Ten years of ERA5 reanalysis data are combined with met-mast and lidar
observations from 10 offshore platforms to investigate low-level jet
characteristics over the Dutch North Sea. The objective of this study is to
combine the best of two worlds: (1) ERA5 data with a large spatiotemporal
extent but inherent accuracy limitations due to a relatively coarse grid and
an incomplete representation of physical processes and (2) observations that
provide more reliable estimates of the measured quantity but are limited in
both space and time. We demonstrate the effect of time and range limitations
on the reconstructed wind climate, with special attention paid to the impact
on low-level jets.
For both measurement and model data, the representation of wind speed is
biased. The limited temporal extent of observations leads to a wind speed
bias on the order of ±1 m s-1 as compared to the long-term mean. In
part due to data-assimilation strategies that cause abrupt discontinuities in
the diurnal cycle, ERA5 also exhibits a wind speed bias of approximately 0.5 m s-1. The representation of low-level jets in ERA5 is poor in terms of a
one-to-one correspondence, and the jets appear vertically displaced (“smeared
out”). However, climatological characteristics such as the shape of the
seasonal cycle and the affinity with certain circulation patterns are
represented quite well, albeit with different magnitudes. We therefore
experiment with various methods to adjust the modelled low-level jet rate to the
observations or, vice versa, to correct for the erratic nature of the short
observation periods using long-term ERA5 information. While quantitative
uncertainty is still quite large, the presented results provide valuable
insight into North Sea low-level jet characteristics. These jets occur
predominantly for circulation types with an easterly component, with a clear
peak in spring, and are concentrated along the coasts at heights between 50 and 200 m.
Further, it is demonstrated that these characteristics can be used as
predictors to infer the observed low-level jet rate from ERA5 data with
reasonable accuracy.
Introduction
On average, wind speed increases with height above the surface and the rate
of increase can be described using simple formulas (e.g. power-law or
logarithmic profile; see ). Due to their simplicity and ease
of use, these wind profile parameterisations have been widely
adopted in the wind energy community. However, in some situations these
formulas cannot adequately capture the observed wind profile. During these
situations, the application of a simplified wind profile parameterisation can
introduce error or “uncertainty” into the reconstructed wind climatology.
This is clearly the case for low-level jets (LLJs), for which wind speed reaches a
maximum not far (i.e. roughly less than 500 m) from the surface
(Fig. a).
In line with one of the core
messages of the paper, i.e. that the climatological characteristics of low-level
jets are to be seen with some uncertainty, all figures in this paper
have been rendered in a less formal style.
Wind shear and turbulence intensity
associated with low-level jets also differ substantially from those assumed
under “standard” conditions.
(a) Example low-level jet profile as compared to the “standard”
logarithmic wind profile. (b) Preliminary spatial distribution of annual
low-level jet occurrence based on 10 years of ERA5 data up to 500 m. Overlaid are the location of
the 10 measurement platforms used in this analysis: Met Mast IJmuiden (MMIJ), Hollandse Kust
Noord A (HKNA) and B (HKNB), Hollandse Kust Zuid A (HKZA) and B (HKZB), Lichteiland Goeree (LEG),
Borssele Wind Farm Lots 1 (BWF1) and 2 (BWF2), Europlatform (EPL), and K13. Colour coding is consistent across all figures.
Low-level jets modify wind power performance and loading by impacting wake
recovery rates and vertical profiles of wind speed, direction, and
turbulence .
Thus, for a complete assessment of loads and power, it is important to have a
broad understanding of the site-specific low-level jet characteristics: how
often do they occur, under which circumstances, at what height and with what
strength, and what mechanisms are responsible for their formation? A large
body of literature exists on low-level jets, the majority focusing on the
onshore phenomenon. We refer to for a global climatology and
to for a synthesis of the underlying mechanisms. In
coastal areas, the occurrence of low-level jets has been attributed to the
thermal contrast and differences in surface roughness between land and
sea (e.g. ). linked the
occurrence of coastal jets to their onshore counterpart. In certain areas,
other mechanisms like orographic forcing may play an important
role (e.g. ). Concerning the spatial and temporal variability
of the coastal jets, we refer to and , who
presented global maps based on reanalysis data. Their analyses highlight a
number of large-scale global “hotspots” that, in effect, overshadow more
regional phenomena. Consequently, a systematic long-term characterisation of
coastal jets is lacking for the North Sea.
In a previous publication , we reported on low-level jet
characteristics at a prospective wind power site 85 km off the Dutch coast
(MMIJ, a.k.a “IJmuiden ver”), using 4 years of mast and lidar observations.
The climatology consisted of the diurnal and seasonal variability in
low-level jet occurrence, jet speed, jet height, jet direction, etc.
Inherently, this low-level jet climatology is only valid for the single
observation site examined. In order to generalise the results from this
study and to improve our overall understanding of low-level jets across the
North Sea, we now present a spatial climatology of low-level jets based on
ERA5 reanalysis data (Sect. ; ) and an extended
set of observations.
Preliminary results based on 10 years of data in the lower 500 m of the
atmosphere (Fig. b) show that ERA5 provides
interesting information about the spatial distribution of low-level jets.
However, without observational support, this information is of little value.
Therefore, we incorporate additional lidar observations to provide this
support, but knowledge gained of the Dutch offshore wind climate from these
measurements is inhibited by the relatively short duration of measurement
collection (i.e. typically ∼1 year) and the limited vertical
measurement range (i.e. typically less than 300 m; see Fig. 2 and Appendix A for details
on measurement time and range). Consequently, the aim of this study appears
twofold: (1) observations will be used to validate the ERA5 climatology of
wind and low-level jets, and (2) ERA5 will be leveraged to infer long-term
low-level jet characteristics based on a limited set of observations.
Absolute agreement in low-level jet characteristics between the two data
sources would enable perfect execution of these objectives; however, that is
unlikely. Therefore, we formulated the following research question to
serve/blend both perspectives:
How can observations and reanalysis data be combined to obtain a spatial climatology
of low-level jets that is both rich (in its spatial and temporal extent) and reliable
(in terms of its correspondence with available in situ observations)?
The paper is structured as follows. A brief description of the data and an
elementary evaluation of wind speed itself is provided to illustrate how both
datasets are biased. Thereafter, low-level jet representation within both
datasets is discussed, starting with jet detection and morphology (e.g. jet
height). A common thread throughout the paper is how these characteristics
are impacted by time and (vertical measurement) range limitations. Using the
seasonal cycle of low-level jets as an illustrative example, we experiment
with various methods to post-process the ERA5 data and extend the
observations based on identified correspondence and/or differences. This
exercise is repeated for the diurnal cycle, atmospheric stability and various
circulation patterns. Finally, all of these characteristics are combined to
demonstrate that the “true” low-level jet rate can be reconstructed with
reasonable accuracy if sufficient observations are available. The paper ends
with a comprehensive discussion of the implications and future research
directions.
The focus of this paper is to obtain a reliable spatial representation of the
low-level jets. This provides clues as to the physical mechanisms that govern
them, but a detailed treatment of these processes is outside the scope of the
current work.
To facilitate transparency and reproducibility, a series of Jupyter notebooks is available
as a Supplement to this paper. Consequently, some technical details are left
out of the main text, which is intended as a readable and coherent treatment of the most important results.
A brief description of both datasets and their shortcomings
Observations are available from seven sites
(Fig. b). Three of these sites had two lidars
operating simultaneously and one site (MMIJ) also featured a 90 m met mast.
The temporal span of measurements ranges from 6 months to over 4 years (Fig. ). Some of the lidars were placed in the vicinity of
existing wind farms and are appropriately filtered to remove any potential
wind farm wake effects. More information on quality control and
post-processing of the lidar data can be found in Appendix A. The
observations are available as 10 min averages, but to facilitate
comparison with ERA5, the data were converted to hourly averages.
