1980-2010 Variability in U.K. Surface Wind Climate [Journal of Climate]
By Von Glasow, Roland | |
Proquest LLC |
ABSTRACT
The climate of the northeast Atlantic region comprises substantial decadal variability in storminess. It also exhibits strong inter- and intra-annual variability in extreme high and low wind speed episodes. Here the authors quantify and discuss causes of the variability seen in the
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1. Introduction
Located in one of the most common regions for atmospheric blocking, while also situated toward the end point of a major midlatitude storm track, the
The cold European winter of 2009/10 and the extreme cold of
In
Wang et al. (2009) demonstrated that storminess in the North Atlantic-European region, based on atmospheric sea level pressure gradients, undergoes substantial decadal and longer time scale fluctuations and that these changes have a seasonality and regionality to them. In particular, these authors showed that winter storminess reached an unprecedented maximum in the early 1990s in the
Both the wind energy and insurance industries are sensitive to wind speed distributions. The Weibull distribution function has become widely used in meteorology to estimate how observed wind speeds tend to vary around their mean at sites where only a long-term average is known. Originally used to describe the size distribution of particles, the Weibull distribution has numerous applications, including in general insurance to model reinsurance claim sizes (Kremer 1998). The use and importance of the Weibull distribution has grown immensely in the wind power industry and has been used to help site many thousands of wind turbines (
Numerous authors have also been considering the possible impact of climate change over the twenty-first century on the wind climate of northwest
Hewston (2008) and Hewston and Dorling (2011) introduced for the first time an hourly wind speed database for a network of 43 U.K. surface stations, extending through the period 1980-2005 and providing good spatial coverage. Based on this they presented a climatology of the strongest wind gusts in the context of insurance weather perils. These authors presented evidence of an apparent downward trend in the strongest wind gusts over the
Here we build on the earlier
* update analysis of temporal variability to 2010 and extend the quality control of the Hewston and Dorling database;
* deepen understanding of each of the stations in the network by investigating applicability of the Weibull distribution across locations, interpreting the results from a topographic perspective;
* analyze variations of exceedances of a wider range of wind speed thresholds of interest to both the insurance andwind energy sectors, compare thesewith the largerscale findings of Vautard et al. (2010), and discuss them in the context of key features of the regional-scale atmospheric circulation; and
* quantify the impact of the observed spatial and temporal variations in wind power on output from a synthetic network of 3.6-MW wind turbines, one located at each of the monitoring stations.
The results presented in this paper include analysis and discussion of wind speed threshold exceedance frequencies, the proportion of time that the hourly winds or daily gust speeds are above a set of specific speeds, at individual sites and on average across the network of 40 (39) hourly wind speed (gust speed) sites. This follows the approach adopted by Vautard et al. (2010) but provides detail for the
2. Data, methods, and tools
a. Observed wind data
This study extends the 1980-2005 database described by Hewston and Dorling (2011) of hourly surface wind speed observations (measured at the standard 10-m height) from the
b. Data quality
The wind speed and direction data has undergone rigorous quality control, with checks on the equipment and raw data performed at the UKMO and the BADC. Further information on quality control performed on the MIDAS database and other possible sources of error is available at the BADC website (http://badc.nerc.ac. uk/data/ukmo-midas/ukmo_guide.html; UKMO 2011) and in Hewston and Dorling (2011). Once downloaded, a series of steps were followed to further test the reliability of the information, removing duplicate data, detecting missing values, and checking data consistency. Analysis of Weibull distributions, discussed below, was also helpful in highlighting potential anomalies. The MIDAS data do not normally include an HM value of 1 kt (0.515 m s21) and often use a value of 2 kt (1.03 m s21) when the wind vane indicates gusty conditions (BADC website) to represent a mean speed of 0 or 1 kt. This leads to an overrepresentation of HM wind values of 2 kt and an underrepresentation of 0 and especially 1 kt at many sites. We have, however, made no attempt to redistribute these extra 2-kt values into neighboring bins.
