Interannual Variability and Seasonal Predictability of Wind and Solar Resources

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...

Full description

Bibliographic Details
Published in:Resources
Main Authors: Nir Y. Krakauer, Daniel S. Cohan
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2017
Subjects:
Q
Online Access:https://doi.org/10.3390/resources6030029
https://doaj.org/article/b0a9f84b13264d039c133180129df5ed
id ftdoajarticles:oai:doaj.org/article:b0a9f84b13264d039c133180129df5ed
record_format openpolar
spelling ftdoajarticles:oai:doaj.org/article:b0a9f84b13264d039c133180129df5ed 2023-05-15T15:07:40+02:00 Interannual Variability and Seasonal Predictability of Wind and Solar Resources Nir Y. Krakauer Daniel S. Cohan 2017-07-01T00:00:00Z https://doi.org/10.3390/resources6030029 https://doaj.org/article/b0a9f84b13264d039c133180129df5ed EN eng MDPI AG https://www.mdpi.com/2079-9276/6/3/29 https://doaj.org/toc/2079-9276 2079-9276 doi:10.3390/resources6030029 https://doaj.org/article/b0a9f84b13264d039c133180129df5ed Resources, Vol 6, Iss 3, p 29 (2017) renewable energy interannual variability seasonal forecasting teleconnections Science Q article 2017 ftdoajarticles https://doi.org/10.3390/resources6030029 2022-12-30T20:03:48Z 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. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Merra ENVELOPE(12.615,12.615,65.816,65.816) Resources 6 3 29
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic renewable energy
interannual variability
seasonal forecasting
teleconnections
Science
Q
spellingShingle renewable energy
interannual variability
seasonal forecasting
teleconnections
Science
Q
Nir Y. Krakauer
Daniel S. Cohan
Interannual Variability and Seasonal Predictability of Wind and Solar Resources
topic_facet renewable energy
interannual variability
seasonal forecasting
teleconnections
Science
Q
description 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.
format Article in Journal/Newspaper
author Nir Y. Krakauer
Daniel S. Cohan
author_facet Nir Y. Krakauer
Daniel S. Cohan
author_sort Nir Y. Krakauer
title Interannual Variability and Seasonal Predictability of Wind and Solar Resources
title_short Interannual Variability and Seasonal Predictability of Wind and Solar Resources
title_full Interannual Variability and Seasonal Predictability of Wind and Solar Resources
title_fullStr Interannual Variability and Seasonal Predictability of Wind and Solar Resources
title_full_unstemmed Interannual Variability and Seasonal Predictability of Wind and Solar Resources
title_sort interannual variability and seasonal predictability of wind and solar resources
publisher MDPI AG
publishDate 2017
url https://doi.org/10.3390/resources6030029
https://doaj.org/article/b0a9f84b13264d039c133180129df5ed
long_lat ENVELOPE(12.615,12.615,65.816,65.816)
geographic Arctic
Merra
geographic_facet Arctic
Merra
genre Arctic
genre_facet Arctic
op_source Resources, Vol 6, Iss 3, p 29 (2017)
op_relation https://www.mdpi.com/2079-9276/6/3/29
https://doaj.org/toc/2079-9276
2079-9276
doi:10.3390/resources6030029
https://doaj.org/article/b0a9f84b13264d039c133180129df5ed
op_doi https://doi.org/10.3390/resources6030029
container_title Resources
container_volume 6
container_issue 3
container_start_page 29
_version_ 1766339122541101056