PO.ID based on ERA-interim and ECMWF model data
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High quality North Sea wind climatology based on ERA-interim and ECMWF model data Henk van den Brink, Ine Wijnant and Andrew Stepek KNMI The Netherlands PO.ID X135 1. North Sea Wind Climatology based on ERA-interim and ECMWF model data and a review of existing wind atlases (2014). I.L. Wijnant, H.W. van den Brink and A. Stepek KNMI ([email protected])l The objective of this work is to provide wind climatology which is based on a period that is long enough to ensure that the whole variability of the wind climate is included. Most operational models get updated regularly, but ERA-interim reanalyses are calculated with one “frozen” model version. This guarantees a homogeneous data set (no artificial changes). The 34 year (1979-2012) long ERA-interim data set is however calculated on a rather coarse grid of 80 km and we need ECMWF on a 16 km grid to make the horizontal resolution high enough to get useful information for coastal areas. Most existing wind atlases are based on measurements at 10 m (observation network) or sea surface (satellite). In order to extrapolate these measurements to heights around hub height, assumptions have to be made on the atmospheric stability and the associated vertical profile of the wind. Similar assumptions have to be made in the Measure Correlate Predict (MCP) method for siting wind turbines. There are only a few wind masts where the vertical profile of the temperature, which determines atmospheric stability, is measured and the measurement campaigns are generally short. Model data on the other hand include temperature and therefore stability information in 4D. Therefore the model provides a complete and uniform space coverage of wind profiles that adapt from hour to hour to incorporate the effect of varying stability (e.g. cold air replacing warm above a cold sea). Figure 1 shows that none of the existing wind atlases are based on periods that are long enough. For the North Sea the lowest 11-year average wind speed at about 1 km height is 10% lower than the highest. This means that even 11 year measurement campaigns can provide an average wind speed that differs significantly from the long-term average. Therefore it is important that the period on which the wind climatology is based is long enough to capture the whole natural variability. We do that by using 34 years of ERA-interim reanalyses as a basis for our calculations. Figure 1: None of the existing wind atlases commonly used in the Netherlands include the long-term variability of the wind climate (Bakker, 2012) The disadvantage of the ERA-interim reanalysis is its coarse resolution of 80 km. We therefore use the analyses of the operational ECMWF weather forecasting model on a grid spacing of 16 km to “downscale” the ERA-interim reanalyses. Figure 2 illustrates that this gives a significant improvement especially in coastal areas partly because ECMWF has a far better representation of the land-sea mask than ERA-interim. Figure 2: Difference between size grid boxes has a large effect in coastal areas. Figure 3 illustrates that (1) over open sea the ERA-Interim climatology is similar to the ECMWF climatology, (2) the climatological wind speed in 2010 is considerably lower than in 2011 and 2012 and (3) ECMWF distinguishes Lake Ijssel (5.25- 5.55ºE) from the surrounding land and ERA-Interim does not. Figure 3: Cross section of the Weibull scale parameters at 52.5ºN of ECMWF for 2010 (black), 2011 (red) and 2012 (green) and of ERA-Interim for 2010 (blue). From left to right the peaks represent: the Atlantic, the Irish Sea, the North Sea and Lake Ijssel. We provide 34 year long time series of wind speed as well as climatological information on a 16 by 16 km grid and at 40, 60, 80, 100, 120, 140, 160, 180 and 200 m height. Figure 4: Long term (34 years; 1979-2012) average wind speed at 100 m height on a 16 km grid based on ERA-interim and ECMWF model data. Figure 5: Weibull scale (left) and shape (right) parameter at 100 m height on a 16 km grid based on ERA-interim and ECMWF model data covering 34 years (1979-2012). The technique of combining sufficiently long, but coarse model data with short series of high resolution data seems to work. The next step will be to do the same with Harmonie, KNMI’s very high resolution weather forecasting model (2.5 km grid). Obviously, the main recommendation for future work is to compare our model climatology to high quality measurements and we invite wind energy developers to share their non-public measurements with KNMI for this. Also we would recommend building a tall mast in the middle of the North Sea equipped to measure not only wind at different heights up to 200 m, but also temperature and heat flux. With these measurements it is possible to determine the atmospheric stability. Remote sensing wind instruments such as LIDAR should also be included. If this mast is maintained for a long enough period (at least 50 years), it would provide very valuable information on long-term variability and the