c. Weibull distribution
The Weibull distribution came to prominence in meteorology during the 1970s (Takle and Brown 1978). As a two-parameter density function it can be calculated as
... (1)
where P(U) is the probability distribution of wind speed U, A is the Weibull scale parameter, and k is the shape parameter (Pryor and Barthelmie 2010). For a narrow distribution, with a marked peak, k will take a relatively high value. Numerous statistical methods have been proposed to calculate Weibull scale and shape parameters (Pryor et al. 2004), with Seguro and Lambert (2000) recommending the maximum likelihood method when wind speed data is available in a time series format. When the Weibull shape parameter has a value of 2, it is known as the Rayleigh distribution, and this is often used as the standard for wind turbine manufacturers' performance figures (Weisser 2003). The Weibull distribution, however, has been found to produce a better fit to observed wind speeds than the simpler Rayleigh distribution (Celik 2004).
Nevertheless it is problematic fitting a Weibull distribution at low wind speeds, as highlighted by Justus et al. (1976), who assessed potential output from windpowered generators. On the other hand, it is generally accepted that sites with regular moderate or high wind speeds can almost always be approximated by the Weibull distribution (
Both the 31-yr
d. Wind turbine power
The 31-yr
... (2)
where E is energy density (W m22), r is air density (kg m23), and U is the hub-height wind speed (m s21) (Pryor et al. 2012). For this study, the energy density for each of the 40 HM observation sites is calculated to first order with Eq. (2), using an air density of 1.225 kg m23 (158C at sea level) and assuming negligible density variations (Pryor et al. 2004; Jamil et al. 1995), ignoring altitude and temperature variability between sites (which could theoretically lead up to an associated 68% air density variation compared to the average value adopted).
A limitation of the applicability of the energy density quantity is that even the most modern wind turbines cannot harvest power below and above specific wind speed thresholds (Table 1). Outside this range, the wind speed is either too low to turn the blades or too high, forcing the turbine to be shut down in order to prevent damage (Forster et al. 2011). Based purely on the cubic relationship between wind speed and power generation, energy density returns an overestimation of wind turbine performance, especially during stormy periods such as the early 1990s. For comparison, another method is also used to quantify wind turbine performance to second order, including cut-in and cut-out wind speed thresholds and sensitivity to wind speed variations within that range (Oswald et al. 2008). For each of the 40 HM observation sites, a synthetic state-of-the-art 3.6-MW wind turbine is considered for the duration of the recorded observations and the 10-m winds are adjusted to the typical hub height of 100 m using the power-law approximation, ignoring the important effect of variable atmospheric stability and surface roughness (z0) for this simple estimate (
... (3)
where U(z1) and U(z2) are the wind speeds at heights z1 and z2, respectively, and p is the power-law exponent taken to be equal to 0.14 (
e. North Atlantic Oscillation
The HM and DMGS 1980-2010 wind speed database presents an excellent opportunity to investigate the relationship between theNAOindex andU.K. wind speeds and assess the impacts of the phase changes of the NAO on land-based wind measurements and wind energy output estimates. This furthers the work of Cheng et al. (2011), who used satellite observations to investigate interannual variability of high wind occurrence in the North Atlantic over the period 1988-2009. The particular NAO index used for this study is based on normalized sea level pressure observations made at
3. Results and discussion
a. Interannual variability
Figure 2 shows a time series of annual average 10-mHM wind speeds in the form of the 10th, 50th, and 90th percentiles, quantifying the intersite variability. The 10th and 50th percentile 5-yr moving averages exhibit peaks in the early 1980s and early 1990s, with a general statistically significant decrease visible over the full 1980-2010 period (confidence levels of 99.9% and 95% for the 10th and 50th percentiles, respectively, using ordinary least squared linear regression analysis). The 90th percentile shows a much more pronounced early 1990s peak, without the general decline seen in the 10th and 50th percentiles, but with a statistically significant decrease since 1990 (at the 99% level). The 10th and 50th percentiles show that in the mid to late 2000s wind speeds began to recover; however, the anomalously low winds of 2010, discussed in detail below, are at odds with this recovery. Figure 2 highlights large year-to-year variability in wind speeds for all percentiles-for example, the median varying from 4.3 to 5.3 m s21. Our results and those of other authors highlight the presence of strong decadal variability and we include linear trend analyses here only for completeness. Behind these results from the network as a whole, it should be noted that 32 of the 40 sites display a decrease in annual mean wind speed over the full period, 15 of which are statistically significant (95% confidence level), while 8 show an increase, 2 of which are statistically significant. There is no clear geographical pattern to the distribution of stations exhibiting statistically significant changes.
