95770 WIND RESOURCE MAPPING FOR PAKISTAN INTERIM OUTPUT - MESOSCALE MODELLING REPORT Client: International Bank for Reconstruction and Development (member of the World Bank Group) Contact Person: Address: 3E Reference: 107549 3E Contact Person: Rory Donnelly Date: 08/01/2014 Version: Final draft for review Classification: Confidential This document is based on an agreement entered into solely between Client and 3E, and no third- party beneficiaries are created hereby. 3E will not be liable to any third parties for services rendered to Client, or for the consequences of the use by a third party of this document. info@3E.eu 3E nv/sa T +32 2 217 58 68 Fortis Bank 230-0028290-83 RPR Brussels www.3E.eu Kalkkaai 6 Quai à la Chaux F +32 2 219 79 89 IBAN: BE14 2300 0282 9083 VAT BE 0465 755 594 B-1000 Brussels SWIFT/BIC: GEBABEBB 1 INTRODUCTION This interim document outlines the preliminary mesoscale modelling results obtained in Phase 1 of the World Bank ESMAP sponsored Renewable Energy Resource Mapping and Geospatial Planning – Pakistan project realised for the Alternative Energy Development Board of Pakistan (AEDB). The report by the Danish Technological University (DTU) is contained as an annex to this report. 2 OBJECTIVES The objective of this report is to describe the modelling strategy and method, show the output of the preliminary model results, and describe how these compare with a selection of existing measurement datasets. 3 METHODOLOGY The methodology employed is the use of the Weather Research and Forecasting (WRF) model, driven by Climate Forecast System Reanalysis (CFSR) data at a resolution in the inner domain of 5 km. This inner domain covers the entirety of Pakistan. The WRF model was run in a series of overlapping 11 day long simulations, run in parallel. The first 24 hours of each has been discarded to allow for model spin-up. Grid nudging was conducted on the outer domain above the boundary layer. Output from the model was post-processed by the generalisation procedure developed at DTU. This procedure removes effects of surface roughness and topography as seen by the model so that these effects can be re-implemented by a higher resolution model (WAsP) at micro-scale model phase. The resulting output is a series of .lib files (WAsP – DTU) that describe the climatology at each point which can be used in further micro-scale modelling to define winds over a prospective wind farm site. More than 36000 generalised wind climate files have been created. This data has been presented in the form of a series of images showing mean and generalised maps of wind speed and wind power density. Preliminary model results have been compared against four measurement sites in the Sindh region. The results show good comparison against all sites. While the timing of the minimum and maximum values in the annual cycle is well modelled, the amplitude of these estimates are overestimated. The diurnal cycles are somewhat less well modelled, with model results showing a lesser reduction in wind speeds at night than are seen in the measurements. The measurement data to be collected in Phase 2 will be crucial in configuring the model for optimal performance and for obtaining a sound understanding of the uncertainty associated with model results across the country. Each of these points is detailed in the DTU meso-scale report attached in Annex A. info@3E.eu 3E nv/sa T +32 2 217 58 68 Fortis Bank 230-0028290-83 RPR Brussels www.3E.eu Kalkkaai 6 quai à la chaux F +32 2 219 79 89 IBAN: BE14 2300 0282 9083 VAT BE 0465 755 594 B-1000 Brussels SWIFT/BIC: GEBABEBB ANNEX A INTERIM MESOSCALE WIND MODELLING AND PRELIMINARY VALIDATION REPORT FOR PAKISTAN QUALITY INFORMATION Author: Rory Donnelly Verified by: Signature: Approved by: Signature: Template V. 14.15 Wind Resource Mapping for Pakistan FINAL DRAFT FOR 3/3 Interim output - Mesoscale modelling report REVIEW 107549 – 08/01/2014 CONFIDENTIAL Interim mesoscale wind modelling and preliminary validation report for Pakistan DRAFT Jake Badger1 , Patrick J. H. Volker1 , Andrea N. Hahmann1 , Jens Carsten Hansen1 , Luis Ferreira2 , Rory Donnelly 2 1 Department of Wind Energy, Technical University of Denmark (DTU), Risø Campus, Denmark 2 3E Brussels, Belgium December 23, 2014 2 METHOD Abstract This document reports on the methods used in phase 1 of the ESMAP wind map- ping project for Pakistan. The interim mesoscale modelling results were calculated by output from simulations of the Weather, Research