques, PhD thesis,
University of Strathclyde, Glasgow, 2014. a
Cai, J., Luo, J., Wang, S., and Yang, S.: Feature selection in machine
learning: A new perspective, Neurocomputing, 300, 70–79,
https://doi.org/10.1016/j.neucom.2017.11.077, 2018. a, b
Demuzere, M., Werner, M., Van Lipzig, N., and Roeckner, E.: An analysis of
present and future ECHAM5 pressure fields using a classification of
circulation patterns, Int. J. Climatol., 29, 1796–1810, https://doi.org/10.1002/joc.1821, 2009. a, b
Díaz, H. and Soares, C. G.: Review of the current status, technology and
future trends of offshore wind farms, Ocean Eng., 209, 107381,
https://doi.org/10.1016/j.oceaneng.2020.107381, 2020. a
Esteban, M. D., Diez, J. J., López, J. S., and Negro, V.: Why offshore wind
energy?, Renew. Energ., 36, 444–450, https://doi.org/10.1016/j.renene.2010.07.009,
2011. a
Fennell, E.: Comparison of Onshore and Offshore Boundary Layers in the HARMONIE
Model for Wind Energy Purposes, Master's thesis, meteorology and Air Quality,
Wageningen University, Wageningen, 2018. a
Foley, A. M., Leahy, P. G., Marvuglia, A., and McKeogh, E. J.: Current methods
and advances in forecasting of wind power generation, Renew. Energ., 37,
1–8, https://doi.org/10.1016/j.renene.2011.05.033, 2012. a, b, c
Fritsch, F. N. and Carlson, R. E.: Monotone piecewise cubic interpolation, SIAM
J. Numer. Anal., 17, 238–246, https://doi.org/10.1137/0717021, 1980. a
Gilbert, C., Messner, J. W., Pinson, P., Trombe, P.-J., Verzijlbergh, R., van
Dorp, P., and Jonker, H.: Statistical post-processing of turbulence-resolving
weather forecasts for offshore wind power forecasting, Wind Energy, 23,
884–897, https://doi.org/10.1002/we.2456, 2020. a
Hallgren, C., Arnqvist, J., Ivanell, S., Körnich, H., Vakkari, V., and
Sahlée, E.: Looking for an Offshore Low-Level Jet Champion among Recent
Reanalyses: A Tight Race over the Baltic Sea, Energies, 13, 3670,
https://doi.org/10.3390/en13143670, 2020. a, b, c
Hanifi, S., Liu, X., Lin, Z., and Lotfian, S.: A critical review of wind power
forecasting methods – past, present and future, Energies, 13, 3764,
https://doi.org/10.3390/en13153764, 2020. a
Heppelmann, T., Steiner, A., and Vogt, S.: Application of numerical weather
prediction in wind power forecasting: Assessment of the diurnal cycle,
Meteorol. Z., 26, 319–331, https://doi.org/10.1127/metz/2017/0820,
2017. a
Hirsikko, A., O'Connor, E. J., Komppula, M., Korhonen, K., Pfüller, A., Giannakaki, E., Wood, C. R., Bauer-Pfundstein, M., Poikonen, A., Karppinen, T., Lonka, H., Kurri, M., Heinonen, J., Moisseev, D., Asmi, E., Aaltonen, V., Nordbo, A., Rodriguez, E., Lihavainen, H., Laaksonen, A., Lehtinen, K. E. J., Laurila, T., Petäjä, T., Kulmala, M., and Viisanen, Y.: Observing wind, aerosol particles, cloud and precipitation: Finland's new ground-based remote-sensing network, Atmos. Meas. Tech., 7, 1351–1375, https://doi.org/10.5194/amt-7-1351-2014, 2014. a
Holtslag, A. A. M., Svensson, G., Baas, P., Basu, S., Beare, B., Beljaars, A. C. M., Bosveld, F. C., Cuxart, J., Lindvall, J., Steeneveld, G. J., Tjernström, M., and Van De Wiel, B. J. H.: Stable atmospheric boundary layers and diurnal cycles:
challenges for weather and climate models, B. Am.
