Interannual variability of wind speeds presents a fundamental source of uncertainty in preconstruction energy estimates. Our analysis of one of the longest and geographically most widespread extant sets of instrumental wind-speed observations (62-year records from 60 stations in Canada) shows that deviations from mean resource levels persist over many decades, substantially increasing uncertainty. As a result of this persistence, the performance of each site's last 20 years diverges more widely than expected from the P50 level estimated from its first 42 years: half the sites have either fewer than 5 or more than 15 years exceeding the P50 estimate. In contrast to this 10-year-wide interquartile range, a 4-year-wide range (2.5 times narrower) was found for "control" records where statistical independence was enforced by randomly permuting each station's historical values. Similarly, for sites with capacity factor of 0.35 and interannual variability of 6 \%, one would expect 9 years in 10 to fall in the range 0.32–0.38; we find the actual 90 \% range to be 0.27–0.43, or three times wider. The previously un-quantified effect of serial correlations favors a shift in resource-assessment thinking from a climatology-focused approach to a persistence-focused approach: for this data set, no improvement in P50 error is gained by using records longer than 4–5 years, and use of records longer than 20 years actually degrades accuracy.
(private-note)I met Mark Handschy at ICEM 2015 in Boulder; this paper cites both my long-ter wind var papers!
But is their argument, that autocorrelation means that you should only use the past 4-5 years (persistence) rather than long-term data (climatology), saying we're wrong in our 20CR/VMM approach?
%0 Journal Article
%1 Bodini2016Yeartoyear
%A Bodini, Nicola
%A Lundquist, Julie K.
%A Zardi, Dino
%A Handschy, Mark
%D 2016
%J Wind Energy Science
%K climatology energy renewables statistics wind
%N 2
%P 115--128
%R 10.5194/wes-1-115-2016
%T Year-to-year correlation, record length, and overconfidence in wind resource assessment
%U http://dx.doi.org/10.5194/wes-1-115-2016
%V 1
%X Interannual variability of wind speeds presents a fundamental source of uncertainty in preconstruction energy estimates. Our analysis of one of the longest and geographically most widespread extant sets of instrumental wind-speed observations (62-year records from 60 stations in Canada) shows that deviations from mean resource levels persist over many decades, substantially increasing uncertainty. As a result of this persistence, the performance of each site's last 20 years diverges more widely than expected from the P50 level estimated from its first 42 years: half the sites have either fewer than 5 or more than 15 years exceeding the P50 estimate. In contrast to this 10-year-wide interquartile range, a 4-year-wide range (2.5 times narrower) was found for "control" records where statistical independence was enforced by randomly permuting each station's historical values. Similarly, for sites with capacity factor of 0.35 and interannual variability of 6 \%, one would expect 9 years in 10 to fall in the range 0.32–0.38; we find the actual 90 \% range to be 0.27–0.43, or three times wider. The previously un-quantified effect of serial correlations favors a shift in resource-assessment thinking from a climatology-focused approach to a persistence-focused approach: for this data set, no improvement in P50 error is gained by using records longer than 4–5 years, and use of records longer than 20 years actually degrades accuracy.
@article{Bodini2016Yeartoyear,
abstract = {Interannual variability of wind speeds presents a fundamental source of uncertainty in preconstruction energy estimates. Our analysis of one of the longest and geographically most widespread extant sets of instrumental wind-speed observations (62-year records from 60 stations in Canada) shows that deviations from mean resource levels persist over many decades, substantially increasing uncertainty. As a result of this persistence, the performance of each site's last 20 years diverges more widely than expected from the P50 level estimated from its first 42 years: half the sites have either fewer than 5 or more than 15 years exceeding the P50 estimate. In contrast to this 10-year-wide interquartile range, a 4-year-wide range (2.5 times narrower) was found for "control" records where statistical independence was enforced by randomly permuting each station's historical values. Similarly, for sites with capacity factor of 0.35 and interannual variability of 6 \%, one would expect 9 years in 10 to fall in the range 0.32–0.38; we find the actual 90 \% range to be 0.27–0.43, or three times wider. The previously un-quantified effect of serial correlations favors a shift in resource-assessment thinking from a climatology-focused approach to a persistence-focused approach: for this data set, no improvement in P50 error is gained by using records longer than 4–5 years, and use of records longer than 20 years actually degrades accuracy.},
added-at = {2018-06-18T21:23:34.000+0200},
author = {Bodini, Nicola and Lundquist, Julie K. and Zardi, Dino and Handschy, Mark},
biburl = {https://www.bibsonomy.org/bibtex/215796d14c2709476ddb817e29e45615e/pbett},
citeulike-article-id = {14121219},
citeulike-linkout-0 = {http://dx.doi.org/10.5194/wes-1-115-2016},
comment = {(private-note)I met Mark Handschy at ICEM 2015 in Boulder; this paper cites both my long-ter wind var papers!
But is their argument, that autocorrelation means that you should only use the past 4-5 years (persistence) rather than long-term data (climatology), saying we're wrong in our 20CR/VMM approach?},
day = 24,
doi = {10.5194/wes-1-115-2016},
interhash = {c5503632251829fefc04a7bc16dc0fcb},
intrahash = {15796d14c2709476ddb817e29e45615e},
issn = {2366-7451},
journal = {Wind Energy Science},
keywords = {climatology energy renewables statistics wind},
month = aug,
number = 2,
pages = {115--128},
posted-at = {2016-08-24 15:38:32},
priority = {2},
timestamp = {2018-06-23T13:36:59.000+0200},
title = {Year-to-year correlation, record length, and overconfidence in wind resource assessment},
url = {http://dx.doi.org/10.5194/wes-1-115-2016},
volume = 1,
year = 2016
}