LA CONSTITUCIÓN DE LA REPÚBLICA DEL ECUADOR.

In order to quantify the degree of agreement between the different indices, the Pearson correlation coefficient was calculated using selected combinations of the concurrent annual index values. The results of the analysis are shown in Table (4.2) below.

1960 1970 1980 1990 2000 2010 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 1960 1970 1980 1990 2000 2010 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 Wi n d i n d e x Years W&K

Linear Fit of W&K 95% CI Wi n d i n d e x Years CLC Linear Fit of CLC 95% CI

Figure 4.6: A comparison between a wind index calculated using the BADC-7 stations and the same

index based on CLC classification

1980 1985 1990 1995 2000 2005 2010 2015 0.8 0.9 1.0 1.1 1.2 1980 1985 1990 1995 2000 2005 2010 2015 0.8 0.9 1.0 1.1 1.2 Wi n d i n d e x Years W&K

Linear Fit of W&K 95% CI Wi n d i n d e x Years CLC Linear Fit of CLC 95% CI

Figure 4.7: A comparison between a wind index calculated using the BADC-57 stations and the same

Index 1 Index 2 Concurrent years Correlation coefficient

BADC-7 BADC-57 29 0.887

BADC-7 ERA-40 (UK) 44 0.629

BADC-7 UKCIP 38 0.781

BADC-7 (”W&K” in Figure 4.6) CLC 57 0.942

BADC-57 ERA-40 (57) 19 0.755

BADC-57 ERA-40 (UK) 44 0.876

BADC-57 GH 13 0.919

BADC-57 (”W&K” in Figure 4.7) CLC 29 0.856

Table 4.2: Pearson correlation coefficient calculated using concurrent annual values for different indices

There is a high degree of correlation between the indices calculated using the surface data, i.e. the BADC-7, BADC-57, CLC, and GH indices. The highest correlation coefficients are found between BADC-7 and CLC. This supports the decision to use a value of roughness length equal to 0.03 m. Figure (3.8) in section 3.3.2.1 shows that the difference between wind indices calculated for different values of roughness length is negligible. Though, the range of the tested roughness lengths varied between 0.01 m and 0.08 m. Here, it is shown that when the corresponding values are taken into account for roughness length (Table 3.3.2.2) of the stations used in the study, the difference in the results remains undetectable. The most striking finding though is the high correlation between BADC-57 and the index provided by GH. This agreement is of great importance for both industry and academic community. It provides evidence that it is feasible to calculate a cost effective wind index which would give an indication about the long-term mean wind speed for the UK from surface stations. The correlation between the BADC-7 and ERA-40 index is somewhat lower. The BADC-7 index is calculated using a relatively small number of point observations whereas the ERA-40 index is more spatially homogeneous. In addition, the ERA-40 index is based on six-hourly rather than hourly data, though this should still capture the main features of diurnal variation and is unlikely to introduce bias. This is consistent with the previous observation in that 57 stations will provide a higher degree of spatial smoothing. The correlation between the BADC-7 stations and the UK Met Office gridded dataset lies somewhere in between. Both are generated with surface observations, though the latter would be expected to exhibit a greater degree of spatial smoothing given the much larger number of stations used to generate the 5km x 5km grid.

In order to analyse the degree of spatial smoothing of the BADC-7 stations, the Pearson correlation was calculated between each combination of the seven sites using the annual mean wind speeds at each site. The results of this are shown in Table (4.3).

Lerwick Stornoway Airport Boscombe Down Valley Aberporth Aldergrove Tiree Lerwick 1 0.002 -0.277 -0.089 0.186 -0.071 0.168 Stornoway Airport 1 -0.317 0.119 -0.130 0.270 0.241 Boscombe Down 1 0.680 0.409 0.351 0.376 Valley 1 0.441 0.529 0.508 Aberporth 1 0.373 0.547 Aldergrove 1 0.731 Tiree 1

Table 4.3: Pearson correlation coefficient calculated using annual mean wind speed values for

combinations of the BADC-7 stations

In addition, the correlation is plotted as a function of distance in Figure (4.8)

0 100 200 300 400 500 600 700 800 900 1000 1100 -1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00 C or r e l at i on C oe f f oc i e n t Distance (km)

Figure 4.8: Pearson correlation coefficient calculated using the annual mean wind speeds at the

BADC-7 sites as a function of the distance between the different site combinations

Figure (4.8) confirms an obvious trend with separation distance. What is striking is the high average correlation between the annual mean wind speeds at sites such as Aldergrove and Tiree (0.731) which are 208km apart. This would be extremely useful during the so called Phase 1 among wind analysts/developers. During Phase 1, a wind analyst has no information about the wind regime (i.e. no actual recorded data) at the prospective site. The usual procedure followed therefore, is to identify similar sites, preferably in the vicinity of the site under investigation, in order to make the initial estimation. However, this may not be feasible and during this phase several assumptions have to be taken which obviously may increase the uncertainty in evaluating a project. The usual reason behind this procedure is that the time required for an application for a mast to be granted or the time until a landowner consents to a developer to install a mast can be crucial from a planning perspective for a future project. One condition is the wind speed

distribution to be very alike between the reference sites and the target one. Indeed, as Figure (3.3) illustrates, the predominant wind in Aldergrove is at 210◦ whereas in Tiree, while it is fairly exposed from all the directional sectors, the predominant direction is 240◦. Moreover, the topography between the reference sites and the target station must be the same. This is due to the fact that different terrain will have different local effects which may affect wind speed. Again, when checking the satellite images for the aforementioned sites, no major issue related to the topography of both sites is raised. However, as it is mentioned in Chapter 6, it is highly recommended a study to take place that will assess the impact of the distinct topography of several sites on their between correlation in the long-term wind speeds.