Meteorol. Soc., 94, 1691–1706, https://doi.org/10.1175/bams-d-11-00187.1,
2013. a
Huang, N., Lu, G., and Xu, D.: A permutation importance-based feature selection
method for short-term electricity load forecasting using random forest,
Energies, 9, 767, https://doi.org/10.3390/en9100767, 2016. a
Huth, R., Beck, C., Philipp, A., Demuzere, M., Ustrnul, Z., Cahynová, M.,
Kyselỳ, J., and Tveito, O. E.: Classifications of atmospheric circulation
patterns: recent advances and applications, Ann. NY Acad.
Sci., 1146, 105–152, 2008. a
Jones, P., Harpham, C., and Briffa, K.: Lamb weather types derived from
reanalysis products, Int. J. Climatol., 33, 1129–1139,
https://doi.org/10.1002/joc.3498, 2012. a, b
Kalverla, P. C., Steeneveld, G.-J., Ronda, R. J., and Holtslag, A. A. M.: An
observational climatology of anomalous wind events at offshore meteomast
IJmuiden (North Sea), J. Wind Eng. Ind.
Aerod., 165, 86–99, https://doi.org/10.1016/j.jweia.2017.03.008, 2017. a
Kalverla, P. C., Steeneveld, G.-J., Ronda, R. J., and Holtslag, A. A. M.:
Evaluation of three mainstream numerical weather prediction models with
observations from meteorological mast IJmuiden at the North Sea, Wind Energy,
22, 34–48, https://doi.org/10.1002/we.2267, 2019. a
Kursa, M. B. and Rudnicki, W. R.: Feature selection with the Boruta package,
J. Statist. Softw., 36, 1–13,
https://doi.org/10.18637/jss.v036.i11, 2010. a, b
Lahouar, A. and Slama, J. B. H.: Hour-ahead wind power forecast based on random
forests, Renew. Energ., 109, 529–541, https://doi.org/10.1016/j.renene.2017.03.064,
2017. a, b
Lamraoui, F., Fortin, G., Benoit, R., Perron, J., and Masson, C.: Atmospheric
icing impact on wind turbine production, Cold Reg. Sci. Technol.,
100, 36–49, https://doi.org/10.1016/j.coldregions.2013.12.008, 2014. a
Lee, J. and Zhao, F.: Global Wind Report 2021,
available at: https://gwec.net/global-wind-report-2021/, last access:
30 March 2021. a
Lee, J. A., Hacker, J. P., Delle Monache, L., Kosović, B., Clifton, A.,
Vandenberghe, F., and Rodrigo, J. S.: Improving wind predictions in the
marine atmospheric boundary layer through parameter estimation in a
single-column model, Mon. Weather Rev., 145, 5–24,
https://doi.org/10.1175/MWR-D-16-0063.1, 2017. a
Leung, D. Y. and Yang, Y.: Wind energy development and its environmental
impact: A review, Renewable and Sustainable Energy Reviews, 16, 1031–1039,
https://doi.org/10.1016/j.rser.2011.09.024, 2012. a
Lledó, L., Torralba, V., Soret, A., Ramon, J., and Doblas-Reyes, F. J.:
Seasonal forecasts of wind power generation, Renew. Energ., 143, 91–100,
https://doi.org/10.1016/j.renene.2019.04.135, 2019. a
Mass, C. F., Ovens, D., Westrick, K., and Colle, B. A.: Does increasing
horizontal resolution produce more skillful forecasts?: The Results of Two
Years of real-Time Numerical Weather Prediction over the Pacific Northwest,
B. Am. Meteorol. Soc., 83, 407–430,
https://doi.org/10.1175/1520-0477(2002)083<0407:DIHRPM>2.3.CO;2, 2002. a
MathWorks: TreeBagger,
available at: https://www.mathworks.com/help/stats/treebagger.html, last
access: 30 March 2021. a
Molinder, J., Körnich, H., Olsson, E., Bergström, H., and Sjöblom, A.: Probabilistic forecasting of wind power production losses in cold climates: a case study, Wind Energ. Sci., 3, 667–680, https://doi.org/10.5194/wes-3-667-2018, 2018. a
Molinder, J., Scher, S., Nilsson, E., Körnich, H., Bergström, H., and
Sjöblom, A.: Probabilistic Forecasting of Wind Turbine Icing Related
Production Losses Using Quantile Regression Forests, Energies, 14, 158,
https://doi.org/10.3390/en14010158, 2021. a
Müller, M., Homleid, M., Ivarsson, K. I., Køltzow, M. A. Ø., Lindskog, M., Midtbø, K. H., Andrae, U., Aspelien, T., Berggren, L., Bjørge, D., Dahlgren, P., Kristiansen, J., Randriamampianina, R., Ridal, M., and Vignes, O.: AROME-MetCoOp: A Nordic convective-scale
operational weather prediction model, Weather Forecast., 32, 609–627,
https://doi.org/10.1175/WAF-D-16-0099.1, 2017. a
Nielsen, T. S., Joensen, A., Madsen, H., Landberg, L., and Giebel, G.: A new
reference for wind power forecasting, Wind Energy, 1, 29–34,
https://doi.org/10.1002/(SICI)1099-1824(199809)1:1%3C29::AID-WE10%3E3.0.CO;2-B, 1998. a
Pearson, G., Davies, F., and Collier, C.: An analysis of the performance of the
UFAM pulsed Doppler lidar for observing the boundary layer, J.
