An evaluation of wind indices for KVT Meso, MERRA and MERRA2

KVT/TPM/2016/RO96 An evaluation of wind indices for KVT Meso, MERRA and MERRA2 Comparison for 4 met stations in Norway Tuuli Miinalainen

Content 1 Summary... 3 2 Introduction... 4 3 Description of data and methods used... 6 3.1 WIND INDEX 6 3.2 MODEL DATA AND MET MAST DATA 6 3.2.1 KVT MESO...7 3.2.2 MERRA and MERRA2...7 3.2.3 Met mast data...7 4 Results... 9 4.1 RESULTS FOR LTM INDEX COMPARISONS 9 4.2 RELATIVE ERRORS 11 5 Discussion... 14 6 References... 15 Appendix A... 16 2

1 Summary This report presents the results of a comparison between wind indices based on KVT Meso, MERRA2 and MERRA model data. The wind indices are compared to real met mast data from four different met stations in Norway. The results from the analysis show that for these four stations the KVT Meso index was better at capturing the observed variability in the wind conditions than MERRA and MERRA2, and is therefore the preferred choice as a long term reference over the two other data sets. It is shown that the KVT Meso wind speed index has a higher correlation with met mast data than corresponding indices from MERRA or MERRA2 at these four stations. Furthermore, the error statistics (mean absolute error, root mean squared error) between the mast indices and model indices were on average lower with KVT Meso than with the MERRA datasets, Table S1. Table S1. The average statistics for the four met stations, model data compared to mast data. RMSE is the root mean square error, MAE is the mean absolute error. Average statistics for the 4 stations Correlation RMSE MAE KVT Meso 0.792 2.648 2.157 MERRA 0.627 3.755 3.158 MERRA2 0.659 3.749 3.074 The distribution of relative errors in the long term wind speed using the different indices has also been evaluated. Assuming a measurement period of one year at any of these four stations would result in a lower uncertainty if the wind analysis is based on the KVT Meso indices for long term corrections compared to MERRA or MERRA2. Using three years of on-site measurements as a reference period in long term correction (L3Y) yields significantly lower uncertainty compared to using a one year period (LTM). This was the case for all three data sets. The error distribution of MERRA and MERRA2 using three years of on-site data was found to be of the same level as the uncertainty of using KVT Meso for only one year of on-site data. Table S2: The standard deviations of relative error distributions with LTM indices (Last twelve month) and L3Y indices (Last three years). Standard deviation of relative errors with LTM indices, all stations KVT Meso 2.77 MERRA 3.95 MERRA2 3.95 Standard deviation of relative errors with L3Y indices, all stations KVT Meso 1.79 MERRA 2.93 MERRA2 2.68 3

2 Introduction When assessing wind speed for a long term period, using model data instead of reference station data has several advantages (e.g. ability to solve geographical variations, represent wind better for hub height). However, in recent years there has been extensive discussion among the industry about which model performs better when estimating the wind speed for a long term period. To investigate this Kjeller Vindteknikk has in the present study compared wind indices based on the KVT Meso dataset to corresponding indices based on MERRA and MERRA2 data. Kjeller Vindteknikk publishes wind index maps for Norway, Sweden and Finland in their home page for each month. An example of KVT s wind index map is presented in Figure 1. Figure 1: KVT's wind index map for November 2016 An assessment of different long term correction methods and different data sources was carried out by Liléo et al.(2013) [2], where the WRF ERA model data was compared to MERRA data for some locations in Sweden. The model data was also compared to observational data at selected met stations. In addition, an evaluation was made of the number of years of data needed to create appropriate long term corrections and of their related uncertainty levels. The analysis included wind data from tall wind measurement masts from relatively short time periods. The met stations chosen for the validation were located in low complexity forested areas in Sweden. 4