(a) Time–height plots of wind speed for each platform, illustrating
the data collection periods, temporal overlap between platforms and episodes
of missing data. (b) Site-specific measurement heights. Reference elevation
for the ERA5 data have been included for comparison. The colour coding in
(a) highlights episodes of high (yellow/green) and low (blue) wind speed.
ERA5 is the latest reanalysis dataset from the European Centre
for Medium-range Weather Forecasts (ECMWF). Re(trospective) analysis is the
procedure of fitting a state-of-the-art weather model to historical
measurements (satellites, weather stations, etc.) to obtain a long-term
dataset that is both spatially and physically consistent and depicts the
state of the atmosphere as it evolved through time. ERA5 is the successor of
ERA-interim, and similarly ERA5 is expected to be widely used for wind
resource assessment studies . Compared to its predecessor,
ERA5 has a finer horizontal grid of about 30 km and also enhanced vertical
resolution (for this study, data were retrieved on a 0.3∘ by
0.3∘ latitude–longitude grid). ERA5 is based on a newer model version
and, moreover, provides output at hourly intervals, enabling a comprehensive
analysis of sporadic features such as low-level jets. ERA5 data from the
North Sea domain between 2008 and (the end of) 2017 in the lowest 500 m demonstrates the ability of the model to resolve low-level jets
(Fig. b).
Before analysing the morphology of these jets, we illustrate the limitations
of both datasets concerning the representation of wind speed.
Figure a shows averaged wind profiles for the grid points
closest to each of the measurement locations (we verified that this approach
is comparable to spatial interpolation between multiple neighbouring grid
points). The full lines represent all 10 years of ERA5 data,
Some
lines are exactly on top of each other because they are in the same grid
point. Both are plotted, though, to preserve colour coding.
whereas the
dashed lines indicate averaged wind profiles derived from data subsets, which
only incorporate ERA5 data when observations are available. The full lines
are all quite close together, while the data subsets exhibit a much larger
spread. Variability between the full lines can be related to physical
differences between sites (e.g. distance to coast). Dissimilarity between the
ERA5 10-year datasets and the ERA5 data subsets indicates that, due to the
limited time extent of the observations, the data subsets are not
representative of the site climatology. For some sites, this
representativity bias almost reaches 2 m s-1, and even
for MMIJ, wherein measurements occurred for the longest period, it still
amounts to ∼0.5 m s-1. The primary reason for this bias at MMIJ is
that the data contain more winter than summer months, and the wind is
generally stronger in winter. Because the MMIJ data span more than 4 years,
some of them can be discarded in order to ensure an equal representation of the
seasons within the data. However, at the other stations, the temporal period
of observation is limited, and using a similar seasonality filter would
result in almost half of the data being removed, which is not desirable.
Worse still, Hollandse Kust
Noord (HKN) observations do not encompass a complete year, and even if
they did, inter-annual variability can be substantial. Available observations
therefore cannot be used to derive the long-term wind climatology directly.
However, by correlating a short-term dataset with long-term observations at a
nearby site, the long-term wind characteristics at the target site can be
inferred with reasonable accuracy. This procedure is known as
measure–correlate–predict (MCP; ). While not discussed
here, the application of similar techniques to the low-level jet phenomena will
be examined later in this document.
ERA5 also demonstrates bias in its representation of site winds. An
error diagram of the wind speed in ERA5 (subsets) versus
observations is provided in Fig. c. In this
diagram (co-opted from ), the mean error (BIAS) is
plotted on the x axis, the standard deviation of the error distribution (STDE) is plotted on the y axis and, by virtue of the relation
BIAS2+STDE2=RMSE2, the distance to the
origin represents the root mean square error (RMSE). Wind speed data from all
observation levels were aggregated in this figure to evaluate the overall
performance of ERA5 at each measurement site. For example, the Hollandse Kust Zuid (HKZ) lidars
show a strong bias (i.e. systematic error) but have a relatively small
standard deviation (i.e. random error). ERA5 site-specific RMSE values,
ranging from 1.25 to 1.5 m s-1, can be caused by multiple model aspects
such as the limited grid resolution and the incomplete representation of
physical processes. Uncertainties in the observations can also contribute to
overall error statistics. Based on the manufacturer information and previous
validation , the uncertainty in the observations can only
account for about 2 % of the errors. Finally, displacement in space or time as well as discrepancies between point-based measurements and modelled
control volumes can contribute to errors, although we did our best to
minimise these effects, e.g. by using appropriate time averaging of the
observations (see the Supplement).
The observed biases exhibit a strong diurnal variation. During the night
(Fig. b), the bias is roughly between 0 and -0.5 m s-1, depending on the location. However, at 10:00 UTC, there is a sharp
decrease in the bias of ∼-0.5 m s-1 for most stations. The reason
for this discontinuity can be found in the IFS (Integrated Forecasting
System) documentation . ERA5 is produced with a 4D-VAR
data-assimilation algorithm that uses two 12-hourly windows running between
09:00–21:00 and 21:00–09:00 UTC. This means that all hourly fields up to the 09:00 UTC analysis
are based on the nighttime observations, while data from 10:00 UTC onwards are
based on the daytime observations. We hypothesise that the impact of the
data assimilation is magnified during the nighttime because nighttime
boundary layers are generally shallower; the difficulty of appropriately
assimilating observational data within the (stable) boundary layer is
discussed in and . Discontinuity in the
diurnal cycle is present at each model level up to 300 m, irrespective of the
season and platform; however, it seems to be slightly stronger for those
stations closer to the coast.
(a) Averaged wind speed profiles for each measurement location,
based on 10 years of ERA5 data (full lines) and data subsets (dashed lines). (b) Mean (full lines) and
standard deviation (dashed lines) of the error between ERA5 (subsets) and the observations, for each
measurement site, as a function of the time of the day. (c) Error diagram of wind speed in
ERA5 (subsets) versus observations for all lidar datasets. Colour coding is the same in all subplots, so (c) can serve as a legend.
Jet detection: a precarious procedure
Low-level jets are identified by seeking local maxima in the wind profiles.
Having identified a local maximum, the jet strength, height, and fall-off are
analysed. Fall-off, as indicated in Fig. a, is
defined as the difference between the maximum and the subsequent (moving
upwards) local minimum or, if no local minimum is present, the top of the
wind profile. Most results in this study are based on an absolute fall-off
threshold of 2 m s-1. Figure demonstrates how this
threshold influences the low-level jet detection rate and further how the
detection of low-level jets is influenced by both time and (vertical
measurement) range limitations. The figure consists of five scatter plots,
each depicting the fall-off versus the jet height for each wind profile that
was detected with a local maximum. The differences between the panels are the
underlying data analysed – i.e. observations and varying subsets of ERA5
data.
The first panel (Fig. a) is based on 10 years of ERA5
data and the model levels contained within the lower 500 m of the atmosphere.
The two dashed lines represent limiting factors: (1) the fall-off threshold of
2 m s-1 (horizontal dashed line) and (2) limitations due to observation
height (vertical dashed line). The model data extend up to 500 m, but the
observations only reach up to about 300 m (depending on the platform). All
platforms are overlaid (shorter datasets on top). Only points above the
horizontal dashed line are included in the low-level jet climatology that is
presented in the next sections. The numbers in the top left corner of each
panel give the number of jets above the fall-off threshold and the total
number of jets plotted.
Figure b–d are based on subsets of the ERA5 dataset. In
panel (b), ERA5 data are incorporated only if observations are available; as
expected, this substantially limits the total number of low-level jets
(85 % reduction). In panel (c), we have retained all 10 years of data, but only at
observation heights (i.e. data above 300 m were discarded and the remaining
data were vertically interpolated – using a cubic spline – between the
remaining model levels to obtain the ERA5 wind speeds at the exact
observation height). The effect of this step is that 93 % of the meaningful
jet events (i.e. those exceeding the fall-off threshold) vanish, and not just
those above 300 m. In order to classify a wind profile as a jet, fall-off
above must be properly resolved. This explains why a jet at 100 m can also
vanish from the climatology if data from above 300 m are removed. The
pronounced impact of this vertical range limitation on the ERA5 data raises
the question of whether the observed low-level jet climatology would be
much different if we could observe higher-altitude winds. An increased
measurement range might reveal not only low-level jets above hub height, but
also new low-level jets at hub height that are currently not identified as
such.