Atmos. Ocean. Technol., 26, 240–250,
https://doi.org/10.1175/2008JTECHA1128.1, 2009. a
Quaschning, V. V.: Renewable energy and climate change, 2nd edn., John Wiley & Sons, Chichester, West Sussex, UK, Hoboken, N. J., 2020,
2019. a
Reen, B. P. and Stauffer, D. R.: Data assimilation strategies in the planetary
boundary layer, Bound.-Lay. Meteorol., 137, 237–269,
https://doi.org/10.1007/s10546-010-9528-6, 2010. a
Rubner, Y., Tomasi, C., and Guibas, L. J.: The earth mover's distance as a
metric for image retrieval, Int. J. Comput. Vision, 40,
99–121, https://doi.org/10.1023/A:1026543900054, 2000. a
Sandu, I., Beljaars, A., Bechtold, P., Mauritsen, T., and Balsamo, G.: Why is
it so difficult to represent stably stratified conditions in numerical
weather prediction (NWP) models?, J. Adv. Model. Earth
Sy., 5, 117–133, https://doi.org/10.1002/jame.20013, 2013. a
Shaw, W. J., Lundquist, J. K., and Schreck, S. J.: Research needs for wind
resource characterization, B. Am. Meteorol. Soc.,
90, 535–538, https://doi.org/10.1175/2008BAMS2729.1, 2009. a
Shi, K., Qiao, Y., Zhao, W., Wang, Q., Liu, M., and Lu, Z.: An improved random
forest model of short-term wind-power forecasting to enhance accuracy,
efficiency, and robustness, Wind Energy, 21, 1383–1394,
https://doi.org/10.1002/we.2261, 2018. a
Siemens: Siemens Wind Turbine SWT-3.6-120: Thoroughly Tested, Utterly
Reliable,
available at: https://pdf.archiexpo.com/pdf/siemens-gamesa/swt-36-120/88089-134487.html
last access: (19 March 2021), 2011. a
Sims, R. E.: Renewable energy: a response to climate change, Sol. Energy, 76,
9–17, https://doi.org/10.1016/S0038-092X(03)00101-4, 2004. a
Soman, S. S., Zareipour, H., Malik, O., and Mandal, P.: A review of wind power
and wind speed forecasting methods with different time horizons, in: North
American Power Symposium 2010, IEEE, 1–8,
https://doi.org/10.1109/NAPS.2010.5619586, 2010. a
Sproson, D. and Sahlée, E.: Modelling the impact of Baltic Sea upwelling on
the atmospheric boundary layer, Tellus A, 66, 24041, https://doi.org/10.3402/tellusa.v66.24041, 2014. a
Stull, R. B.: An introduction to boundary layer meteorology, vol. 13, Springer Science & Business Media, Dordrecht, 1988. a
Svensson, N.: Mesoscale Processes over the Baltic Sea, PhD thesis, Acta
Universitatis Upsaliensis, Uppsala, 2018. a
Svensson, N., Bergström, H., Sahlée, E., and Rutgersson, A.: Stable
atmospheric conditions over the Baltic Sea: model evaluation and climatology, Boreal Environ. Res., 21, 387–404,
2016. a
SWEA: 100 percent renewable electricity by 2040,
available at: https://swedishwindenergy.com/wp-content/uploads/2019/10/Svensk_Vindenergi_ROADMAP_2040_rev_ENG-1.pdf
(last access: 30 March 2021), 2019. a
Swider, D. J., Beurskens, L., Davidson, S., Twidell, J., Pyrko, J., Prüggler, W., Auer, H., Vertin, K., and Skema, R.: Conditions and costs for renewables electricity
grid connection: Examples in Europe, Renew. Energ., 33, 1832–1842,
https://doi.org/10.1016/j.renene.2007.11.005, 2008. a