and Forecasting (WRF) model. We document the method used to run the mesoscale simulations and to generalize the WRF model wind climatologies. Four meteorological mast measurements in the coastal Sindh province have been used to give a preliminary validation in a limited area. 1 Introduction The conventional method used to produce estimates of wind resource over large areas or regions, such as on a national scale, is to analyze wind measurements made at a number of sites around the region, as in for example the European Wind Atlas (Troen and Petersen, 1989). In order for this method to work well there needs to be a sufficient quantity of high quality data, covering the country. This criterion is sometimes difficult to satisfy and therefore other methods are required that typically give good indications of the geographical distribution of the wind resource, and as such will be very useful for decision making and planning of feasibility studies. Numerical wind atlas methodologies have been devised to solve the issue of insufficient wind measurements. The latest methodology developed at at DTU Wind Energy uses the Weather Research and Forecasting (WRF) model in a dynamical downscaling mode to produce mesoscale analysis. It is this method that is employed in this study and described in this report. The method has recently been documented in Hahmann et al. (2014) and verified against tall masts in the North and Baltic Sea. This report is structured as follows: Sections 2 and 3 describe the general method and the specific modelling setup of the WRF modelling systems used in the generation of the Pakistan phase 1 output. In Section 4 the results are presented. In Section 5 a preliminary validation of the interim modelling results against observations is presented. Section 6 sets out a number of points of discussion and recommendation for how the modelling will be advanced in the next phase of the project, as well sa recommendations for the project in general. Finally, Section 7 presents some conclusions. 2 Method Numerical wind atlas methodologies have been devised to solve the issue of insufficient wind measurements. Two methodologies have been developed and used at DTU Wind Energy. The first methodology is the KAMM/WAsP method developed at Risø National Laboratory. It has been used extensively for a number of national projects. The origins of the method are described in Frank and Landberg (1997) and further details of the downscaling method developed are found in Badger et al. (2014). That KAMM/WAsP methodology has since been upgraded to use a newer and more sophisticated mesoscale model, namely the Weather Research and Forecasting (WRF) model. The wind atlas method used in this study was calculated by carrying out a large number of 10 days mesoscale model simulation using the WRF model to cover a multiyear period. The output from the WRF simulations is analysed in a number of ways. For example, investigation of the dynamic variation of wind speeds as a function of time of day and month of year. Specific 1 3 MODELLING meteorological phenomena in the model output relevant to wind energy can be investigated, and an understanding of the important meteorological phenomena is sought. To use the simulation data for wind resource assessment the data must be post processed. The post processing is called generalization. The generalization method has been used extensively in a number of wind resource assessment studies, particularly within the KAMM/WAsP method. The WRF wind atlas method with generalization and validation was first carried out within the Wind Atlas for South Africa project (WASA, 2014). For more details on the generalization method see Appendix A. The post-processing allows a proper verification to be carried out, in which wind climate estimates derived from mesoscale modelling and measurements can be compared, by using the software WAsP. Without the post-processing step no verification is possible, because the surface description within the model does not agree with reality, and therefore model winds will not agree with measured winds, except perhaps in extremely simple terrain or over water far from coasts. 