climatology of off-shore atmospheric stability. It could also serve as a reference data set for the MCP method and it could be used to improve short range wind forecasts. At the moment neither climate models nor measurements indicate that the wind climate of the North Sea is changing (wind speeds are expected to stay within the natural variability range). However, we lack high quality measurements at hub height to establish wind speed trends with a high degree of certainty. Satellites provide measurements with a good horizontal coverage offshore but the temporal resolution is relatively poor (4 times a day at best). This means sampling biases have to be corrected for by collocation procedures. The same goes for local biases caused by quality control procedures. Extrapolation to hub height has been improved but remains an issue. Better measurements and models are needed to resolve these issues. The wind climatology presented here was essentially made in three steps. First we perform two Weibull fits of the 2011-2012 wind speeds: one for ERA-interim and one for ECMWF 16 km (available since 2010). The differences between the two fits are then used to transform the whole ERA-Interim data set. This is done for all grid boxes (16 by 16 km) in the horizontal grid and all levels relevant for offshore wind energy (40, 60, 80, 100, 120, 140, 160, 180 en 200 m). From the adapted ERA-interim data set, wind speed time series of the whole 34 year period are extracted and climatological statistics derived, e.g. the wind speed frequency distribution via the Weibull parameters, wind roses and annual and decadal wind statistics (including the probability of extremes). Abstract Method Results Objectives Conclusions References EWEA 2014, Barcelona, Spain: Europe’s Premier Wind Energy Event Figure 6: Minimum (left) and maximum (right) 11 year running mean of the annual average wind speed at 100 m height, based on a transformation of the ERA-interim (1979-2012) wind speed distribution to that of the ECMWF operational weather forecasting model on a 16 km grid. Figure 7: 10%-percentile of the 11 year running mean of the annual average wind speed at 100 m height, based on a transformation of the ERA-interim (1979-2012) wind speed distribution to that of the ECMWF operational weather forecasting model on a 16 km grid. Figure 8: Once in 50 year return value of the wind speed at 100 m height and an example of a wind rose (right) based on a transformation of the ERA-interim (1979-2012) wind speed distribution to that of the ECMWF operational weather forecasting model on a 16 km grid. Era-interim gives model values every 6 hours.
Transcript of PO.ID based on ERA-interim and ECMWF model data
High quality North Sea wind climatology based on ERA-interim and ECMWF model data
Henk van den Brink, Ine Wijnant and Andrew Stepek KNMI The Netherlands
PO.ID X135
1. North Sea Wind Climatology based on ERA-interim and ECMWF model data and a review of existing wind atlases (2014). I.L. Wijnant, H.W. van den Brink and A. Stepek KNMI ([email protected])l
The objective of this work is to provide wind climatology which is based on a period that is long enough to ensure that the whole variability of the wind climate is included. Most operational models get updated regularly, but ERA-interim reanalyses are calculated with one “frozen” model version. This guarantees a homogeneous data set (no artificial changes). The 34 year (1979-2012) long ERA-interim data set is however calculated on a rather coarse grid of 80 km and we need ECMWF on a 16 km grid to make the horizontal resolution high enough to get useful information for coastal areas.
Most existing wind atlases are based on measurements at 10 m (observation network) or sea surface (satellite). In order to extrapolate these measurements to heights around hub height, assumptions have to be made on the atmospheric stability and the associated vertical profile of the wind. Similar assumptions have to be made in the Measure Correlate Predict (MCP) method for siting wind turbines. There are only a few wind masts where the vertical profile of the temperature, which determines atmospheric stability, is measured and the measurement campaigns are generally short. Model data on the other hand include temperature and therefore stability information in 4D. Therefore the model provides a complete and uniform space coverage of wind profiles that adapt from hour to hour to incorporate the effect of varying stability (e.g. cold air replacing warm above a cold sea).
Figure 1 shows that none of the existing wind atlases are based on periods that are long enough. For the North Sea the lowest 11-year average wind speed at about 1 km height is 10% lower than the highest. This means that even 11 year measurement campaigns can provide an average wind speed that differs significantly from the long-term average. Therefore it is important that the period on which the wind climatology is based is long enough to capture the whole natural variability. We do that by using 34 years of ERA-interim reanalyses as a basis for our calculations.