Height and time limitations are combined in panel (d) in order to develop an
ERA5 dataset that is fair to compare with observations (panel e). Judging
from the figure, it seems that ERA5 does not perform well. Much fewer jets
are found above the fall-off threshold in the ERA5 data as compared to the
observations. Indeed, a more quantitative comparison in the form of a
contingency table, based on one-to-one (1:1) jet correspondence between the
two datasets, shows a very low critical success index (∼0.2) and
probability of detection (∼0.2; see the Supplement). In other
words, only 20 % of low-level jets are correctly represented by ERA5. Does
that imply that ERA5 is useless? No! Figure a indicates
that potentially relevant information was filtered out. Even though the
fall-off is typically much smaller (to the extent that it falls below the
fall-off threshold), the height distribution of the ERA5 jets seems similar to
the observations (also see Sect. 4). Perhaps the ERA5 jets appear
vertically displaced or just not strong enough? This would not come as a
surprise: weather models have long been known to generate excessive vertical
mixing under stable conditions, effectively “smearing out” low-level
jets . If the height thresholds for the ERA5 data are modified to 500 m, the 1:1 correspondence is still quite poor (critical
success index ∼0.2; probability of detection ∼0.5), but despite an
inability to accurately denote the total number of low-level jets, other
characteristics appear to be captured quite well – e.g. the average monthly
low-level jet rate. Therefore, the remainder of this paper is devoted to the
analysis of such low-level jet characteristics and methods to consolidate
ERA5 and measurement data.
Scatter plots of fall-off versus jet height for various
representations of model data and observations. In (a) and (b), the jet height
is represented by discrete model levels. Since these are specified in terms
of pressure rather than height, they can exhibit small height variations in
time. In (c), (d), and (e), jet height is represented by fixed measurement heights,
and to improve the readability of the graph we added small random perturbations
to these heights. See text for further explanation of the figure.
Vertical range affects perceived jet morphology
Jet height and jet strength are of paramount importance for wind energy
applications. Small variations in height can result in either symmetric or
asymmetric loads on the turbine, and typical strengths in the rated part of
the power curve are probably less critical than typical strengths in the
cubic part. It turns out, though, that the concepts of “typical” height and
strength are not self-evident.
Figure displays probability distributions of jet
strengths (panel a) and jet heights (panel b) for various representations of
the ERA5 data and observations.
Obviously, it is physically
impossible to have a jet strength or height below zero. This is an artefact
of the visualisation – it has a smoothing effect. We experimented with other
visualisations (smaller bandwidth or histograms) but found that this
visualisation best represented the underlying data.
It shows that the jet
height and strength distributions are sensitive to the range limitation. The
median observed jet strength is about 8 m s-1. This is quite well
reflected in the ERA5 data if we consider all levels up to 500 m, but after
imposing the range limitation, the jet strength is underestimated by about 3 m s-1. The observed median jet height is around 80 m. The ERA5 jet
height distribution is broader with greater jet heights for the data up to
500 m, while it is narrower with lower jet heights for the range-limited
data. To obtain a robust result, this figure is based on the aggregated data
from all platforms. Separate figures for each individual platform show
similar characteristics, although the jets near the coast seem to be somewhat
closer to the surface than jets further offshore (not shown).
Three different representations of the observations are included in
Fig. . The first one is based on the 10 min data.
The second is based solely on the data of each full hour; in other words, we
discarded five-sixth of the data. With this strategy, (small) discrepancies in
low-level jet timing can have a disproportionate impact on the results. A
more permissive evaluation (the third representation) is based on hourly
averages obtained with a sliding window, where each full hour is an average
including the 10 min data from the preceding two and the following three
time stamps. This last version of the observations is used throughout the
remainder of the paper. This figure demonstrates that the differences between
various resampling methods in terms of jet height and jet strength are small.
Kernel density estimates of the probability distribution of jet
strength (a) and jet height (b) for various representations of the ERA5 data
(full lines) and the observations (dashed lines), aggregated over all
stations.
Datasets agree: most jets in spring and summer
Figure displays the seasonal cycle of low-level
jets and, in a similar fashion as Fig. , how this cycle
is subject to time and range limitations. Over 10 years' time and 500 m height (panel a), the seasonal cycle is smooth and differences between the
individual platforms are small. Ideally, we would compare this to 10 years of
observations up to 500 m, but since those data are not available we take
spatial and temporal subsets of the ERA5 data instead. By investigating how
this affects the seasonal cycle, we identify methods to extend upon the
limited observations. Over the shorter measurement periods (panel b) the
seasonal cycle appears much more erratic than the 10-year climatology. Some
years are not very representative, and some datasets do not even cover a
complete cycle. As we will see later on, a favourable weather pattern for
low-level jets is a weak large-scale forcing typically associated with
high-pressure systems. Such “blocked” weather patterns can last for several
weeks, and their occurrence can thus cause large differences in monthly
low-level jet rates. In other words, the seasonal cycle based on only 1 or
a few years is very sensitive to inter-annual variability. Upon vertical
subsetting or interpolation to measurement heights (panel c), the seasonal cycle
is still visible, albeit with a much smaller amplitude. The combined effect
(panel d) leads to a very uninformative climatology because the monthly low-level
jet rates are all (close to) zero except for some unrepresentative spikes.
Based on panel (b), we expect that the observations are similarly affected by
the limited time window of the observations. Indeed, panel (e) shows an erratic
seasonal cycle with an amplitude somewhere between panels (b) and (d).
Thus, both datasets agree on the presence of an annual cycle, but the
amplitude differs between (various representations of) ERA5 and the
observations. Moreover, the observation periods are too short to obtain a
reliable climatology. To distill a more robust signal from the observations,
we combined the data from all sites before computing the monthly means and
smoothed the resulting signal with a moving average of 3 months. The
result is the dashed black line in panel (e). We then repeated these steps for
the ERA5 data (panels a–d), but before plotting these lines, we scaled them
with the observations, using a fixed scaling factor that is simply the ratio
between the mean low-level jet rate in the respective representation of ERA5
(panels a–d) and the mean of the observations (panel e). The result is
promising: the seasonal cycle is similar for all datasets, peaking at about
5 % in June. The crude manipulation of the data leads to a large error
margin, though, and we wonder whether we can find a more sophisticated
approach to achieve a similar result. Furthermore, because valuable
information is lost if we discard the ERA5 data above observation heights, we
will continue to work with the ERA5 data up to 500 m in the remainder of this
paper.
Seasonal cycle for various representations of model data and
observations. Shading is the sensitivity to ±0.5 m s-1 for the LLJ
fall-off threshold. The dashed lines represents an aggregated seasonal cycle
of all platforms, smoothed with a rolling average of 3 months (2 at the
edges) and scaled with the ratio of the mean jet frequency in the respective
representations of ERA5 and the mean jet frequency in the observations.
Simple scalings for the seasonal cycle
In the previous section we learned that 10 years of ERA5 data leads to a
smooth seasonal cycle, but shorter observation periods lead to an erratic
seasonal cycle because the months in the subset are not representative of the
long-term monthly means. We also saw that upon aggregation and smoothing,
both ERA5 and observations show similar seasonal cycles that differ mostly in
their amplitudes. In this section we seek to combine the information from
both data sources to reconstruct the “true” seasonal cycle of low-level jets
over the North Sea. We considered two different approaches.