The scope of the present report is to compare the KVT Meso dataset to MERRA and MERRA2 data, and validating the results with met mast data from Norwegian met stations. First, wind indices were calculated for all model data sets. Second, these model indices were validated by a comparison to indices based on observational data from four Norwegian coastal met stations. The methods and data used for this report are presented in Chapter 3. Results are presented in Chapter 4, followed by a discussion in Chapter 5. 5

3 Description of data and methods used In this chapter the concept of wind index and also descriptions of model data and met mast data are presented. 3.1 Wind index A wind index is a measure of how the mean wind speed during a specific period has been relative to what is considered to be normal. In the present analysis the focus is on comparing last twelve months indices. This is the equivalent to measuring wind speed in a mast for a 12 month period and comparing the measured average wind speed to the expected long term conditions at the site. Last twelve months (LTM) index (%) is the mean wind speed for the last twelve months divided by the mean wind speed of the whole reference period. The reference period used in this analysis is a 16-year-period from 2000 to 2015 and the LTM-indices are computed for each month for the years 2001-2015. For example, the LTM index for January 2015 is computed by LTM index 01/2015 = mean FF 02/2014,FF 03/2014,FF 04/2014,,FF 01/2015 100% (1) mean FF 01/2000,FF 02/2000,,FF 12/2015 To illustrate the concept of LTM, an example of LTM periods for different months in the winter 2015 are presented in Figure 2. Figure 2: Last twelve months (LTM) period for Dec 2014, Jan 2015 and Feb 2015. By comparing indices instead of the actual wind speed values, one can assess how the models are able to capture long term variability in wind speed. Furthermore, comparing indices reduces the effect of different height levels in models and mast data. The seasonality of the data will often contribute to high correlation coefficients when comparing e.g. monthly average wind speeds from two sources. By using the LTM method also the seasonality of the data is removed. 3.2 Model data and met mast data In this section the three model sources and the met stations that are used for validation are presented. 6

3.2.1 KVT MESO KVT Meso is a downscaled model dataset based on the ERA-Interim reanalysis and the WRF model [1], and is developed by Kjeller Vindteknikk. The spatial resolution of KVT Meso data used for this comparison is 4 km, and the time resolution is 1 hour. More details about the model setup and background can be found in the Appendix A. 3.2.2 MERRA and MERRA2 MERRA (Modern Era Retrospective-Analysis for Research and Applications) is a NASA reanalysis based on satellite data and ground station data. It has been generated with the version 5.2.0 of the GEOS atmospheric model. The spatial resolution for MERRA data is 0.5 which corresponds to approximately 35-50 km. The time resolution of the data is 1 hour. [3] MERRA2, released in 2016, is an evolved version of MERRA data. The model behind MERRA2 is an improved version of GEOS with more developed parameters. Furthermore, it incorporates more recent, updated data sources and observations. The spatial and temporal resolution is nearly the same as for MERRA data (0.5, 1hour). [4] 3.2.3 Met mast data The met stations for the validation were chosen from the coastline of Norway. Their locations are presented in Figure 1 and descriptions of mast data are presented in Table 1. Figure 3: The met stations for comparison 7

