T. Spangehl et al.: Intercomparing the quality of recent reanalyses for offshore wind farm planning
1415
6°35’15.5” E (https:/www.fino1.de/en/location.html, last ac-
cess: 6 November 2023).
olution products CERRA and HoKliSim-De is seen for 1992
to 1995. Likewise, the offsets between the different products
are not necessarily stable over time. For example, COSMO-
REA6 shows a positive offset for most years, except for 1995,
1996 and 2017, 2018 where COSMO-REA6 shows lower
wind speed values than other products.
Frequency distribution of wind speed and wind
direction
3.1.1 Interannual to multi-annual variability
The wind speed at heights near 100 m shows considerable in-
:erannual variability. During the period from 2004 to 2017
the observed annual mean wind speed at FINO1 ranges
from a maximum of 10.29 ms”! in 2007 to a minimum of
8.56 ms”! in 2016 (Fig. 1a). The mast corrected data is not
available for January 2018 to August 2019. Therefore, ob-
servations 2018 onwards are not shown. The commission-
ing of the wind farms Alpha Ventus in the direct vicinity
east of FINO1 in 2010, Borkum Riffgrund I (2015) to the
southeast and Trianel Windpark Borkum (2015) to the north-
west led to a reduction of the wind speed at FINO1 (Podein
et al., 2022). For this reason, the evaluation of the reanal-
yses and other model-based products using FINO1 in-situ
measurements as reference focuses on the undisturbed pe-
riod from 2004 to 2009. For this period the data availabil-
ity of the mast corrected wind speed measurements is 95 %
or higher for every individual year. Here the observed an-
nual mean wind speed ranges from 9.65 ms”! in 2004 to
10.29ms7! in 2007 (Fig. 1b). All model-based products
capture the interannual variability during the undisturbed
period. Contrary to the observations all model-based prod-
ucts including the global and regional reanalyses, the re-
gional downscaling simulation with COSMO-CLM and the
wind atlas product show maximum wind speed in 2008
and only second highest wind speed in 2007. Biases of in-
dividual years range from —0.47 ms”! (2007) for NEWA
co 0.44 ms”! (2008) for COSMO-REA2 (Table 2). ERA5
shows a negative bias of —0.24ms”! for 2004-2009. The
Dias Is consistently negative over the individual years. A neg-
ative bias of —0.10 ms”! is found for HoKliSim-De for the
same time period. Again, the bias is consistently negative but
smaller than for ERAS5 for every individual year (Table 2).
On the other hand, COSMO-REA6 shows a positive bias of
0.13 ms! which is predominantly positive for the individual
years. The smallest bias of only 0.01 m s7! for 2004-2009 is
found for CERRA. Likewise, the bias is smallest for most
of the individual years. For the years 2004 to 2009 there is
no evidence for any systematic change of the bias over time
for any of the products (Table 2). With regard to the analysis
of the undisturbed time period it is concluded that the ob-
servations are within the uncertainty range of the reanalyses
(COSMO-REAG6, CERRA, ERA5). Moreover, the regional
downscaling simulation HoKliSim-De shows a smaller bias
compared to the driving ERAS5 dataset.
Besides interannual variability the time series indicate
multi-annual variations of the wind speed. Higher than av-
erage wind speed values are seen for years 1998 to 2000
which are consistently represented by regional reanaly-
ses (COSMO-REA6, CERRA), HoKliSim-De and ERA5S5
(Fig. la). A larger offset between ERA5 and the higher res-
The frequency distribution of wind speed for 2004-2009
from FINO1 measurements at 102 m shows maximum val-
ues betwen 8 and 11ms”!, as indicated by the grey his-
togram in Fig. 2. The regional reanalyses COSMO-REA6
and CERRA, the global reanalysis ERA5 and the regional
downscaling simulation HoKliSim-De (model-based prod-
ucts shown in blue) resemble the observed frequency dis-
tribution of wind speed (observations shown in grey) for
JINOL For better readability of the following discussion the
statistical parameters of the pdf’s shown in Fig. 2 are sum-
marised in Table 3. The distribution obtained from COSMO-
REA6 shows a slight shift towards higher values compared
to the observations, which is manifested by a marginally
higher mean value of 10.05 ms”! compared to the observed
valued of 9.93 ms”7!, likewise a marginally higher median
(9.75/9.6 ms”), 99th percentile value (22.63/22.02 ms!)
and scale parameter (11.35/11.21 ms7!). COSMO-REA6
and the observations show a nearly perfect match for
the shape parameter (2.25/2.26), which confirms the good
agreement seen for the other parameters (Fig. 2a). CERRA
shows a very good agreement with the observed mean value
(9.95/9.93 ms 7!) and a marginally larger median value
(9.7/9.6 ms !). The underestimation of the 99th percentile
value seen for CERRA (21.75/22.02 ms!) is slightly more
pronounced than for COSMO-REAG6. The shape parameter
(2.3/2.26) and the scale parameter (11.23/11.21 ms7') of
CERRA are in good agreement with observations for the
time period considered here (Fig. 2b). The frequency dis-
tribution of ERAS is slightly shifted towards lower values
compared to observations. This shift is reflected by a slightly
lower mean (9.69/9.93 ms !), median (9.43/9.6 ms”).
99th percentile value (21.09/22.02 m s7!), and scale parame-
ter (10.94/11.21 m s7!) (Fig. 2c). The frequency distribution
of HoKliSim-De is characterised by a slight underestimation
of the mean (9.83/9.93 m s 7), median (9.51/9.6 ms 7), and
scale parameter (11.10/11.21 ms”7!). The underestimation
of these parameters is less pronounced than for ERA5S5. The
99th percentile value (22.14/22.02 m s”!) is in good agree-
ment with observations indicating that the higher resolu-
tion improves the representation of extreme wind speeds
(Fig. 2d). All model-based products show a slight underes-
timation of the 1st percentile value compared to the observa-
tions.
Wind roses consisting of 16 pre-defined sectors (N, NNE.
NE. ENE. E, ESE. SE, SSE, S, SSW. SW, WSW. W. WNW.
attos://doi.org/10.5194/asr-20-109-2 u?
Adv. Sei. Res., 20, 109-128. 2023
