Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic
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Published:2023-01-02
Issue:1
Volume:8
Page:1-23
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ISSN:2366-7451
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Container-title:Wind Energy Science
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language:en
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Short-container-title:Wind Energ. Sci.
Author:
Redfern StephanieORCID, Optis Mike, Xia GengORCID, Draxl CarolineORCID
Abstract
Abstract. As offshore wind farm development expands, accurate wind resource forecasting over the ocean is needed. One important yet relatively unexplored aspect of offshore wind resource assessment is the role of sea surface temperature (SST). Models are generally forced with reanalysis data sets, which employ daily SST products. Compared with observations, significant variations in SSTs that occur on finer timescales are often not captured. Consequently, shorter-lived events such as sea breezes and low-level jets (among others), which are influenced by SSTs, may not be
correctly represented in model results. The use of hourly SST products may improve the forecasting of these events. In this study, we examine the
sensitivity of model output from the Weather Research and Forecasting model (WRF) 4.2.1 to different SST products. We first evaluate three different
data sets: the Multiscale Ultrahigh Resolution (MUR25) SST analysis, a daily, 0.25∘ × 0.25∘ resolution product; the
Operational Sea Surface Temperature and Ice Analysis (OSTIA), a daily, 0.054∘ × 0.054∘ resolution product; and SSTs from
the Geostationary Operational Environmental Satellite 16 (GOES-16), an hourly, 0.02∘ × 0.02∘ resolution product. GOES-16 is not processed at the same level as OSTIA and MUR25; therefore, the product requires gap-filling using an interpolation method to create a complete map with no missing data points. OSTIA and GOES-16 SSTs validate markedly better against buoy observations than MUR25, so these two products are selected for use with model simulations, while MUR25 is at this point removed from consideration. We run the model for June and July of 2020 and find that for this time period, in the Mid-Atlantic, although OSTIA SSTs overall validate better against in situ observations taken via a buoy array in the area, the two products result in comparable hub-height (140 m) wind characterization performance on monthly timescales. Additionally, during hours-long flagged events (< 30 h each) that show statistically significant wind speed deviations between the two simulations, both simulations once again demonstrate similar validation performance (differences in bias, earth mover's distance, correlation, and root mean square error on the order of 10−1 or less), with GOES-16 winds validating nominally better than OSTIA winds. With a more refined GOES-16 product, which has been not only gap-filled but also assimilated with in situ SST measurements in the region, it is likely that hub-height winds characterized by GOES-16-informed simulations would definitively validate better than those informed by OSTIA SSTs.
Funder
U.S. Department of Energy
Publisher
Copernicus GmbH
Subject
Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment
Reference47 articles.
1. Aird, J. A., Barthelmie, R. J., Shepherd, T. J., and Pryor, S. C.:
Occurrence of Low-Level Jets over the Eastern U.S. Coastal Zone at Heights Relevant to Wind Energy, Energies, 15, 445, https://doi.org/10.3390/en15020445, 2022. a 2. Alvera-Azcárate, A., Barth, A., Rixen, M., and Beckers, J. M.:
Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions: application to the Adriatic Sea surface temperature, Ocean Model., 9, 325–346, https://doi.org/10.1016/j.ocemod.2004.08.001, 2005. a 3. Banta, R. M., Pichugina, Y. L., Brewer, W. A., James, E. P., Olson, J. B., Benjamin, S. G., Carley, J. R., Bianco, L., Djalalova, I. V., Wilczak, J. M., Hardesty, R. M., Cline, J., and Marquis, M. C.:
Evaluating and Improving NWP Forecast Models for the Future: How the Needs of Offshore Wind Energy Can Point the Way, B. Am. Meteorol. Soc., 99, 1155–1176, https://doi.org/10.1175/BAMS-D-16-0310.1, 2018. a 4. Bureau of Ocean Energy Management: Outer Continental Shelf Renewable Energy Leases Map Book, https://www.boem.gov/renewable-energy/mapping-and-data/renewable-energy-gis-data
(last access: 19 December 2022), 2018. a 5. Byun, D., Kim, S., Cheng, F.-Y., Kim, H.-C., and Ngan, F.: Improved Modeling Inputs: Land Use and Sea-Surface Temperature, Final Report, Texas Commission on Environmental Quality, https://www.tceq.texas.gov/airquality/airmod/project/pj_report_met.html
(last access: 19 December 2022), 2007. a
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