Figure 1: None of the existing wind atlases commonly used in the Netherlands include the long-term variability of the wind climate (Bakker, 2012)
The disadvantage of the ERA-interim reanalysis is its coarse resolution of 80 km. We therefore use the analyses of the operational ECMWF weather forecasting model on a grid spacing of 16 km to “downscale” the ERA-interim reanalyses. Figure 2 illustrates that this gives a significant improvement especially in coastal areas partly because ECMWF has a far better representation of the land-sea mask than ERA-interim.
Figure 2: Difference between size grid boxes has a large effect in coastal areas.
Figure 3 illustrates that (1) over open sea the ERA-Interim climatology is similar to the ECMWF climatology, (2) the climatological wind speed in 2010 is considerably lower than in 2011 and 2012 and (3) ECMWF distinguishes Lake Ijssel (5.25-5.55ºE) from the surrounding land and ERA-Interim does not.
Figure 3: Cross section of the Weibull scale parameters at 52.5ºN of ECMWF for 2010 (black), 2011 (red) and 2012 (green) and of ERA-Interim for 2010 (blue). From left to right the peaks
represent: the Atlantic, the Irish Sea, the North Sea and Lake Ijssel.
We provide 34 year long time series of wind speed as well as climatological information on a 16 by 16 km grid and at 40, 60, 80, 100, 120, 140, 160, 180 and 200 m height.
Figure 4: Long term (34 years; 1979-2012) average wind speed at 100 m height on a 16 km grid based on ERA-interim and ECMWF model data.
Figure 5: Weibull scale (left) and shape (right) parameter at 100 m height on a 16 km grid based on ERA-interim and ECMWF model data covering 34 years (1979-2012).
The technique of combining sufficiently long, but coarse model data with short series of high resolution data seems to work. The next step will be to do the same with Harmonie, KNMI’s very high resolution weather forecasting model (2.5 km grid).
Obviously, the main recommendation for future work is to compare our model climatology to high quality measurements and we invite wind energy developers to share their non-public measurements with KNMI for this. Also we would recommend building a tall mast in the middle of the North Sea equipped to measure not only wind at different heights up to 200 m, but also temperature and heat flux. With these measurements it is possible to determine the atmospheric stability. Remote sensing wind instruments such as LIDAR should also be included. If this mast is maintained for a long enough period (at least 50 years), it would provide very valuable information on long-term variability and the climatology of off-shore atmospheric stability. It could also serve as a reference data set for the MCP method and it could be used to improve short range wind forecasts. At the moment neither climate models nor measurements indicate that the wind climate of the North Sea is changing (wind speeds are expected to stay within the natural variability range). However, we lack high quality measurements at hub height to establish wind speed trends with a high degree of certainty.
Satellites provide measurements with a good horizontal coverage offshore but the temporal resolution is relatively poor (4 times a day at best). This means sampling biases have to be corrected for by collocation procedures. The same goes for local biases caused by quality control procedures. Extrapolation to hub height has been improved but remains an issue. Better measurements and models are needed to resolve these issues.
The wind climatology presented here was essentially made in three steps. First we perform two Weibull fits of the 2011-2012 wind speeds: one for ERA-interim and one for ECMWF 16 km (available since 2010). The differences between the two fits are then used to transform the whole ERA-Interim data set. This is done for all grid boxes (16 by 16 km) in the horizontal grid and all levels relevant for offshore wind energy (40, 60, 80, 100, 120, 140, 160, 180 en 200 m). From the adapted ERA-interim data set, wind speed time series of the whole 34 year period are extracted and climatological statistics derived, e.g. the wind speed frequency distribution via the Weibull parameters, wind roses and annual and decadal wind statistics (including the probability of extremes).
Abstract
Method
Results
Objectives
Conclusions
References
EWEA 2014, Barcelona, Spain: Europe’s Premier Wind Energy Event
Figure 6: Minimum (left) and maximum (right) 11 year running mean of the annual average wind speed at 100 m height, based on a transformation of the ERA-interim (1979-2012) wind speed distribution to that of the ECMWF operational weather forecasting model on a 16 km grid.
Figure 7: 10%-percentile of the 11 year running mean of the annual average wind speed at 100 m height, based on a transformation of the ERA-interim (1979-2012) wind speed distribution to that of the ECMWF operational weather forecasting model on a 16 km grid.
Figure 8: Once in 50 year return value of the wind speed at 100 m height and an example of a wind rose (right) based on a transformation of the ERA-interim (1979-2012) wind speed distribution to that of the ECMWF operational weather forecasting model on a 16 km grid. Era-interim gives model values every 6 hours.