To learn more about the nature of winds experienced in the
The proportion of time when the network average HM wind speed exceeds the 11 m s21 threshold ranges from just over 2% of the time in 2010, due to the cold and relatively calm months of January and December that year (see 2010 wind speed and direction in Fig. 4d), to 6.7%in 1990, associatedwith the storminess of January and February. The interannual variation is striking with, for an extreme example, 1986 experiencing winds in excess of 11 m s21 for twice as many hours as in the previous and following years, a feature also reported by Vautard et al. (2010) for
Figures 2 and 3 reveal a large change between the adjacent years 1986 and 1987, with 1986 recording far higher wind speeds. To further investigate this difference, network average wind roseswere produced for both years [Figs. 4b,c; also shown are the 1980-2010 climatology (Fig. 4a) and the extreme year of 2010 (Fig. 4d)], with 1986 revealing a much more pronounced tendency for southwesterly winds. This is to be expected with stronger southwesterly winds associated with the extratropical cyclone storm track. Increased southwesterly winds are positively correlated with the NAO (Cheng et al. 2011) and the monthly NAO index is significantly more positive in January, October, November, and December in 1986 than in the equivalent 1987 months.
The peaks of the early 1980s and early 1990s are further highlighted by the 5-yr running mean of network averageHMwind speed threshold exceedance shown in Fig. 5, although the early 1980s peak is not as pronounced as in the 10th and 50th percentiles of site HM wind speeds shown in Fig. 2. In Fig. 5, in addition to the 11 and 13 m s21 exceedance thresholds shown in Fig. 3, further thresholds of 3, 5, 7, 9, and 15 m s21 are also included. Although the logarithmic scale somewhat reduces the visual impact of the variability, nevertheless a statistically significant decrease ($99% confidence) over the last 20 years remains visible for exceedance thresholds in the range 7-15 m s21. As expected, the contribution of individual sites to the total exceedance percentage varies throughout the network, especially as the exceedance thresholds rise and become of interest for the insurance sector. [This is discussed in detail below (section 3e), with Fig. 10a highlighting the site contribution variations for the 15 m s21 threshold.]
One of the findings of Vautard et al. (2010) was a general decline in European wind speeds over the last 30 years, especially for extreme winds, whereas
The DMGS exhibits a similar long-term variability to that of theHMas depicted by the 5-yr moving average of network averageDMGS threshold exceedance shown in Fig. 6. Higher thresholds are included here compared with the HM analysis, ranging from 9 to 35 m s21, revealing peaks in the early 1980s and early 1990s with the exception of the highest 35 m s21 exceedance threshold, which does not have such a marked peak in the early 1980s but amore extrememaximumin the runningmean around 1991/92. The 35 m s21 1980-2010 decline is statistically significant (with 99% confidence), accommodating a peak in 1993, with the wind speed exceeding the threshold 0.5% of days (at all sites), compared to 2001 and 2010 when this threshold was not breached at all (not shown).
Sensitivity tests of the interannual variability of threshold exceedances to the network configuration have been carried out (not shown), based on the removal of the most significant contributor stations to the 15 (HM) and 25 m s21 (DMGS) exceedance thresholds in Figs. 5 and 6, respectively.While the removal of these stations leads to inevitable quantitative changes of exceedance percentage, the interpretation of the periods of enhanced and reduced exceedance remains unchanged, indicating low sensitivity to specific station choice.
b. North Atlantic Oscillation: Driver of temporal wind climate variations
Positive peaks in the NAO index are seen in the early 1980s and particularly in the early 1990s when the 10-yr Gaussian-weighted filter was at its highest during the whole 189-yr time period (CRU website). The decrease since the early 1990s is apparent, and partly explains the declining tendency in HM and DMGS
To investigate the effects that the NAO index variations have on the observed
c.