The first method applies a correction to the observations, based on
information about their representativity. For each month and each platform,
we calculated the ratio between the low-level jet occurrence in the full and
subsets of the ERA5 data. Months for which this factor is much smaller (or
larger) than 1 are characterised by above- (below-) average low-level jet
occurrence. We then applied these ratios as correction factors to the
observed monthly means to adjust the outliers and obtain a more
representative seasonal cycle. However, this method did not lead to
satisfactory results because the correction factors were not robust: if only
1 year of data was available, and a month was very unrepresentative, the
correction factor would become very high/low and the adjustment would
overcompensate. Consequently, the reconstructed long-term seasonal cycles
still appeared erratic and were deemed unreliable (this result is therefore
not shown here, but is available in Supplement 4/6). For MMIJ the measurement period
spanned more than 4 years and consequently, the monthly low-level jet
occurrence already started converging on the climatological seasonal cycle.
For this platform, the correction factors were closer to 1 and we obtained a
reasonably smooth seasonal cycle. This emphasises that for this correction
method, at least several years of measurement data are required to obtain a
reliable estimate of the long-term low-level jet climatology.
Whereas the first method was aimed at correcting the observations (using ERA5
as a “vehicle” to assess their representativity), with the second method we
aim to correct the long-term ERA5 data based on prior evaluation of its
performance during the short-term period for which we have observations. This
can be readily understood from Fig. . We compare
panels (b) and (e), and seek a fixed scaling factor that minimises the difference
between each pair of monthly observed and simulated LLJ frequencies.
Denoting the monthly mean low-level jet frequency in ERA5 and
collocated observations with x and y, respectively, an
optimised scaling factor can be found by solving for a in
y=ax (using linear least squares regression). We do this
for each platform individually and also for their combined signal.
The results are illustrated in Fig. a. The lighter
colours represent the individual platforms, while the black line and scatter
points represent the combined monthly means. The overall fit, based on all
available data, has a slope of 0.44, but there are substantial differences between
the individual platforms, with slopes between 0.15 and 0.73 and relatively
large scatter. The difference between platforms could be random, due to the
limited availability of measurement data, or systematic, in which case
different sites need different scaling parameters. If the difference is
random, the global optimum indicated by the black line in
Fig. a could do justice to all individual platforms because it incorporates a much larger body of measurement data than any
single-site regression. Applying this factor of 0.44 to the full ERA5 data
provides us with a smooth seasonal cycle with reduced amplitude (similar to
the black dashed line in Fig. a, but now based on an optimised scaling factor). In other words, the seasonal cycle of
low-level jets based on ERA5 data up to 500 m overestimates the observed
cycle (based on measurement up to 300 m) by a factor of ∼2. However, as
shown in Fig. b, there seems to be a spatial
dependence in the scaling factors with larger slopes away from the coast,
implying that the different sites need different scaling parameters. In order
to cross-validate the single-platform regressions, we need to split the
measurement data in train and test datasets, but this poses a challenge. Like
before, the data record at MMIJ is long enough to obtain a reasonable
prediction of the test data, but some of the other data records are very
short and splitting them would, for example, leave only 3 months of training data, which obviously leads to very poor statistics, especially since there are
hardly any low-level jets in winter. Without cross-validation, more data are
available for regression, but this introduces the risk of overfitting and
therefore quantitative evaluation will be biased. Qualitatively, the
resulting seasonal cycles still appear erratic (Supplement 4/6).
Thus, despite similarities between the datasets, it is not straightforward to
either correct the observations using ERA5 representativity factors or to
correct the ERA5 data using a scaling factor derived from collocated
observations. In this section, we used the seasonal cycle to obtain
aggregated low-level jet characteristics (i.e. monthly means), but perhaps we
can identify other characteristics that lead to better results.
(a) Illustration of linear regression between monthly low-level jet
rates in the ERA5 data (subset, up to 500 m) and the observations. Black line
and scatter points represent aggregated data of all platforms, while the
other colours correspond to fits for individual platforms. Dashed black line
indicates a 1:1 correspondence. (b) Spatial distribution of the obtained fit
parameters for each individual platform. Like the colour coding, marker size
is scaled with the slope of the regression.
Other jet characteristics and their scaling potentialDiurnal cycle and stability
After analysing the seasonal cycle of low-level jets in-depth, we now briefly
consider some other variables that describe relevant characteristics of the
low-level jet climatology, starting with the diurnal cycle.
Figure a–c are again similar to
Fig. , now only including the ERA5 data up to
500 m. From the observations, it appears that the low-level jets occur
throughout the day, but with a small dip around 11:00 UTC. Panels (b) and (c), based
on short temporal subsets, are so erratic that it is difficult to distinguish
this diurnal cycle by eye. After aggregating all platforms and smoothing the
data (black dashed lines), we find that the observations and ERA5 agree on
the general shape, but again we needed to scale the ERA5 signals because they
differed in magnitude: the diurnal cycle in ERA5 is much more pronounced. At
this point, we think it is good to stress that several mechanisms can lead to
low-level jets in coastal areas (see Sects. and ), and the resulting diurnal signature should not be
confused with that of the typical onshore nocturnal jet that is often found
over land. As in the previous section, we performed linear regression to
identify optimal scaling parameters for the dashed black lines in panels (a and b). The difference with the previous section is that the regression is now
based on pairs of hourly instead of monthly observed and simulated
low-level jet frequencies. The scatter in this data is larger than for the
seasonal cycle, but the spatial distribution of the fitting parameters is
similar (not shown).
The second row in Fig. shows the relation between
low-level jet occurrence and atmospheric stability (expressed by the bulk
Richardson number based on the ERA5 surface data: 2 m temperature, skin
temperature, and 10 m wind). Scatter points represent mean aggregated
low-level jet frequencies over 50 stability bins. Both ERA5 and the
observations agree that low-level jets are typically associated with stable
stratification, although for some platforms in panels (d) and (e), there seems to
be a substantial number of jets for unstable conditions as well. In the
subsets (panel e) this distinctive behaviour is not as clear, and in the
observations it seems mostly absent. Without going into detail, we note that
low-level jets can be formed by different mechanisms, and it is possible that
ERA5 represents one mechanism better than another or perhaps one mechanism
is actually over-represented. Also note that in panels (e) and (f) there are
(positive) values of the Richardson number for which no low-level jets are
observed. In panel (d), this is not the case, which indicates that the
measurement periods are too short to adequately sample the full range of
stability conditions. Finally, we note that in panel (d), the low-level jet
rate seems to decrease again for very stable situations. This could be an
artefact of the bulk Richardson number or a physical limit: a stable
atmosphere leads to a low-level jet, but the low-level jet produces wind
shear, and consequently, the bulk Richardson number decreases. The fact that
this behaviour is not reflected in the observations suggests that the true
stability (that would have been observed) was actually smaller than what ERA5
predicted. Again, we tried to scale the amplitude of the stability signature
by performing linear regression between pairs of low-level jet frequencies in
ERA5 and observations (now based on stability bins instead of monthly or
hourly groupings). The slopes are larger than those based on the seasonal and
diurnal cycle (∼1.0), but qualitatively they seem to be less robust (not
shown).
Average low-level jet rate for each hour of the day (a–c) and as
function of the bulk Richardson number (d–f), for the full (10 years) of
ERA5 data up to 500 m (a, d), a subset of this data collocated with the
observations (b, e), and the observed data (c, f). Like in
Fig. , the black dashed lines represented a
scaled and smoothed aggregated signal based on all platforms.