Table 1: Descriptions of the met stations chosen for validation. Location, data coverage and time resolution are given for each station. The data coverage refers to the coverage during the period 2000-2015. Percentage of valid wind Met station name Lon Lat Time resolution Note speed data (%) Vigra 6.115 62.5617 96.23 % 1h Ørland 9.6105 63.7045 98.81 % 1h and 6h Bodø 14.3588 67.2672 98.29 % 6h Slettnes 28.217 71.0888 98.61 % 6h Missing data filled with synth data from Ona Data for 2015 with 6h res, data for 2000-2014 with 1h res Two missing months filled with estimated values from Helligvær The selected stations are from the Norwegian coastal zone (airports, lighthouses). All have little local influence and also minimum homogeneity problems. The stations are geographically spread along the coast. Wind data for all stations marked in Figure 3 have been obtained from Norwegian Meteorological Institute s web portal eklima [5]. Table 1 shows the location, data availability, time resolution and other relevant information for each station. For all stations we found good data coverage throughout the analysis period. However, for the Vigra station there were some periods missing in the wind data, and therefore calculating monthly averages could not be done for some months. This issue was handled by filling the missing data with synthesized data for wind speed. The data to fill the gaps for this station was created by using met mast data from years 2000-2015 from the nearby met station Ona as a reference and then producing a synthesized data set with respect to the Vigra wind time series. The synthesization technique is described in Lileo et al. (2013) [2]. Furthermore, there were two months of data missing for the Bodø station (April and May 2005). The average wind speeds for these two months were estimated with the help of data from a nearby met station Helligvær. The estimation was done by calculating the ratios of monthly wind speed values between Bodø and Helligvær for April and May individually. After that, the monthly wind speeds for Helligvær were corrected with the calculated ratio and then resulting monthly values were taken to fill the two month gap in Bodø wind data series. For the Slettnes and Ørland stations there was sufficient data coverage for computing each monthly average. 8

4 Results At first, the LTM indices were computed for all months in the years 2001 to 2015 for all four stations. Next, the indices were analyzed and the error statistics between models and mast data were computed. Finally, the differences between using one year indices and three year indices were evaluated. All the results are presented in this chapter. 4.1 Results for LTM index comparisons The scatter plots for LTM indices between the station data and model data for all stations are presented in Figure 4, Figure 5, Figure 6 and Figure 7. The plots show how the modeled wind indices compare to the observed wind indices for each month during the 2001-2015 period. The plots also give the mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (r), and the best fit linear regression line of the data. The average statistics are summarized in Table 2. Figure 4: Scatter plots for last twelve month indices for Bodø station 9

Figure 5: Scatter plots for last twelve months indices for Slettnes met mast Figure 6: Scatter plots for last twelve months indices for Vigra met mast 10

Figure 7: The scatter plots for LTM wind indices for Ørland met station For all the four stations we find that the KVT Meso is more aligned with the observed data compared to MERRA and MERRA2.The mean absolute error and root mean square error is also lower for KVT Meso than for MERRA and MERRA2, for all the stations. The indices from KVT Meso give, for all stations, a higher correlation coefficient to the observations compared to MERRA and MERRA2. In general we note that for all modeled data the variability in the data series is smaller than in the observations. This can be seen as the tilt of the regression line (y=ax+b) as a<1 for all stations and all models. For three of the stations we note an improvement in the statistics for MERRA2 compared to MERRA, but for Bodø MERRA performs slightly better than MERRA2. The average values of error statistics of Figures 4, 5, 6 and 7 are presented in Table 2, where it is indicated that KVT Meso captures the variability in the wind conditions better than MERRA or MERRA2 at the 4 stations. Table 2: The average statistics of the four met stations, model data compared to mast data. RMSE is the root mean square error, MAE is the mean absolute error. Average statistics of the 4 stations Correlation RMSE MAE KVT Meso 0.792 2.648 2.157 MERRA 0.627 3.755 3.158 MERRA2 0.659 3.749 3.074 4.2 Relative errors The relative errors (%), RE, for LTM indices for all four stations together are presented in Figure 8. This data distribution can be given as: 11

RE month,year = LTM Observed Model month,year LTMmonth,year Observed. (2) LTM month,year A coarse estimate of the long term wind conditions, WS longtern, at a site based on one year of measurements can be calculated by: WS Longterm = WS 1 year /WI 1 year (3) Where WS 1 year is the average wind speed measured over a 1 year period, while WI 1 year is the wind speed index for the same 1 year period. The WI 1 year is in principal the same as the LTM covering the same 1 year time period. If the modeled LTM index is higher than the observed index this would lead to an underestimation of the long term wind speed, while an overestimation of the long term wind speed can be expected if the modeled LTM is lower than the observed. The distribution of the relative errors given in Figure 8 will therefore represent the frequency and magnitude of the errors in the estimation of the long term wind speed assuming a random 12 month period of measurements at one of these 4 stations. The standard deviations of LTM indices relative errors are presented in Table 3. It is clear that for these four stations KVT Meso has narrower error distribution than MERRA of MERRA2. In other words, KVT Meso indices are more accurate as a long term reference data set than the MERRA or MERRA2 indices for these stations. Figure 8: The histograms for relative errors (%) with LTM indices for all four stations Table 3: The standard deviations of relative errors with LTM indices, all stations together Standard deviation of relative errors with LTM indices, all stations KVT Meso 2.77 MERRA 3.95 MERRA2 3.95 12

