Skip to main content

Advertisement

SpringerLink
Log in

Improved quality gridded surface wind speed datasets for Australia

  • Original Paper
  • Published:
Meteorology and Atmospheric Physics Aims and scope Submit manuscript

Abstract

Gridded near-surface (2 and 10 m) daily average wind datasets for Australia have been constructed by interpolating observational data collected by the Australian Bureau of Meteorology (BoM). The new datasets span Australia at 0.05 × 0.05° resolution with a daily time step. They are available for the period 1 January 1975 to present with daily updates. The datasets were constructed by blending observational data collected at various heights using local surface roughness information. Error detection techniques were used to identify and remove suspect data. Statistical performances of the spatial interpolations were evaluated using a cross-validation procedure, by sequentially applying interpolations after removing the observed weather station data. The accuracy of the new blended 10 m wind datasets were estimated through comparison with the Reanalysis ERA5-Land 10 m wind datasets. Overall, the blended 10 m wind speed patterns are similar to the ERA5-Land 10 m wind. The new blended 10 m wind datasets outperformed ERA5-Land 10 m wind in terms of spatial correlations and mean absolute errors through validations with BoM 10 m wind weather station data for the period from 1981 to 2020. Average correlation (R2) for blended 10 m wind is 0.68, which is 0.45 for ERA5-Land 10 m wind. The average of the mean absolute error is 1.15 m/s for blended 10 m wind, which is lower than that for ERA5-Land 10 m wind (1.61 m/s). The blending technique substantially improves the spatial correlations for blended 2 m wind speed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Data availability

The datasets generated during the current study are available from the corresponding author on reasonable request.

References

  • ABARES (2016) The Australian Land Use and Management Classification Version 8, Australian Bureau of Agricultural and Resource Economics and Sciences. CC BY 3.0. ISBN: 978–1–74323–310–8, ABARES project: 115063–43590. Canberra.

  • Brune S, Keller JD, Wahl S (2021) Evaluation of wind speed estimates in reanalyses for wind energy applications. Adv Sci Res 18:115–126

    Article  Google Scholar 

  • Bureau of Meteorology (BoM) (2007) Climate statistics for Australian locations, compiled 2 February 2007, vi. http://www.bom.gov.au/climate/cdo/about/definitionsother.shtml. Accessed 12 Aug 2020

  • Cardone VJ, Greenwood JG, Cane MA (1990) On trends in historical marine wind data. J Clim 3:113–127. https://doi.org/10.1175/1520-0442(1990)003%3c0113:OTIHMW%3e2.0.CO;2

    Article  Google Scholar 

  • Cheng X, Zhao T, Gong S (2016) Implications of East Asian summer and winter monsoons for interannual aerosol variations over central-eastern China. Atmos Environ 129:218–228

    Article  Google Scholar 

  • Coelingh JP, van Wijk AJM, Holtslag AAM (1996) Analysis of wind speed observations over the North Sea. J Wind Eng Ind Aerodyn 61(1):51–69. https://doi.org/10.1016/0167-6105(96)00043-8

    Article  Google Scholar 

  • Coppin P, Ayotte K, Steggel N (2003) Wind Resource Assessment in Australia - A Planners Guide. Report by the Wind Energy Research Unit, CSIRO Land and Water

    Google Scholar 

  • Donnelly JR (1984) The productivity of breeding ewes grazing on Lucerne or grass and clover pastures on the Tablelands of southern Australia. III. Lamb mortality and weaning percentage. Aust J Agric Res 35(5):709–721

    Article  Google Scholar 

  • Dunn R, Azorin-Molina C, Mears C, Berrisford P, McVicar T (2016) Surface winds. In state of the climate 2015. Bull Am Meteor Soc 97(8):S38–S40

    Google Scholar 

  • Fan W, Liu Y, Chappell A, Dong L, Xu R, Ekström M, Fu T, Zeng Z (2021) Evaluation of global reanalysis land surface wind speed trends to support wind energy development using in situ observations. J Appl Meteorol Climatol 60(1):33–50. https://doi.org/10.1175/JAMC-D-20-0037.1

    Article  Google Scholar 

  • Fujiwara M, Wright JS, Manney GL, Gray LJ, Anstey J, Birner T, Davis S, Gerber EP, Harvey VJ, Hegglin MI, Homeyer CR, Knox JA, Kruger K, Lambert A, Long CS, Martineau P, Molod A, Monge-Sanz BM, Santee ML, Tegtmeier S, Chabrillat S, Tan DG, Jackson DR, Polavarapu S, Compo GP, Dragani R, Ebisuzaki W, Harada Y, Kobayashi C, McCarty W, Onogi K, Pawson S, Simmons A, Wargan K, Whitaker JS, Zou CZ (2017) Introduction to the SPARC reanalysis intercomparison project (S-RIP) and overview of the reanalysis systems. Atmos Chem Phys 17:1417–1452. https://doi.org/10.5194/acp-17-1417-2017

    Article  Google Scholar 

  • Geoscience Australia (2011) Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM). Bioregional Assessment Source Dataset.