Weather types and the spatial distribution of low-level jets
We also investigated the relation between low-level jet frequency and typical
circulation patterns. We used Lamb weather types (LWTs; ) to
perform this analysis. To derive these weather types we used the ERA5 mean
sea level pressure on a 5∘ latitude–longitude grid of 16 points as
laid out in the appendix of but centred over the area of
interest. The method distinguishes three main groups: those with a dominant
cyclonic (anticlockwise, low-pressure area) circulation, those with a
dominant anticyclonic (clockwise, high-pressure area) circulation, and those
with a “pure directional” flow. These three groups are further subdivided
based on the main direction of the flow over the North Sea (north, northeast,
east, etc.). If there is no dominant direction, the LWT is “pure
(anti)cyclonic”. Pressure fields characterised by the absence of a dominant
forcing are “undefined”. In total, this yields 27 different circulation
patterns. We computed average low-level jet rates for each group.
To illustrate the association between the circulation type and the low-level
jet occurrence, Fig. shows the average low-level jet rate per
weather type in the North Sea domain, based on 10 years of ERA5 data up to
500 m. The streamlines show the dominant flow pattern for each weather type:
the columns represent different wind directions over the North Sea, while the
full rows represent different rotation types. In the first full row, the
rotation is predominantly clockwise, in the bottom full row, the rotation is
mostly anticlockwise, and the middle full row is characterised by the absence
of rotation. Notice how the same wind direction can be associated with
different large-scale flows – and how this can impact the low-level jet
rate. Like before, we will not go into each individual feature in this figure
in detail, but we will focus on overall characteristics. In general, we see
that low-level jets are concentrated along the coastlines. This extends and
refines the global findings of and for
the North Sea domain. Low-level jets are much more dominant for certain Lamb
weather types. Most notably, the weather type “undefined” often gives rise to the formation
of jets. This makes sense, as low-level jets are subtle phenomena, and the
absence of a strong large-scale flow eases their development. Furthermore, we
observe that low-level jets occur frequently during large-scale flows with a
pronounced easterly component. Note that easterly flows bring in continental
air, while westerly flows originate from the Atlantic. Low-level jets are
uncommon for westerly flows. Closer inspection reveals that the differences
in spatial distribution of the low-level jets (e.g. comparing the Dutch and
Norwegian coastlines) seems to be related to whether the large-scale flow is
directed offshore. The British Isles are different in this respect, since for
westerly flows we do not observe an increased low-level jet rate off the
eastern coast of Great Britain.
Like with the previous characteristics, we performed linear regression
between ERA5 and observed low-level jet frequency, this time aggregated over
the various Lamb weather types. We found similar patterns in ERA5 and the
observations (not shown), but the spatial distribution of the scaling
parameters is different. Most slopes are around 0.4, but Lichteiland Goeree (LEG) stands out with
a slope of 0.65. This is not a huge difference, but it implies that our
earlier hypothesis – that the slope increases with distance to coast – does
not hold for all predictors. Indeed, one could argue that with Lamb weather
types as a predictor, the scaling parameters are spatially more robust. Thus,
while we believe that the spatial distribution in Fig. is
actually meaningful, the absolute low-level jet rate (as indicated by the
colour bar) is still off by a factor of ∼2.
Spatial distribution of the low-level jet rate in ERA5 data.
As explained in the text, the values shown here overestimate available observations
and should be interpreted with caution. A: anticyclonic; C: cyclonic; U: undefined; N, NE, E, etc., are eight wind
direction sectors; combinations of a direction and a rotation type are “hybrid” weather types, while weather types without
a dominant rotational component are “pure directional”. Streamlines illustrate the dominant large-scale flow pattern
(averaged over each LWT). The relative occurrence of each weather type is indicated as well. The figure can be enlarged for more detail or downloaded separately from the article web page.
Combining multiple predictors to extend observations
So far, we have tried to scale the low-level jet climatology with simple
linear factors applied to individual characteristics (e.g. seasonal cycle).
Perhaps, we can find a more sophisticated transformation function by
combining multiple predictors? In this section we use the MMIJ data to
illustrate how this could be applied in practice. In contrast to the previous
sections, which focused on aggregated low-level jet frequencies,
here we consider individual wind profiles. The procedure resembles
the Model Output Statistics (MOS) forecasts that are widely used for weather
forecasts (e.g. , chap. 6.5.2) and is
similar to the measure–correlate–predict methods mentioned in
Sect. . We use a machine learning package
to perform this task, and for readability, we will not highlight all the
technical details here. However, Jupyter notebooks are available as
a Supplement to facilitate reproducibility.
The general idea is illustrated in Fig. a: we have a short
time series with observations and a long reanalysis dataset. Based on the
overlapping part of the data, we determine the optimal parameters of a
statistical model (depicted by the red box). We then use this model to
predict the value of the observations, given the available long-term
reanalysis data. In the illustration, it seems as though one reanalysis
variable is used for this purpose, but in fact, we can use as many variables
as we want. In our case, the variable we want to predict is the probability
that a low-level jet will be observed, given various predictor variables from
the ERA5 data. Because this is a binary outcome (a jet either occurs or
not), our model of choice is a logistic regression model, which predicts the
probability of a positive outcome as function of one or several predictor
variables. The general form of this model is
p=11+e-(β0+β1x1+β2x2+…),
where βi represents the coefficients of the corresponding predictor variables
xi. In a short exploratory phase, we experimented with various
combinations of predictor variables. We found the best performance for a
small set of predictor variables consisting of time of the year, atmospheric
stability, and Lamb weather type. This makes sense, as together these
variables encompass information about wind speed, direction, and history of
the flow, as well as the probability of stable stratification and baroclinic
conditions. Indeed, each of these variables alone already provided valuable
information in the previous sections. For optimal performance, these
variables were preprocessed as follows: to truthfully represent its cyclic
nature, date was encoded by splitting the day of year into a sine and cosine
contribution. The Lamb weather type is a categorical variable, and to make it
suitable for regression it was encoded by converting it to the binary
representation of the numbers up to 27 (the total number of weather types)
and treating each digit as an individual binary variable. Stability was
represented by the difference between the 2 m temperature and
sea-surface temperature, which provided better results than the bulk
Richardson number. We also experimented with various training algorithms to
determine the coefficients βi of the logistic model (intermediate
results can be found in the Supplement). In the end, we settled on a
stochastic gradient descent algorithm.
First, we took only half of the MMIJ dataset (a bit more than 2 years) to
train the model (in other words: we fitted the parameters of our logistic
regression model to the first half of the data). The light blue line in
Fig. b shows the seasonal cycle of low-level jets in those first
2 years of observations. Note that this seasonal cycle is very erratic. This
can be expected for such a short period, but the question is whether the
additional information contained in the predictor variables enables us to
predict the other 2 years, despite the unrepresentative training data. Thus,
in the next step, we used our trained model to predict the other half of the
dataset. In fact, the model predicts the probability that a low-level jet
occurs. An individual jet is predicted only if the probability is higher than
50 %, but this happens only occasionally. Therefore, rather than
predicting individual jet events, we used the predicted probabilities
directly and computed the monthly mean predicted probability
(Fig. b, orange line). To evaluate the performance, we compared
the predicted seasonal cycle with that based on the true observations during
the second part of the dataset (Fig. b, light green line). The
true seasonal cycle was indeed smoother than in the first 2 years, but it
peaked a bit higher and earlier than predicted. To quantify this result, we
computed the root mean square error between the monthly means of the
predicted and test data and found it to be about 1 % point. This result
confirms that the model generalises well to new input data.
We then used the full MMIJ dataset to train the same model. With twice as
much training data as before, we were confident that the model would achieve
at least a similar performance and thus predict the seasonal cycle to within
1 % point RMSE (but probably better). The observed seasonal cycle
averaged over these 4 years of training data (Fig. b, red line)
was still clearly affected by the unrepresentative months in the first half
of the dataset. Apparently, 4 years of data is still not enough for the
climatology to converge. Therefore, in the final step, we used the trained
model to predict the 10-year seasonal cycle. The result (Fig. b,
purple line) is a smooth seasonal cycle which peaks in May at about 9 %.