3 Modelling The Weather, Research and Forecasting (WRF) Model (Skamarock et al., 2008) is a mesoscale numerical weather prediction system designed to serve both operational forecasting and at- mospheric research needs. The simulations used to generate the interim wind modelling results utilize the Advanced Research WRF (ARW-WRF) version 3.5.1 model released on 23 September 2013. The WRF modelling system is in the public domain and is freely available for community use. It is designed to be a flexible, state-of-the-art atmospheric simulation sys- tem that is portable and efficient on available parallel computing platforms. The WRF model is used worldwide for a variety of applications, from real-time weather forecasting, regional climate modelling, to simulating small-scale thunderstorms. Although designed primarily for weather forecasting applications, ease of use and quality has brought the WRF model to be the model of choice for downscaling in wind energy applications. This model was used in wind-related studies concerning: wind shear in the North Sea (Pe˜ na and Hahmann, 2012) and over Denmark (Draxl et al., 2014), organized convection in the North Sea (Vincent et al., 2012), low-level jets in the central USA (Storm en et al., 2009), wind climate over complex terrain (Horvath et al., 2012), gravity waves (Lars´ et al., 2012), extreme winds (Lars´en et al., 2013), among others. 3.1 Model setup The simulations for the interim wind modelling were integrated on a grid with horizontal spacing of 45 km × 45 km (outer domain, D1, with 96 × 96 grid points), 15 km × 15 km (first nested domain, D2, with 187 × 187 grid points) and 5 km × 5 km (second nest, D3, with 325 × 397 grid points). Maps of the model domains are displayed in Fig. 1. The surface roughness length for innermost domain, D3, is given in Fig. 2. In the vertical the model was configured with 41 levels with model top at 50 hPa. The lowest 10 of these levels are within 1000 m of the surface and the first level is located at approximately 14 m AGL. Table 1 lists the details of the model configuration, including the model parametrizations used in the simulations. The actual namelist used in the simulations 2 3.1 Model setup 3 MODELLING Figure 1 – WRF model domains configuration and terrain elevation (m). Top left: 45 km × 45 km domain (D1), top right: 15 km x 15 km (D2) and bottom: 5 km × 5 km (D3). The inner lines show the position of D2 and D3 in D1 and D2, respectively. The colour scale indicates the terrain height. 3 3.1 Model setup 3 MODELLING Figure 2 – WRF model domain D3 surface roughness length. The horizontal grid spacing is 5 km × 5 km. The colour bar to the bottom left indicates the values of surface roughness length. 4 3.1 Model setup 3 MODELLING is presented in Appendix C. 5 3.1 Model setup 3 MODELLING Table 1 – Summary of model and system setup and physical parameterizations used for the WRF simulations. Model setup: WRF (ARW) Version 3.5.1. Mother domain (D1; 96 × 96 grid points) with 45 km grid spacing; 2 nested domains: D2 (187 × 187 grid points) using 15 km and D3 (325× 397 grid points) with 5 km horizontal grid spacing on a Lambert conformal projection (see Fig. 1). 41 vertical levels with model top at 50 hPa; 10 of these levels are placed within 1000 m of the surface; The first 6 levels are located approximately at: 14, 43, 72, 100, 129 and 190 m. MODIS (2001–2010) land-cover classification of the International Geosphere-Biosphere Pro- gramme. Enhanced lake options used. Simulation setup: Initial, boundary conditions, and fields for grid nudging come from the The Climate Forecast System Reanalysis [1979 - 2010] (CFSR) at 0.5◦ × 0.5◦ resolution. Runs are started (cold start) at 00:00 UTC every 10 days and are integrated for 11 days, the first 24 hours of each simulation are disregarded. Sea surface temperature (SST) from Optimum Interpolation Sea Surface Temperature (OISST) at 0.25◦ × 0.25◦ resolution (Reynolds et al., 2010) and are updated daily. Model output: hourly (lowest 11 vertical levels) for D3, 3-hourly for D1 and D2, wind speeds at 5 vertical levels every 10 minutes for D3 only. Time step in most simulations: approx. 180 seconds. One-way nested domains; 5 grid point nudging zone. Grid nudging on D1 only and above level 10; nudging coefficient 0.0003 s−1 for wind, tem- perature and specific humidity. No nudging in the PBL. Physical parameterizations: Precipitation: WRF Single-Moment 5-class scheme (option 4), Kain-Fritsch cumulus param- eterization (option 1) turned off on D3. Radiation: RRTM scheme for longwave (option 1); Dudhia scheme for shortwave (option 1) PBL and land surface: Mellor-Yamada-Janjic scheme (Mellor and Yamada, 1982) (option 2), Eta similarity (option 2) surface-layer scheme, and Noah Land Surface Model (option 2). Surface roughnesses are kept constant at their winter value. Diffusion: Simple diffusion (option 1); 2D deformation (option 4); 6th order positive definite numerical diffusion (option 2); rates of 0.06, 0.08, and 0.1 for D1, D2, and D3, respectively; vertical damping. Positive definite advection of moisture and scalars. 