Taylor, K. E.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res.-Atmos., 106, 7183–7192,
https://doi.org/10.1029/2000JD900719, 2001. a
Treiber, N. A., Heinermann, J., and Kramer, O.: Wind power prediction with
machine learning, in: Computational sustainability. Studies in Computational
Intelligence, edited by: Lässig, J., Kersting, K., and Morik, K., Springer, 645,
13–29, https://doi.org/10.1007/978-3-319-31858-5_2, 2016.
a
Tuononen, M., O’Connor, E. J., Sinclair, V. A., and Vakkari, V.: Low-level
jets over Utö, Finland, based on Doppler lidar observations, J.
Appl. Meteorol. Climatol., 56, 2577–2594,
https://doi.org/10.1175/JAMC-D-16-0411.1, 2017. a, b
Vakkari, V., Manninen, A. J., O'Connor, E. J., Schween, J. H., van Zyl, P. G., and Marinou, E.: A novel post-processing algorithm for Halo Doppler lidars, Atmos. Meas. Tech., 12, 839–852, https://doi.org/10.5194/amt-12-839-2019, 2019. a
Vannitsem, S., Bremnes, J. B., Demaeyer, J., Evans, G. R., Flowerdew, J., Hemri, S., Lerch, S., Roberts, N., Theis, S., Atencia, A., Bouallègue, Z. B., Bhend, J., Dabernig, M., de Cruz, L., Hieta, L., Mestre, O., Moret, L., Odak Plenković, I., Schmeits, M., Taillardat, M., van den Bergh, J., van Schaeybroeck, B., Whan, K., and Ylhaisi, J.:
Statistical postprocessing for weather forecasts–review, challenges and
avenues in a big data world, B. Am. Meteorol. Soc.,
102, 681–699, https://doi.org/10.1175/BAMS-D-19-0308.1, 2020. a, b, c
Vassallo, D., Krishnamurthy, R., Sherman, T., and Fernando, H. J.: Analysis of
Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting,
Energies, 13, 5488, https://doi.org/10.3390/en13205488, 2020. a, b
Wilczak, J., Finley, C., Freedman, J., Cline, J., Bianco, L., Olson, J., Djalalova, I.,Sheridan, L., Ahlstrom, M., Manobianco, J., Zack, J., Carley, J. R., Benjamin, S., Coulter, R., Berg, L. K., Mirocha, J., Clawson, K., Natenberg, E., and Marquis, M.: The Wind
Forecast Improvement Project (WFIP): A public–private partnership addressing
wind energy forecast needs, B. Am. Meteorol. Soc.,
96, 1699–1718, https://doi.org/10.1175/BAMS-D-14-00107.1, 2015. a
Wu, L., Shao, M., and Sahlée, E.: Impact of Air–Wave–Sea Coupling on the
Simulation of Offshore Wind and Wave Energy Potentials, Atmosphere, 11, 327,
https://doi.org/10.3390/atmos11040327, 2020. a
Short summary
As wind power becomes more popular, there is a growing demand for accurate power production forecasts. In this paper we investigated different methods to improve wind power forecasts for an offshore location in the Baltic Sea, using both simple and more advanced techniques. The performance of the methods is evaluated for different weather conditions. Smoothing the forecast was found to be the best method in general, but we recommend selecting which method to use based on the forecasted weather.
As wind power becomes more popular, there is a growing demand for accurate power production...
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