A certain degree of uncertainty must however be expected for the long term correction of a measurement station with only one year of data. This uncertainty will be reduced if the on-site measurements are carried out for a longer time period. As an example a three year period of on-site measurements are assumed. The uncertainty in the long term corrections using three year data instead on one year is illustrated by the calculation of last three year indices (L3Y) instead of LTM indices. The relative errors in last three year indices for all four stations together are presented in Figure 9, and standard deviations for the errors are presented in Table 4. Figure 9: The histograms for relative errors (%) with last three years indices for all four stations Table 4: The standard deviations of relative errors with last three years indices (L3Y), all stations together Standard deviation of relative errors with L3Y indices, all stations KVT Meso 1.79 MERRA 2.93 MERRA2 2.68 Compared to the histograms in Figure 8, the error distributions in Figure 9 for all models are more narrow and tapered. The standard deviations presented in Table 4 indicate the same conclusion; that the error with long term correction decreases with a longer period of on- site measurements. As with LTM indices, the error distribution for KVT Meso is clearly narrower than for MERRA or MERRA2. One should also note that by using MERRA or MERRA2 as a source for long term corrections at these sites you need 3 years of on-site data to get the same error distribution as you get from KVT Meso using only 1 year of on-site data. 13

5 Discussion The comparison performed in the present study shows a higher accuracy for the KVT Meso compared to MERRA and MERRA2 at the four selected Norwegian met stations. This accuracy improves when taking three years as a reference period instead of one year. It is needed to acknowledge that in this analysis the data from met masts are assumed to correspond to actual wind speed, and the uncertainties in the observations are ignored. These uncertainties might have a slight effect on results, especially as the measurements are from 10 m height. In order to minimize the effect of the measurement quality, the met stations selected for validation were chosen from well exposed areas with minimum local influence. Moreover, the comparison is only performed for four stations, all from coastal areas with complex terrain close by. This should be taken into account in the discussion of the results presented earlier since the coastal effects and complex terrain are not well captured by the coarse MERRA grids. Contrarily, the coastal effects are captured more accurately by higher resolution models, and this is one of the benefits for KVT Meso. Therefore, further analysis should be carried out with other stations with less complex terrain and with less coastal influences. It is challenging to find such measurement data that has been under good quality control and covers a sufficiently long time period. One of the benefits using model data sets for long term correction is that the models represent the geographical variability in the wind conditions. Met-stations, in contrast, are often challenging to use for long term correction purposes at a potential wind energy site since the site is typically located at some distance from the nearest met station and is typically influenced by other local terrain features than the measurement. Often reference masts are located in lowland regions, while the wind energy sites are located at more exposed locations. In this work we have used a rather coarse index method as a long term correction method. This method is typically not used as a long term correction method by the wind industry since it does not consider differences in for example wind direction. Therefore the errors given in Figure 8 and Figure 9 differ from the errors one would obtain if using an established long term correction method (e.g. MCP method). On the other hand, comparing wind indices is appropriate when studying differences between models for long term trends in wind speed. 14