6 3.2 Data processing 3 MODELLING Most choices in the model setup are fairly standard and used by other modelling groups. The only special setting for wind energy applications is the use of a constant surface roughness length, thus disabling the annual cycle available in the WRF model. This choice is consistent with the generalization procedure discussed in section 2 and Appendix A. Figure 3 – WRF model simulation schematic showing how the simulation period is covered by a succession of overlapping 11 day simulations. The first day of the simulations, which overlaps with the last day of the previous simulation, is for model spin-up and is not used in subsequent analysis. The final simulation covered the 10-year period January 2001 – December 2010, and was run in a series of 11-day long overlapping simulations, with the output from the first day of each simulation being discarded, see Fig. 3. This method is based on the assumptions described in Hahmann et al. (2010) and Hahmann et al. (2014). The simulation used grid nudging that continuously relaxes the model solution towards the gridded reanalysis but this was done only on the outer domain and above the boundary layer (level 10 from the surface) to allow for the mesoscale processes near the surface to develop freely. Because the simulations were re-initialized every 10 days, the runs are independent of each other and can be integrated in parallel reducing the total time needed to complete a multi-year climatology. The grid nudging and 10-days reinitialization keeps the model solution from drifting from the observed large- scale atmospheric patterns, while the relatively long simulations guarantee that the mesoscale flow is fully in equilibrium with the mesoscale characteristic of the terrain. 3.2 Data processing Wind speeds and directions are derived from the WRF model output, which represents 10- minutes or hourly instantaneous values. For evaluating the model wind speed climatology, the zonal and meridional wind components on their original staggered Arakawa-C grid were interpolated to the coordinates of the mass grid. The interpolated wind components were then used to compute the wind speed and rotated to the true north to derive the wind direction. For a given height, e.g., 100 m, wind speeds are interpolated between neighboring model levels using logarithmic interpolation in height. It was found that this interpolation procedure preserves more of the original features in the model wind profile compared to other schemes 7 4 RESULTS (e.g., linear or polynomial interpolation of the wind components). For the model grid points inside Pakistan in domain D3 time-series for the entire period for the wind speed, wind direction at 5 heights, and 1/L were generated. The generation of the time-series is a rather time consuming process because the WRF output files are stored for every three hours for the whole domain. The generation of time-series requires that for every grid-point in the considered region all files for the whole period have to be accessed. 4 Results In this section the results in the form of the annual mean wind climate are presented based on the 10 years of simulation, covering the years 2001 to 2010 inclusive. First the simulated winds are presented. These represent the annual mean wind speed and power density at 100 m a.g.l. directly from the modelling, see Figs. 5 and 6. Therefore the winds in these maps reflect the orography and surface roughness length as they are represented in the model rather than the real orography and roughness length. Figure 7 shows the wind speed at 100 m a.g.l. on unrotated longitude and latitude axes in order to indicate the rotated axes of the modelling domains. Static Data Height 10min Data pro- WRF Raw 60min (WindsT) Time-series Generalization .lib reduction Interpolation cessing Raw 10min (Winds) Figure 4 – Schematic representation of the data processing used to create the wind climate files that compose the WRF-based NWA. Next the generalized winds are presented. These represent the annual mean wind speed and power density at 100 m a.g.l. for standardized condition of flat terrain with surface roughness length of 3 cm everywhere, see Figs. 8 and 9. Now the winds in these maps reflect the variation of the winds due to all influences other than the microscale orography and surface roughness change. The generalization process allows for microscale orography and surface roughness change effects to be added for any particular site, using the WAsP software. This is done via the generalized wind climate file, which are created for every WRF model grid point inside Pakistan. An example of generalized wind climate file data is given in Fig. 10. Figure 11 shows the location pertaining to the more than 36000 generalized wind climate files. 