The considerable intra-annual wind variation in the
The spring 15 m s21 exceedance percentage (Fig. 8) generally hovers around 0.5%, peaking at over 1% in 1994. Autumn, meanwhile, does not reveal a peak during the early 1990s, but was more extreme instead at the start of the observation period during the early 1980s and also peaked in the late 1990s before declining once more, partially consistent with the findings of Vautard et al. (2010), during 1979-2008, that the most substantial linear decrease in
Because the seasonal variation of the HM wind exceedance threshold of 15 m s21 is so strong, especially between winter and summer, we show in Fig. 9 the network average wind direction distribution for each season over the 1980-2010 period. All of the seasons are dominated, on average, by winds from the southwest quadrant, winter unsurprisingly having the strongest such winds, associated with the storm track moving south during the Northern Hemisphere winter (Dacre and Gray 2009). Autumn has a similar-looking wind rose to that of winter, whereas summer and spring have different appearances, summer having a more influential northwest quadrant (and lower wind speeds overall) and spring a more significant northeasterly component. During summer the Atlantic westerlies are less dominant with the storm track pushed north by the Azores high, leading to climatologically more high pressure systems centered to the west of the
d. Spatial variability
When dealing with the network average of exceedance thresholds, spatial variability is hidden. Spread across the
Inland sites rarely contribute to either exceedance threshold compared with their more coastal neighbors. The inland northern sites of Eskdalemuir (31) and Salsburgh (33) are situated only 50 miles from each other and have similar altitudes of 242 and 277 m, respectively; however, Salsburgh contributes far more to the 15 and 25 m s21 exceedance thresholds (just under 10%for each), with Eskdalemuir not exceeding 25 m s21 at all during the 1980-2010 period. Eskdalemuir is situated in a north-south-oriented valley, with tree-covered ridges on either side, whereas the Salsburgh monitoring site is located on an exposed grass covered hill with a large flat top to the north and east. Centrally located in
Wind roses are shown for the directions of HM winds exceeding the thresholds of 15 and 25 m s21, to confirm where the strongest winds originate (Fig. 11). The 15 m s21 and the 25 m s21 thresholds are dominated by southwesterly winds with the southwest quadrant (1908- 2708) accounting for 59.9 and 78.9%, respectively, as Hewston and Dorling (2011) found for extreme (top 2%) DMGSs.
The DMGS 1980-2010 39-site network average wind rose (not shown) is similar to that of the HM (Fig. 4a), with the proportion of wind direction for each quadrant (Table 2) also extremely similar. This is the same when comparing individual site HM wind roses (Fig. 10) with equivalentDMGSwind roses (not shown). This suggests that the factors, whether site aspect, local-scale flow, or synoptic-scale flow, that contribute to the direction of HM winds are the same for DMGSs.
e. Application of the Weibull function to describe wind speed distributions
The spatial variation of wind speeds in the
The Weibull distribution describes the observed HM winds well as shown by the histograms in Fig. 12. The Weibull distribution provides a better fit to the sites with comparatively few low wind speeds, as shown when comparing the sites of
Weibull shape parameter (k) values seem to be a function of both the strength of the mean wind and the impact of site characteristics. Sites with very low wind speeds such as East Malling (8) produce low values of k, due to the high counts of low wind values; however, other sites with higher means but with anomalous wind roses (varying greatly from that of the network average, affected by local site characteristics; Fig. 10) such as Bala (17) and West Freugh (30) also have low k (not shown), associated with topographic effects such as local valley flows. Sites with lowmeans but evenly distributed (similar to network average) wind roses such as
The Weibull distribution does not approximate the DMGS distribution as accurately as for theHMwinds as shown by Fig. 13. The k values are much higher than for the HMs, ranging between ;2.4 and ;2.9, which is unsurprising given that the use of the DMGS metric eliminates many low values. The wind speed threshold of 12 m s21 required for a good Weibull fit according to Jamil et al. (1995) seems not to be reliable for DMGSs, with sites possessing averages above and below 12 m s21, being underestimated for the most frequent values and overestimated for the lower wind speeds (Fig. 13). Generally the tails of the distributions are well approximated for the higher average DMGS sites and slightly overestimated for the sites with lower average DMGS.