This is our best estimate of the low-level jet seasonal cycle, based on the
coalescence of reliable measurements and extensive reanalysis data. Compared
to the results presented in Sect. , we can conclude
that we have adjusted the erratic nature of the short-term observations
(Fig. e), resulting in a seasonal cycle similar to
that shown in Fig. a, but with reduced amplitude.
Compared to this final result, the crude amplitude adjustment with which we
started in Sect. now appears far too strong.
The results presented in this section are intended as proof of principle, and
for the purpose of illustration we tried to keep things conceptually simple. With
respect to the selection of predictor variables, choice of model, and method
of cross-validation, we realise that the possibilities are endless. The
availability of sufficient measurement data is key to an exhaustive follow-up
study.
(a) Illustration of the MCP, MOS, or machine learning (ML) procedure in which a (logistic)
model is trained with observation data and then used to predict long-term characteristics. (b) Illustration
of the MMIJ seasonal cycle of low-level jets based on 2 years of observed data (train), 2 years
of predicted (pred) and observed data (test), 4 years of observed data (train), and 10 years of predicted data (pred).
Discussion
This paper has demonstrated our efforts to infer reliable low-level jet
characteristics by combining observations and reanalysis data. We have
deliberately chosen to illustrate how the results are impacted by limitations
of the data and choices in the analysis. In this section we summarise our
work, discuss the implications and offer an outlook for future research
directions.
We started with a general validation of the ERA5 data for the observed wind
speed at measurement locations in the North Sea. We found that the overall
root mean square error is between 1.25 and 1.5 m s-1. The bias shows a
clear discontinuity at 10:00 UTC, which is related to the data-assimilation
strategy that was used to produce ERA5. Users of the ERA5 data should
consider a suitable bias correction (e.g. ), but we
strongly suggest that future reanalysis products use sliding or at least
partly overlapping observation windows. We also demonstrated that the
observations alone can also not be relied upon because the limited temporal
extent of the measurement data leads to biased climatologies. Thus, in the
remainder of the paper we focused on finding a suitable way of combining the
two datasets: a procedure similar to measure–correlate–predict methods but
tailored to low-level jets instead.
Low-level jet detection is very sensitive to the vertical extent of the data,
and this has important implications for the interpretation of all results.
Typical jet characteristics like jet height and jet strength cannot be
reliably inferred from range-limited observations. With this restriction in
mind, we can say that many of the observed jets occurred at heights fully or
partly in the range spanned by contemporary wind turbine blades. Moreover,
the typical observed jet strength is about 8 m s-1, which is in the cubic
part of the power curves of these turbines. We therefore expect that the
low-level jet impact on loads and power can be substantial. ERA5 is not able
to reliably reproduce these characteristics. There are some indications that
the jets are “smeared out”: they appear higher and weaker than observed.
Given this vertical displacement, a fair comparison between ERA5 and the
observations is difficult. Considering the lower 300 m only, ERA5 drastically
underestimates the amount of jets, but including heights up to 500 m, ERA5
shows more low-level jets than observed. We decided to include the data up to
500 m because they give a stronger climatological signature.
Even though 1:1 correspondence between ERA5 and the observations is poor,
both datasets agree on the following climatological characteristics: most
jets occur in spring and summer; the diurnal cycle is weak and only around
noon are the chances for low-level jets slightly lower; low-level jets are
typically associated with stably stratified conditions; the absence of strong large-scale forcing or flow regimes with a pronounced easterly or
offshore component are favourable for their formation. From the ERA5 data, we
learned that low-level jets are concentrated along the coasts. We then compared
the frequency of low-level jets between ERA5 and the observations. In the
most general terms, we can state that the mean low-level jet rates based on
ERA5 up to 500 m typically overestimate the amount of low-level jets that
would have been observed with lidars up to 300 m by a factor of about 2. To
improve upon this result we illustrated how a logistic regression model
was able to predict the seasonal cycle of low-level jets at MMIJ to within
1 % point RMSE. This is a promising result, and we expect that our results
can still be improved upon. Longer measurement datasets would form a major
contribution to further advancement as well.
The characteristics identified in this paper provide some clues as to the
processes that govern these jets. The academic literature recognises two
dominant formation mechanisms, both of which are supported by our results.
The first is frictional decoupling . This
theory describes a perturbed system attempting to re-establish equilibrium.
As the accelerating wind field in the lower atmosphere is deflected by the
Coriolis effect, it moves around its new equilibrium in a circular fashion.
Over land, frictional decoupling has been linked to the decay of turbulent
mixing around sunset, and it has been suggested that a similar situation
applies in coastal areas upon the abrupt surface (temperature and roughness)
transition . This mechanism is supported by our results,
which show that low-level jets are frequent for winds directed offshore and
in stable conditions. The second mechanism relates low-level jets to
horizontal temperature gradients (baroclinity; see ).
According to this theory, the tilt of isobaric surfaces leads to a thermal
wind component that under certain conditions can manifest itself as a low-level jet.
This mechanism has been coupled to low-level jets over gently sloping
terrain but equally applies to coastal areas where large horizontal
temperature differences can occur due to differential heating between the
land and sea surface . The fact that most low-level jets
occur in spring and summer supports a baroclinic contribution and possibly
an interplay with the evolution of sea breezes, which show a similar seasonal
cycle (e.g. ). In the end, we expect that both processes
are likely to contribute to the low-level jet climatology. Finally, we note
that we also spotted a low-level jet with a clear frontal structure in the
ERA5 data. It is unlikely that such events contribute significantly to the
low-level jet climatology, but the characteristics of such jets may be very
different and potentially much more harmful for (offshore) wind turbines.
Other causes have been described in the literature, such as orographic blocking.
We do not expect this to play a major role along the Dutch coast, but for some
of the low-level jets that are present in ERA5 along the British and
especially the Norwegian coast it may play an important
role . A more detailed investigation of the ERA5 data
may allow us to separate these mechanisms. This is an interesting direction
for further research.
With respect to future work, it would also be interesting to look at other
datasets. In this paper, we have used ERA5 data to analyse the spatial
characteristics of low-level jets directly. However, ERA5 is currently being
used to develop higher-resolution, down-scaled reanalysis datasets (e.g. the
New European Wind Atlas and the Dutch Offshore Wind
Atlas), and it would be worthwhile to see if they improve upon ERA5. Another
interesting alternative is COSMO-REA6 , which is
down-scaled from ERA-interim, but with its resolution of 6 km it might
outperform ERA5. The current paper can serve as a guideline for the
investigation of other reanalysis datasets.
Finally, a note on dealing with low-level jets in practice. It would be
worthwhile to include a low-level jet case as standard inflow field for wake
and load simulations. Recent papers have developed affordable methods to
provide realistic inflow fields .
Expensive computational fluid dynamics (CFD) simulations have been used to derive parameterisations to
generate realistic inflow fields for wind farm simulations. The second cited
paper also includes low-level jet profiles in the early morning. These
profiles can be compared with the morphology and frequency distributions
detailed in the current paper to optimise yield and lifetime. Since the
presence of the coastline turns out to have an important effect on the
formation of low-level jets, it would be interesting to perform an additional
precursor large-eddy simulation (LES) for such a heterogeneous terrain. This could also
shed light on the mechanisms involved in jet formation.
Code and data availability
The ERA5 data were generated by ECMWF as part of the Copernicus
Climate Change Service and will in the future be available through the Climate
Data Store at https://cds.climate.copernicus.eu/cdsapp\#!/dataset/reanalysis-era5-pressure-levels?tab=overview (last access: 25 March 2019) . Observations were
distributed by ECN, part of TNO, by order of the Dutch Ministry of Economic Affairs.
They can be accessed at https://windopzee.net/en/home/ (last access: 18 March 2019) (ECN, 2019). A series of Jupyter
notebooks to facilitate reproducibility is available in the Supplement.