  • Guo H, Xu M, Hu Q (2011) Changes in near-surface wind speed in China: 1969–2005. Int J Climatol 31(3):349–358. https://doi.org/10.1002/joc.2091

    Article  Google Scholar 

  • Jakob D (2010) Challenges in developing a high-quality surface wind-speed data-set for Australia. Aust Meteorol Oceanogr J 60:227–236

    Article  Google Scholar 

  • Jancewicz K, Szymanowski M (2017) The relevance of surface roughness data qualities in diagnostic modeling of wind velocity in complex terrain: a case study from the Śnieżnik Massif (SW Poland). Pure Appl Geophys 174:569–594. https://doi.org/10.1007/s00024-016-1297-9

    Article  Google Scholar 

  • Jeffrey SJ, Carter JO, Moodie KB, Beswick AR (2001) Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ Model Softw 16(4):309–330. https://doi.org/10.1016/S1364-8152(01)00008-1

    Article  Google Scholar 

  • Kaspar F, Niermann D, Borsche M, Fiedler S, Keller J, Potthast R, Rösch T, Spangehl T, Tinz B (2020) Regional atmospheric reanalysis activities at Deutscher Wetterdienst: review of evaluation results and application examples with a focus on renewable energy. Adv Sci Res 17:115–128. https://doi.org/10.5194/asr-17-115-2020

    Article  Google Scholar 

  • Kim JC, Paik K (2015) Recent recovery of surface wind speed after decadal decrease: a focus on South Korea. Clim Dyn 45(5):1699–1712. https://doi.org/10.1007/s00382-015-2546-9

    Article  Google Scholar 

  • Klink K (1999) Trends in mean monthly maximum and minimum surface wind speeds in the coterminous United States, 1961 to 1990. Climate Res 13:193–205. https://doi.org/10.3354/cr013193

    Article  Google Scholar 

  • Liu X, Li Q, Wang H, Ren Z, He G, Zhang D, Han T, Sun B, Pan D, Ji T (2021) Response of potential grassland vegetation to historical and future climate change in inner Mongolia. Rangel J. https://doi.org/10.1071/RJ20108

    Article  Google Scholar 

  • Lymburner L, Tan P, Mueller N, Thackway R, Lewis A, Thankappan M, Senarath U (2011) The National Dynamic Land Cover Dataset. Geoscience Australia, Symonston, Australia

    Google Scholar 

  • McAlpine CA, Bowen ME, Rhodes JR (2010) Landscape and regional perspectives from eastern Australia. Temperate woodland conservation and management edited by David Lindenmayer, Andrew Bennett and Richard Hobbs. Collingwood CSIRO Publishing, Melbourne, pp 231–240

    Google Scholar 

  • McVicar TR, Roderick ML, Donohue RJ, Li LT, Van Niel TG, Thomas A, Dinpashoh Y (2012) Global review and synthesis of trends in observed terrestrial near-surface wind speeds: Implications for evaporation. J Hydrol. https://doi.org/10.1016/j.jhydrol.2011.10.024

    Article  Google Scholar 

  • McVicar TR, Van Niel TG, Li LT, Roderick ML, Rayner DP, Ricciardulli L, Donohue RJ (2008) Wind speed climatology and trends for Australia, 1975–2006: capturing the stilling phenomenon and comparison with near-surface reanalysis output. Geophys Res Lett 35(20):L20403. https://doi.org/10.1029/2008GL035627

    Article  Google Scholar 

  • Muñoz SJ (2019) ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://cds.climate.copernicus.eu/cdsapp#!/dataset/https://doi.org/10.24381/cds.e2161bac?tab=overview. Accessed 10 Mar 2021

  • Muñoz SJ, Dutra E, Agustí PA, Albergel C, Arduini G, Balsamo G, Boussetta S, Choulga M, Harrigan S, Hersbach H, Martens B, Miralles DG, Piles M, Rodríguez FNJ, Zsoter E, Buontempo C, Thépaut J (2021) ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst Sci Data 13(9):4349–4383

    Article  Google Scholar 

  • Otero C, Manchado C, Arias R, Bruschi VM, Gómez-Jáuregui V, Cendrero A (2012) Wind energy development in Cantabria, Spain. Methodological approach, environmental, technological and social issues. Renew Energy 40(1):137–149. https://doi.org/10.1016/j.renene.2011.09.008

