A data-driven framework for the reconstruction of satellite-derived wind speed image time-series

Abstract

<p>Wind assessment studies call for accurate and consistent datasets to evaluate the wind resource potential in the long term. Satellite-derived wind speed estimates have been widely employed in wind energy applications [1–3] due to their high spatial resolution. Synthetic Aperture Radar (SAR) sensors, in particular, provide image snapshots of wind fields on a (sub-) kilometer scale, although at irregular temporal intervals. Moreover, the scenes acquired are often tilted due to satellite’s orbit. The formed wind speed image time-series is, therefore, both spatially and temporally incomplete.</p> <p>This study attempts to reconstruct Sentinel-1 A&B OCN Level-2 wind speed image time-series by employing a data-driven framework and using reanalysis as auxiliary data. More precisely, the methodology resembles what is generally called analog forecasting in climate studies, where past climate conditions are used to predict current weather state [4]. Although the analog method has been long used for empirical-statistical downscaling of Global Circulation Models (GCMs) [5,6], few studies address the problem of gap-filling record observations/estimates [7,8]. In the same context, Empirical Orthogonal Functions (EOF) are used in this work to classify (decompose) the data sets into classes of similar weather states and use this classification to reconstruct the missing information based on the co-registered climate variables. Once physically consistent patterns (analogs) are identified in the historical image record, synthetic wind speed images are generated to fill the data gaps.</p> <p>The method is benchmarked in the offshore area around Cyprus against the probabilistic framework of Multiple-Point Statistics (MPS). Image cross-validation, in combination with statistical metrics, is used to evaluate the method’s performance. Results show that the proposed methodology can furnish a reliable framework for wind speed spatiotemporal variability reconstruction in an offshore wind resource assessment context. An illustration of the method in terms of wind power density estimation is provided over an annual period.</p> <p>References</p> <ul> <li>Nielsen, M.; Astrup, P.; Hasager, C.B.; Barthelmie, R.; Pryor, S. Satellite Information for Wind Energy Applications. <strong>2004</strong>, <em>1479</em>.</li> <li>Medina-Lopez, E.; McMillan, D.; Lazic, J.; Hart, E.; Zen, S.; Angeloudis, A.; Bannon, E.; Browell, J.; Dorling, S.; Dorrell, R.M.; et al. Satellite Data for the Offshore Renewable Energy Sector: Synergies and Innovation Opportunities. <em>Remote Sens. Environ.</em> <strong>2021</strong>, <em>264</em>, 112588, doi:10.1016/j.rse.2021.112588.</li> <li>Edwards, M.R.; Holloway, T.; Pierce, R.B.; Blank, L.; Broddle, M.; Choi, E.; Duncan, B.N.; Esparza, Á.; Falchetta, G.; Fritz, M.; et al. Satellite Data Applications for Sustainable Energy Transitions. <em>Front. Sustain.</em> <strong>2022</strong>, <em>3</em>, 64, doi:10.3389/frsus.2022.910924.</li> <li>Dutton, J. What Is Analog Forecasting? - World Climate Service Available online: https://www.worldclimateservice.com/2021/09/02/what-is-analog-forecasting/ (accessed on 9 January 2023).</li> <li>Bettolli, M.L. Analog Models for Empirical-Statistical Downscaling. <em>Oxford Res. Encycl. Clim. Sci.</em> <strong>2021</strong>, doi:10.1093/acrefore/9780190228620.013.738.</li> <li>Zorita, E.; Storch, H. von The Analog Method as a Simple Statistical Downscaling Technique: Comparison with More Complicated Methods in: Journal of Climate Volume 12 Issue 8 (1999) Available online: https://journals.ametsoc.org/view/journals/clim/12/8/1520-0442_1999_012_2474_tamaas_2.0.co_2.xml (accessed on 9 January 2023).</li> <li>Hoeltgebaum, L.E.B.; Dias, N.L.; Costa, M.A. An Analog Period Method for Gap-Filling of Latent Heat Flux Measurements. <em>Hydrol. Process.</em> <strong>2021</strong>, <em>35</em>, doi:10.1002/hyp.14105.</li> <li>Henn, B.; Raleigh, M.S.; Fisher, A.; Lundquist, J.D. A Comparison of Methods for Filling Gaps in Hourly Near-Surface Air Temperature Data. <em>J. Hydrometeorol.</em> <strong>2013</strong>, <em>14</em>, 929–945, doi:10.1175/JHM-D-12-027.1.</li> </ul>