f. Wind energy implications
The HM wind speeds have been converted into network average energy density and potential power output (PPO) of a synthetic wind turbine network. Table 2 highlights just how important the southwest quadrant is for wind power production. Both methods show significant year-to-year variability of power output over the 1980-2010 period (Fig. 14), as originally seen in the annual average percentile HM wind speeds (Fig. 2), in the HM threshold exceedances (Figs. 3 and 5), in the DMGS threshold exceedances (Fig. 6), and in the NAO index (CRU website). Peaks in energy density and PPO are seen in the early 1980s and early 1990s and are clearly displayed by the 5-yr moving averages. The anomalous year of 2010 stands out in both energy metrics, representing the lowest values of thewhole period; the extreme variability of consecutive years 1986 and 1987 is also clear. The main difference between the two methods is the more marked peak in the early 1990s in energy density. The unprecedented storminess described by Wang et al. (2009) of the early 1990s produced the most extreme winds of the period in the
The range of annual mean PPO is large, 867-1265 kW (2010 and 1986 respectively) with an average of 1087 kW. During the highest production year, the synthetic 3.6-MW wind turbine network was working on average at 35% efficiency (or load factor; with the assumption of steady winds) and at 24% efficiency for the lowest production year. The year 1986 saw 16% more energy generated than the 1980-2010 average whereas 2010 was 20% below. The energy produced in 1987 was just 73% of that of 1986, a much larger difference than the interannual variability in wind energy density that
The demand for electricity in the
4. Conclusions and outlook
The characteristics of the
* The 10th and 50th (but not the 90th) percentile HM wind speeds have declined significantly over this specific period, while still incorporating a peak in the early 1990s. 2010 recorded the lowest annual 10th and 90th percentile and second lowest (behind 1987) 50th percentile wind speed over the whole 1980-2010 period (Fig. 2). This is all, however, in the context of longer-term decadal variability.
* The Weibull distribution is more suited to representing HM winds rather than DMGS distributions at typical land-based sites, the former revealing sitespecific shape parameter values ranging from 1.4 to 2.1 (Fig. 12), somewhat in contrast with the often assumed k 5 2 Rayleigh distribution, with associated implications for turbine site selection.
* As theHMexceedance thresholds rise, the early 1980s peak in exceedance frequency diminishes, while the early 1990s peak becomes more apparent (Fig. 5), with a declining tendency since, confirming the early 1990s unprecedented peak in northeast Atlantic winter storminess reported by Wang et al. (2009). This is not fully consistent with Vautard et al. (2010), who highlighted a temporally broader decline for the whole of
* The DMGS exceedance thresholds exhibit similar variations to those of the HM, with the highest thresholds (30 and 35 m s21) displaying the most marked early 1990s peak and a decline since (Fig. 6), indicating that the decrease of extreme DMGSs highlighted by Hewston and Dorling (2011) has continued through to 2010, contributing to the reduction in
* The network average 1980-2010 HM prevailing wind direction is in the southwest quadrant (40% of the time). However, significant seasonal and interannual variation is apparent in the relative frequency of all wind directions and this needs to be accounted for in wind energy assessments.
* The 40% frequency in southwest quadrant winds translates into a 51% proportion of energy in the wind (Table 2).
* The range of network average annual mean potential power output is significant, from 220% to 116% around the average, with the synthetic energy produced in 1987 just 73% of the previous year, 1986, and 2010 the lowest producing year of all (Fig. 14).