Lidar data
Vertically pointing lidar provides efficient and non-intrusive measurement of boundary-layer winds. Compared to traditional meteorological masts, lidars typically
expand the height and vertical sampling frequency of offshore wind
measurements. Lidar data from seven measurement sites were used in this study
to analyse North Sea LLJ spatiotemporal behaviour. Lidar types used included
the WINDCUBE v2 pulsed lidar (only at LEG) and the Zephir 300s
continuous-wave (CW) lidar (all other platforms). The lidars were typically
platform mounted, except within the Borssele wind farm and Hollandse Kust
wind zones (Noord and Zuid) where the lidar was instrumented atop a floating
met ocean buoy. At these locations, two lidar-equipped met ocean buoys were
positioned simultaneously.
CW and pulsed wind lidar are coherent systems, meaning they both analyse
Doppler shift frequencies to determine an estimate of the radial wind
speed . However, radial velocity and vertical wind profile
extraction techniques differ between the two lidar types. Whereas pulsed wind
lidars use range gates to near-simultaneously extract radial velocity
estimates at multiple points in space, CW wind lidar can only extract a
radial velocity estimate at the beam focus length. This beam focus length
must be modified in time in order to measure the wind field at varying
elevation levels. The radial wind speed is defined as the motion of the wind
towards or away from the remote-sensing system, and therefore unless the wind
is moving along one of these radials, then the wind speed will not be fully
resolved. Consequently, CW and pulsed wind lidar use varying adaptations of
conical scanning techniques to resolve the horizontal wind
field at varying elevation levels. For brevity, these differences are not
detailed here. However, because of these differences, the vertical wind
profile was resolved at 17 s intervals for the CW wind lidar and at 4 s
intervals for the pulsed wind lidar. These wind profiles are then analysed by
the lidar software and output as a 10 min average vertical wind profile. A
summary of the lidar measurement heights and data collection periods for all
sites is provided in Fig. .
Data quality control is imperative to ensure an accurate depiction of the
offshore LLJ. The implementation of data quality control varied depending upon
the lidar type (i.e. ZephIR 300s versus WINDCUBE v2), although considerations
were made to ensure that data quality control was employed relatively
uniformly between measurement sites. Wind lidar data from both the Borssele
wind farm and Hollandse Kust (Noord and Zuid) wind zones have additionally
had quality control measures implemented by Fugro Oceanor. An overview of
these quality control procedures can be found online
(https://offshorewind.rvo.nl/data-borssele, last access: 22 March 2019). The data quality control procedures
implemented are as follows. First, plausible value checks were implemented in
the wind data. Any 10 min observation that met the following criteria was
removed from the data record.
The mean wind speed was either greater than the period maximum wind speed or less than the period minimum wind speed.
The mean wind speed was less than 0.05 m s-1.
Turbulence intensity (TI) for the period fell below 0.10 % (i.e. 0.001).
At the measurement height, the value of TI was 10 standard deviations
(σTI) greater than the mean (μTI) TI value (i.e. TI ≥μTI+10σTI);
μTI and σTI
were defined as the height-respective value for the entire data collection period. Because TI
typically decreases with mean wind speed, this threshold was only imposed if the 10 min mean wind speed exceeded 4 m s-1.
Specific quality control measures were also applied to the lidar wind data.
Any 10 min observation that satisfied the following criteria was removed
from the data record.
A lidar error code (e.g. 9998 or 9999) was reported.
The carrier-to-noise ratio (CNR) was less than -22 (the value of CNR provides a measure of signal strength, i.e. quality). CNR was only
outputted by the WINDCUBE v2 wind lidar.
Backscatter magnitude was less than 10-5 or greater than 100 – backscatter served as a proxy for CNR for
data reported by the ZephIR 300s lidar.
Prior analyses (e.g. ) demonstrate that the ZephIR 300s
lidar can incorrectly measure wind direction by 180∘. Analyses of wind
data at MMIJ from 1 January 2012 through 1 January 2014 indicated that
approximately 3.6 % of the measured wind data exhibited this flow reversal.
Although mitigation (i.e. removal) of this data is possible, it requires
independent wind direction measurements from a collocated meteorological
mast. Because mast data were not available at each site, these wind direction
errors were not removed. However, ZephIR 300s lidar wind direction errors did
not appear to impact the measured wind speed, which is the main focus of this
paper. In order to account for the wake effect of neighbouring wind farms on
wind speed measurements, wind direction sectors were filtered and
corresponding data (wind speed and direction) were removed. A generous
estimate of 20 km was used to denote the maximum wind farm wake length.
The supplement related to this article is available online at: https://doi.org/10.5194/wes-4-193-2019-supplement.
Author contributions
Data analysis and preparation of the paper were performed by PCK,
under the supervision of GJS and AAMH. Processing of lidar data
was performed by JBDJ, who also wrote the text for the Appendix.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The analysis was performed on the high-performance computing facility offered
by the Dutch Science Organization NWO (grant number SH-312-15). This work is
part of the EUROS project (NWO/TTW research grant number STW-14158). We thank
two anonymous reviewers for their constructive feedback on an earlier version
of the paper.
Review statement
This paper was edited by Jakob Mann and reviewed by two
anonymous referees.
ReferencesBanakh, V. A., Smalikho, I. N., Köpp, F., and Werner, C.:
Representativeness of wind measurements with a cw Doppler lidar inthe
atmospheric boundary layer, Appl. Optics, 34, 2055–2067,
10.1364/AO.34.002055, 1995.Bhaganagar, K. and Debnath, M.: Implications of Stably Stratified Atmospheric
Boundary Layer Turbulence on the Near-Wake Structure of Wind Turbines,
Energies, 7, 5740–5763, 10.3390/en7095740, 2014.Blackadar, A. K.: Boundary Layer Wind Maxima and Their Significance for the
Growth of Nocturnal Inversions, B. Am. Meteorol.
Soc., 38, 283–290, 10.1175/1520-0477-38.5.283, 1957.Bollmeyer, C., Keller, J. D., Ohlwein, C., Wahl, S., Crewell, S., Friederichs,
P., Hense, A., Keune, J., Kneifel, S., Pscheidt, I., Redl, S., and Steinke,
S.: Towards a high-resolution regional reanalysis for the European CORDEX
domain, Q. J. Roy. Meteor. Soc., 141, 1–15,
10.1002/qj.2486, 2015.Carta, J. A., Velázquez, S., and Cabrera, P.: A review of
measure-correlate-predict (MCP) methods used to estimate long-term wind
characteristics at a target site, Renew. Sust. Energ. Rev.,
27, 362–400, 10.1016/j.rser.2013.07.004, 2013.Carter, G. M., Dallavalle, J. P., and Glahn, H. R.: Statistical Forecasts Based
on the National Meteorological Center's Numerical Weather Prediction System,
Weather Forecast., 4, 401–412,
10.1175/1520-0434(1989)004<0401:SFBOTN>2.0.CO;2, 1989.Christakos, K., Varlas, G., Reuder, J., Katsafados, P., and Papadopoulos, A.:
Analysis of a Low-level Coastal Jet off the Western Coast of Norway, Energy
Proced., 53, 162–172, 10.1016/j.egypro.2014.07.225, eERA DeepWind' 2014, 11th Deep Sea Offshore Wind R&D Conference, 2014.Copernicus Climate Change Service (C3S): ERA5: Fifth generation of ECMWF
atmospheric reanalyses of the global climate, Copernicus Climate Change
Service Climate Data Store (CDS), available at: https://cds.climate.copernicus.eu/cdsapp\#!/dataset/reanalysis-era5-pressure-levels?tab=overview (last access: 25 March 2019), 2017.Dörenkämper, M., Optis, M., Monahan, A., and Steinfeld, G.: On the
Offshore Advection of Boundary-Layer Structures and the Influence on Offshore
Wind Conditions, Bound.-Lay. Meteorol., 155, 459–482,
10.1007/s10546-015-0008-x, 2015.