    Article  Google Scholar 

  • Palutikof JP, Kelly PM, Davies TD, Halliday JA (1987) Impact of spatial and temporal windspeed variability on wind energy output. J Climate Appl Meteorol 26:1124–1133. https://doi.org/10.1175/1520-0450(1987)026%3c1124:IOSATW%3e2.0.CO;2

    Article  Google Scholar 

  • Paredes P, Martins DS, Pereira LS, Cadima J, Pires C (2018) Accuracy of daily estimation of grass reference evapotranspiration using ERA-Interim reanalysis products with assessment of alternative bias correction schemes. Agric Water Manag 210:340–353. https://doi.org/10.1016/j.agwat.2018.08.003

    Article  Google Scholar 

  • Pelosi A, Chirico GB (2021) Regional assessment of daily reference evapotranspiration: can ground observations be replaced by blending ERA5-land meteorological reanalysis and CM-SAF satellite-based radiation data? Agric Water Manag 258:107169. https://doi.org/10.1016/j.agwat.2021.107169

    Article  Google Scholar 

  • Pelosi A, Terribile F, D’Urso G, Chirico GB (2020) Comparison of ERA5-Land and UERRA MESCAN-SURFEX reanalysis data with spatially interpolated weather observations for the regional assessment of reference evapotranspiration. Water 12:1669. https://doi.org/10.3390/w12061669

    Article  Google Scholar 

  • Pirazzoli PA, Tomasin A (2003) Recent near-surface wind changes in the central Mediterranean and Adriatic areas. Int J Climatol 23(8):963–973. https://doi.org/10.1002/joc.925

    Article  Google Scholar 

  • Pryor SC, Barthelmie RJ, Kjellström E (2005) Potential climate change impact on wind energy resources in northern Europe: analyses using a regional climate model. Clim Dyn 25(7–8):815–835. https://doi.org/10.1007/s00382-005-0072-x

    Article  Google Scholar 

  • Pryor SC, Barthelmie RJ, Young DT, Takle ES, Arritt RW, Flory D, Roads J (2009) Wind speed trends over the contiguous United States. J Geophys Res Atmos 114(D14):D14105. https://doi.org/10.1029/2008JD011416

    Article  Google Scholar 

  • Ramli NI, Ali MI, Saad MSH, Majid TA (2009) Estimation of the Roughness Length (zo) in Malaysia using Satellite Image. In The Seventh Asia-Pacific Conference on Wind Engineering, November 8−12, 2009. Taipei, Taiwan

  • Ramon J, Lledó L, Torralba V, Soret A, Doblas-Reyes FJ (2019) What global reanalysis best represents near surface winds? Q J R Meteorol Soc 145(724):3236–3251. https://doi.org/10.1002/qj.3616

    Article  Google Scholar 

  • Raupach MR (1992) Drag and drag partition on rough surfaces. Bound-Layer Meteorol 60(4):375–395

    Article  Google Scholar 

  • Riley SJ, DeGloria SD, Elliot R (1999) A Terrain ruggedness index that quantifies topographic heterogeneity. Intermt J Sci 5(1–4):23–27

    Google Scholar 

  • Roderick ML, Rotstayn LD, Farquhar GD, Hobbins MT (2007) On the attribution of changing pan evaporation. Geophys Res Lett 34(17):L17403. https://doi.org/10.1029/2007GL031166

    Article  Google Scholar 

  • Rohatgi JS, Nelson V (1994) Wind characteristics: an analysis for the generation of wind power. Alternative energy institute. West Texas A & M University, Canyon, Tex., USA

    Google Scholar 

  • Sailor DJ, Smith M, Hart M (2008) Climate change implications for wind power resources in the Northwest United States. Renew Energy 33(11):2393–2406. https://doi.org/10.1016/j.renene.2008.01.007

    Article  Google Scholar 

  • Shen L, Wang H, Zhao T, Liu J, Bai Y, Kong S, Shu Z (2020) Characterizing regional aerosol pollution in central China based on 19 years of MODIS data: spatiotemporal variation and aerosol type discrimination. Environ Pollut 263:114556

    Article  Google Scholar 

  • Smits A, Klein Tank AMG, Können GP (2005) Trends in storminess over the Netherlands, 1962–2002. Int J Climatol 25(10):1331–1344. https://doi.org/10.1002/joc.1195

    Article  Google Scholar 

  • Tian Y, Miao JF (2019) A Numerical study of mountain-plain breeze circulation in Eastern Chengdu China. Sustainability 11(10):2821. https://doi.org/10.3390/su11102821

    Article  Google Scholar 

  • Troccoli A, Muller K, Coppin P, Davy R, Russell C, Hirsch AL (2012) Long-term wind speed trends over Australia. J Clim 25(1):170–183. https://doi.org/10.1175/2011JCLI4198.1

