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Interannual Variability and Seasonal Predictability of Wind and Solar Resources

Department of Civil Engineering and NOAA-CREST, The City College of New York, New York, NY 10031, USA
Department of Civil and Environmental Engineering, Rice University, Houston, TX 77005, USA
Author to whom correspondence should be addressed.
Resources 2017, 6(3), 29;
Received: 30 June 2017 / Revised: 17 July 2017 / Accepted: 17 July 2017 / Published: 20 July 2017
Solar and wind resources available for power generation are subject to variability due to meteorological factors. Here, we use a new global climate reanalysis product, Version 2 of the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA-2), to quantify interannual variability of monthly-mean solar and wind resource from 1980 to 2016 at a resolution of about 0.5 degrees. We find an average coefficient of variation (CV) of 11% for monthly-mean solar radiation and 8% for wind speed. Mean CVs were about 25% greater over ocean than over land and, for land areas, were greatest at high latitude. The correlation between solar and wind anomalies was near zero in the global mean, but markedly positive or negative in some regions. Both wind and solar variability were correlated with values of climate modes such as the Southern Oscillation Index and Arctic Oscillation, with correlations in the Northern Hemisphere generally stronger during winter. We conclude that reanalysis solar and wind fields could be helpful in assessing variability in power generation due to interannual fluctuations in the solar and wind resource. Skillful prediction of these fluctuations seems to be possible, particularly for certain regions and seasons, given the persistence or predictability of climate modes with which these fluctuations are associated. View Full-Text
Keywords: renewable energy; interannual variability; seasonal forecasting; teleconnections renewable energy; interannual variability; seasonal forecasting; teleconnections
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Krakauer, N.Y.; Cohan, D.S. Interannual Variability and Seasonal Predictability of Wind and Solar Resources. Resources 2017, 6, 29.

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