The recent variability in
Future climate projections have a large spread between models and low signal-to-noise ratio over
Acknowledgments. This research was kindly funded by the
REFERENCES
Atkinson,N., K.Harman,M.Lynn, A.
Barriopedro,D.,R.Garcýá-Herrera,
_____, _____, and
Boccard, N., 2009: Capacity factor of wind power realized values vs. estimates. Energy Policy, 37, 2679-2688.
Brayshaw, D. J., A. Troccoli,
Brown, S.,
Cattiaux, J.,
Celik, A. N., 2004: A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of
Cheng, X., S. Xie, H. Tokinaga, and Y. Du, 2011: Interannual variability of high-wind occurrence over the North Atlantic. J. Climate, 24, 6515-6527.
Dacre, H. F., and
Forster, D.,M. Benzie, S.Winne, and
Gronas, S., 1995: The seclusion intensification of the
Harrison, G.,
Hawkins, E., and
Hess, P., and H. Brezowsky, 1952: Katalog der Grosswetterlagen Europas. Berichte das Deutschen Wetterdienstes in der USZone 33, 39 pp.
Hewston, R., 2008:Weather, climate and the insurance sector. Ph.D. dissertation, University of
_____, and S. R. Dorling, 2011: An analysis of observed maximum wind gusts in the
Hurrell, J. W., Y. Kushnir, G. Ottersen, and
Irwin, J. S., 1979:A theoretical variation of the wind profile powerlaw exponent as a function of surface roughness and stability.
James, P. M., 2007: An objective classification method for Hess and Brezowsky Grosswetterlagen over
Jamil, M., S. Parsa, and
Jones,
_____, T. Jonsson, and
Jung, T., F. Vitart,
Justus, C. G.,
Klawa, M., and U. Ulbrich, 2003: A model for the estimation of storm losses and the identification of severe winter storms in
Kremer, E., 1998: Largest claims reinsurance premiums for the Weibullmodel. Blätter Dt.Ges. Versicherungsmath., 23, 279-284.
Lockwood, M.,
_____,_____,
Malmquist, D. L., Ed., 1999: European windstorms and the North Atlantic Oscillation: Impacts, characteristics, and predictability. RPI Series 2,
McCallum, E., 1990: The Burns' day storm,
Motta, M., R. J. Barthelmie, and P. Vølund, 2005: The influence of non-logarithmic wind speed profiles on potential power output at Danish offshore sites. Wind Energy, 8, 219-236.
Osborn, T. J., 2011: Winter 2009/2010 temperatures and a recordbreaking North Atlantic Oscillation index. Weather, 66, 19-21.
Oswald, J.,
Pöyry, 2011: The challenges of intermittency in North West European power markets. Summary Rep., 16 pp.
Pryor, S. C., and R. J. Barthelmie, 2010: Climate change impacts on wind energy: A review. Renewable Sustainable Energy Rev., 14, 430-437.
_____,
_____, R. J. Barthelmie,
Rodwell, M. J.,
Scaife, A. A., and Coauthors, 2012: Climate change projections and stratosphere-troposphere interaction. Climate Dyn., 38, 2089- 2097, doi:10.1007/s00382-011-1080-7.
Seguro, J. V., and
Serreze, M. C., F. Carse,
Sinden, G., 2007: Characteristics of the
Takle, E. S., and
Troccoli, A.,
UKMO, cited 2011:
Ulbrich, U.,
Vautard, R.,
Wang, X. L.,
Weisser, D., 2003: A wind energy analysis of
Wilks, D. S., 1990: Maximum likelihood estimation for the gamma distribution using data containing zeros. J. Climate, 3, 1495-1501.
Woollings, T., 2010: Dynamical influences on European climate: An uncertain future. Philos.
_____,
NICK EARL AND STEVE DORLING
(Manuscript received
Corresponding author address:
E-mail: [email protected]
Copyright: | (c) 2013 American Meteorological Society |
Wordcount: | 9965 |
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