ECMWF: IFS Documentation – Cy41r2, chap. II: Data Assimilation, ECMWF,
2016.ECN: Wind at Sea, available at: https://windopzee.net/en/home/, last
access: 18 March 2019.Englberger, A. and Dörnbrack, A.: A Numerically Efficient Parametrization
of Turbulent Wind-Turbine Flows for Different Thermal Stratifications,
Bound.-Lay. Meteorol., 169, 505–536, 10.1007/s10546-018-0377-z, 2018.Gebraad, P. M. O., Teeuwisse, F. W., Wingerden, J. W., Fleming, P. A., Ruben,
S. D., Marden, J. R., and Pao, L. Y.: Wind plant power optimization through
yaw control using a parametric model for wake effects – a CFD simulation
study, Wind Energy, 19, 95–114, 10.1002/we.1822,
2014.Glahn, H. R. and Lowry, D. A.: The Use of Model Output Statistics (MOS) in
Objective Weather Forecasting, J. Appl. Meteorol., 11,
1203–1211, 10.1175/1520-0450(1972)011<1203:TUOMOS>2.0.CO;2, 1972.Gutierrez, W., Ruiz-Columbie, A., Tutkun, M., and Castillo, L.: Impacts of
the low-level jet's negative wind shear on the wind turbine, Wind Energ.
Sci., 2, 533–545, 10.5194/wes-2-533-2017, 2017.Holton, J. R.: The diurnal boundary layer wind oscillation above sloping
terrain, Tellus, 19, 200–205, 10.3402/tellusa.v19i2.9766, 1967.Holtslag, A. A. M., Svensson, G., Baas, P., Basu, S., Beare, B., Beljaars, A.
C. M., Bosveld, F. C., Cuxart, J., Lindvall, J., Steeneveld, G. J.,
Tjernström, M., and Van De Wiel, B. J. H.: Stable Atmospheric Boundary
Layers and Diurnal Cycles: Challenges for Weather and Climate Models,
B. Am. Meteorol. Soc., 94, 1691–1706,
10.1175/BAMS-D-11-00187.1, 2013.Jones, P. D., Harpham, C., and Briffa, K. R.: Lamb weather types derived from
reanalysis products, Int. J. Climatol., 33, 1129–1139,
10.1002/joc.3498, 2013.Kalverla, P., Steeneveld, G.-J., Ronda, R., and Holtslag, A. A. M.: Evaluation of
three mainstream numerical weather prediction models with observations from
meteorological mast IJmuiden at the North Sea, Wind Energy, 22, 34–48,
10.1002/we.2267, 2019.Kalverla, P. C., Steeneveld, G.-J., Ronda, R. J., and Holtslag, A. A. M.: An
observational climatology of anomalous wind events at offshore meteomast
IJmuiden (North Sea), J. Wind Eng. Ind.
Aerod., 165, 86–99,
10.1016/j.jweia.2017.03.008, 2017.Lima, D. C. A., Soares, P. M. M., Semedo, A., and Cardoso, R. M.: A Global View
of Coastal Low-Level Wind Jets Using an Ensemble of Reanalyses, J.
Climate, 31, 1525–1546, 10.1175/JCLI-D-17-0395.1, 2018.Mahrt, L., Vickers, D., and Andreas, E. L.: Low-Level Wind Maxima and Structure
of the Stably Stratified Boundary Layer in the Coastal Zone, J.
Appl. Meteorol. Clim., 53, 363–376,
10.1175/JAMC-D-13-0170.1, 2014.Moore, G. W. K. and Renfrew, I. A.: Tip Jets and Barrier Winds: A QuikSCAT
Climatology of High Wind Speed Events around Greenland, J. Climate,
18, 3713–3725, 10.1175/JCLI3455.1, 2005.Nunalee, C. G. and Basu, S.: Mesoscale modeling of coastal low-level jets:
implications for offshore wind resource estimation, Wind Energy, 17,
1199–1216, 10.1002/we.1628, 2014.Olauson, J.: ERA5: The new champion of wind power modelling?, Renew. Energ.,
126, 322–331, 10.1016/j.renene.2018.03.056, 2018.Park, J., Basu, S., and Manuel, L.: Large-eddy simulation of stable boundary
layer turbulence and estimation of associated wind turbine loads, Wind
Energy, 17, 359–384, 10.1002/we.1580,
2014.
Peña, A. and Hasager, C. B.:
Remote sensing for wind energy, DTU Wind Energy, Technical University of Denmark, Risø, Denmark, 2015.Petersen, E. L., Troen, I., Jørgensen, H. E., and Mann, J.: Are local wind
power resources well estimated?, Environ. Res. Lett., 8, 011005,
http://stacks.iop.org/1748-9326/8/i=1/a=011005, 2013.
Poveda, J. M. and Wouters, D.: Wind measurements at meteorological mast
IJmuiden, ECN, Petten, the Netherlands, 2015.Ranjha, R., Svensson, G., Tjernström, M., and Semedo, A.: Global distribution
and seasonal variability of coastal low-level jets derived from ERA-Interim
reanalysis, Tellus A, 65, 20412,
10.3402/tellusa.v65i0.20412, 2013.Reen, B. P. and Stauffer, D. R.: Data Assimilation Strategies in the Planetary
Boundary Layer, Bound.-Lay. Meteorol., 137, 237–269,
10.1007/s10546-010-9528-6, 2010.Rife, D. L., Pinto, J. O., Monaghan, A. J., Davis, C. A., and Hannan, J. R.:
Global Distribution and Characteristics of Diurnally Varying Low-Level Jets,
J. Climate, 23, 5041–5064, 10.1175/2010JCLI3514.1, 2010.
Sedefian, L.: On the Vertical Extrapolation of Mean Wind Power Density, J.
Appl. Meteorol., 19, 488–493, 10.1175/1520-0450(1980)019<0488:OTVEOM>2.0.CO;2, 1980.Shapiro, A., Fedorovich, E., and Rahimi, S.: A Unified Theory for the Great
Plains Nocturnal Low-Level Jet, J. Atmos. Sci., 73,
3037–3057, 10.1175/JAS-D-15-0307.1, 2016.Smedman, A.-S., Tjernström, M., and Högström, U.: Analysis of the
turbulence structure of a marine low-level jet, Bound.-Lay. Meteorol.,
66, 105–126, 10.1007/BF00705462, 1993.Staffell, I. and Pfenninger, S.: Using bias-corrected reanalysis to simulate
current and future wind power output, Energy, 114, 1224–1239,
10.1016/j.energy.2016.08.068, 2016.Steele, C. J., Dorling, S. R., von Glasow, R., and Bacon, J.: Modelling
sea-breeze climatologies and interactions on coasts in the southern North
Sea: implications for offshore wind energy, Q. J. Roy.
Meteor. Soc., 141, 1821–1835, 10.1002/qj.2484, 2015.Tran, T., Tran, H., Mansfield, M., Lyman, S., and Crosman, E.: Four dimensional
data assimilation (FDDA) impacts on WRF performance in simulating inversion
layer structure and distributions of CMAQ-simulated winter ozone
concentrations in Uintah Basin, Atmos. Environ., 177, 75–92,
10.1016/j.atmosenv.2018.01.012, 2018.Van de Wiel, B. J. H., Moene, A. F., Steeneveld, G. J., Baas, P., Bosveld,
F. C., and Holtslag, A. A. M.: A Conceptual View on Inertial Oscillations and
Nocturnal Low-Level Jets, J. Atmos. Sci., 67,
2679–2689, 10.1175/2010JAS3289.1, 2010.Wharton, S. and Lundquist, J. K.: Atmospheric stability affects wind turbine
power collection, Environ. Res. Lett., 7, 014005,
http://stacks.iop.org/1748-9326/7/i=1/a=014005, 2012.
Wilks, D. S.: Statistical methods in the atmospheric sciences,
Academic press, USA, 627 pp., 2006.