6 References [1] WRF, [Online]. Available: http://www.wrf-model.org. [2] S. Liléo, E. Berge, O. Undheim, R. Klinkert and R. E. Bredesen, "Long-term correction of wind measurements. State-of-the-art, guidelines and future work.," Elforsk report 13:18, 2013. [3] The Global Modeling and Assimilation Office: MERRA: Modern-era retrospective analysis for research and applications - web page, Retrieved from: https://gmao.gsfc.nasa.gov/reanalysis/merra/ [4] Molod, A., Takacs, L., Suarez, M., and Bacmeister, J.: Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2, Geosci. Model Dev., 8, 1339-1356, doi:10.5194/gmd-8-1339-2015, 2015. Retrieved from http://www.geosci-model-dev.net/8/1339/2015/gmd-8-1339-2015.html [5] Norwegian Meteorological Institute: eklima web portal. Retrieved from www.eklima.no 15

Appendix A The Weather Research and Forecast (WRF) model is a state-of-the-art meso-scale numerical weather prediction system, aimed at both operational forecasting and atmospheric research needs. A description of the modelling system can be found at the home page http://www.wrfmodel.org/. The model version used in this work is v3.2.1 described in Skamarock et al. 2008 1. Details about the modelling structure, numerical routines and physical packages available can be found in for example Klemp et al. (2000) 2 and Michalakes et al. (2001) 3. The development of the WRF-model is supported by a strong scientific and administrative community in U.S.A. The number of users is large and it is growing rapidly. In addition the code is accessible for the public. The most important input data are geographical data and meteorological data. The geographical data is from National Oceanic and Atmospheric Administration (NOAA). The data includes topography, surface data, albedo and vegetation. These parameters have high influence for the wind speed in the layers close to the ground. The ERA-Interim reanalysis data with approximately 0.7 degree resolution, available from the European Centre for Medium-Range Weather Forecasts (ECMWF) with 6 hours interval, is used as boundary data for the model. ERA-Interim is a reanalysis dataset resultant from the assimilation of all available observation data globally into a numerical weather prediction model in order to create a description of the state of the atmosphere on a uniform horizontal grid and at uniformly spaced time instants (00, 06, 12 and 18 UTC). The assimilation model incorporates data from several thousand ground based observation stations, vertical profiles from radiosondes, aircrafts, and satellites. See Berrisford et al. (2009) 4 and Dee et al. (2011) 5 for further description of the data. Surface roughness and landuse have been updated from Landmäteriets GSD database in Sweden and from the N50 series from Kartverket in Norway. The model setup used for this analysis is shown in Figure A-1. The model has been set up with 4 km x 4 km horizontal resolution. The model is run with 32 layers in the vertical with four layers in the lower 200 m. We have used the Thompson microphysics scheme and the MYJ scheme for boundary layer mixing. The simulation outputs hourly data starting from 01.01.1979 and updated to current date on a monthly basis. Figure A-1: Model set up for the WRF reference simulations of Scandinavia. 1 Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda MG, Huang X-Y, Wang W. and Powers JG, 2008: A Description of the Advanced Research WRF Version 3, NCAR Technical Note NCAR/TN-475+STR, Boulder, June 2008 2 Klemp JB., Skamarock WC. and Dudhia J., 2000: Conservative split-explicit time integration methods for the compressible non-hydrostatic equations (http://www.wrf-model.org/) 3 Michalakes J., Chen S., Dudhia J., Hart L., Klemp J., Middlecoff J., and Skamarock W., 2001: Development of a Next Generation Regional Weather Research and Forecast Model. Developments in Teracomputing: Proceedings of the Ninth ECMWF Workshop on the Use of High Performance Computing in Meteorology. Eds. Walter Zwieflhofer and Norbert Kreitz. World Scientific, Singapore. 4 Berrisford P., Dee D., Fielding K., Fuentes M., Kållberg P., Kobayashi S. and Uppala S., 2009: The ERA-Interim archive. Version 1.0., ERA report series. 5 Dee, D. P. and other authors, 2011:The ERA-Interim reanalysis: configuration and performance of the data assimilation system", Qart. J. R. Meteorol. Soc., 2011. 16