    Article  Google Scholar 

  • Troen I, Petersen EL (1989) European Wind Atlas. Risø National Laboratory. Roskilde, Denmark. pp 656

  • Tuller SE (2004) Measured wind speed trends on the West Coast of Canada. Int J Climatol 24(11):1359–1374. https://doi.org/10.1002/joc.1073

    Article  Google Scholar 

  • Van Ackere S, Van Eetvelde G, Schillebeeckx D, Papa E, Van Wyngene K, Vandevelde L (2015) Wind resource mapping using landscape roughness and spatial interpolation methods. Energies 8(8):8682–8703. https://doi.org/10.3390/en8088682

    Article  Google Scholar 

  • Vautard R, Cattiaux J, Yiou P, Thépaut JN, Ciais P (2010) Northern Hemisphere atmospheric stilling partly attributed to an increase in surface roughness. Nat Geosci 3:756–761. https://doi.org/10.1038/ngeo979

    Article  Google Scholar 

  • Wahba G, Wendelberger J (1980) Some new mathematical methods for variational objective analysis using splines and cross validation. Mon Weather Rev 108:1122–1143

    Article  Google Scholar 

  • Wahba G (1990) Spline Models for Observational Data. Society for Industrial and Applied Mathematics, Philadelphia

    Book  Google Scholar 

  • Wan H, Wang XL, Swail VR (2010) Homogenization and trend analysis of Canadian near-surface wind speeds. J Clim 23(5):1209. https://doi.org/10.1175/2009JCLI3200.1

    Article  Google Scholar 

  • Wang B, Liu DL, Macadam I, Alexanderd LV, Abramowitzd G, Yu Q (2016) Multi-model ensemble projections of future extreme temperature change with statistical downscaling method in eastern Australia. Clim Change 138:85. https://doi.org/10.1007/s10584-016-1726-x

    Article  Google Scholar 

  • Wentz FJ, Ricciardulli L, Hilburn K, Mears C (2007) How much more rain will global warming bring? Science 317(5835):233–235. https://doi.org/10.1126/science.1140746

    Article  Google Scholar 

  • Xu M, Chang CP, Fu C, Qi Y, Robock A, Robinson D, Zhang HM (2006) Steady decline of East Asian monsoon winds, 1969–2000: evidence from direct ground measurements of wind speed. J Geophys Res Atmos 111(D24):D24111. https://doi.org/10.1029/2006JD007337

    Article  Google Scholar 

  • Yan Z, Bate S, Chandler RE, Isham V, Wheater H (2002) An analysis of daily maximum wind speed in northwestern Europe using generalized linear models. J Clim 15(15):2073–2088. https://doi.org/10.1175/1520-0442(2002)015%3c2073:AAODMW%3e2.0.CO;2

    Article  Google Scholar 

  • Yim SHL, Fung JCH, Lau AKH, Kot SC (2007) Developing a high-resolution wind map for a complex terrain with a coupled MM5/CALMET system. J Geophys Res Atmos 112(D5):D05106

    Article  Google Scholar 

  • Young IR, Zieger S, Babanin AV (2011) Global trends in wind speed and wave height. Science 332(6028):451–455. https://doi.org/10.1126/science.1197219

    Article  Google Scholar 

  • Yu J, Zhou T, Jiang Z, Zou L (2019) Evaluation of near-surface wind speed changes during 1979 to 2011 over China based on five reanalysis datasets. Atmosphere 10:804. https://doi.org/10.3390/atmos10120804

    Article  Google Scholar 

  • Zhu L, Miao JF, Zhao TL (2020) Numerical simulation of urban breeze circulation in a heavy pollution event in Chengdu city. Chin J Geophys 63(1):101–122

    Google Scholar 

Download references

Acknowledgements

This research was supported by the Queensland Government Department of Environment and Science. We thank our colleagues Stuart Burgess and Keryn Oude-Egberink for their help in preparation of the manuscript. We thank our colleagues Baisen Zhang, Dorine Bruget and Andrew Clark for their internal reviews and Scott Irvine for assisting with the graphics. The authors wish to acknowledge the reviewers for their comments on the original manuscript. We gratefully acknowledge the use of the following open source software: CDO and R.

Author information

Authors and Affiliations

  1. Science and Technology Division, Department of Environment and Science, Queensland Government, GPO Box 2454, Brisbane, QLD, 4001, Australia

    Hong Zhang, Stephen Jeffrey & John Carter

Authors
  1. Hong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stephen Jeffrey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John Carter
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong Zhang.

Additional information

Responsible Editor: Silvia Trini Castelli.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, H., Jeffrey, S. & Carter, J. Improved quality gridded surface wind speed datasets for Australia. Meteorol Atmos Phys 134, 85 (2022). https://doi.org/10.1007/s00703-022-00925-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00703-022-00925-2

Search

Navigation