8 4 RESULTS Figure 5 – Mean annual simulated wind speed at 100 m above ground level from WRF simulation at 5 km × 5 km grid spacing for the period 2001 to 2010 inclusive. The colour scale indicates the wind speed in m s−1 . 9 4 RESULTS Figure 6 – Mean annual simulated wind power density at 100 m above ground level from WRF simulation at 5 km × 5 km grid spacing for the period 2001 to 2010 inclusive. The colour scale indicates the wind power density in W m−2 . 10 4 RESULTS Figure 7 – Mean annual simulated wind speed at 100 m above ground level from WRF simulation at 5 km × 5 km grid spacing for the period 2001 to 2010 inclusive. The colour scale indicates the wind speed in m s−1 . Unrotated longitude and latitude axes are used in this map in order to indicated the rotation of the modelling domains. 11 4 RESULTS Figure 8 – Mean annual generalized wind speed at 100 m above ground level from WRF simulation at 5 km × 5 km grid spacing for the period 2001 to 2010 inclusive. The standard conditions are flat terrain with uniform surface roughness length (3 cm). The colour scale indicates the wind speed in m s−1 . 12 4 RESULTS Figure 9 – Mean annual generalized wind power density at 100 m above ground level from WRF simulation at 5 km × 5 km grid spacing for the period 2001 to 2010 inclusive. The standard conditions are flat terrain with uniform surface roughness length (3 cm). The colour scale indicates the wind power density in W m−2 . 13 4 RESULTS Figure 10 – Example of the data contained within a generalized wind climate file data. This data can be used in the WAsP software to make predictions of the wind resources at a specific site of interest accounting for the microscale effects due to orography and surface roughness changes. 14 4 RESULTS Figure 11 – Top: The location of the generalized wind climate data for the whole of Pakistan shown in Google Earth. Bottom: A detail of generalized wind climate data coverage including how a user of the data can find out about the data filename using Google Earth. 15 5 PRELIMINARY VALIDATION 5 Preliminary validation Figure 12 – The location of the sites used for the preliminary validation using the codes given in Table 2. Three masts have been chosen for the validation of the Pakistan mesoscale model. The criteria for the data selection is based on availability of the data, duration of the measurement campaign, heights, quality of the data, amongst others. The names and locations of the stations are given in Table 2. Some details of the mea- surements are also given. The locations are shown in the map in Fig. 12. It can be seen that at this stage the preliminary validation only includes stations in the south of the country, and therefore does not constitute a validation of the whole country mapping. In the following subsections the wind climate characteristics for the three sites are described and comparison is made with the modelled wind climate characteristics. The modelled wind climate has been calculated by carrying out a WAsP application for the three sites, using as input data the interim wind modelling generalized wind climates, elevation data from SRTM and an assessment of roughness length in the area based on satellite imagery. The microscale flow effects due to surface roughness length and orography in the region around the three stationssites are shown in Appendix B. 16 5.1 FFC Mast 5 PRELIMINARY VALIDATION Table 2 – Details of the measurement stations used for the preliminary validation. Name (Code) Locations Anemometer & (Vane) Heights [m] Site type FFC (FFC) 25.075900◦ N 67.972847◦ E 10, (28.5), 30, 60, (78.6), 80 inland Keti Bunder (KBU) 24.141467◦ N 67.943400◦ E 10, (28.5), 30, 60, (83.5), 85 inland Babur Bund (BBU) 25.126678◦ N 67.635241◦ E 10, (28.5) 30, 60, (78.5), 80 inland Hawkesbay (HBA) 24.867292◦ N 66.861662◦ E 10, (28.5), 30, 60, (78.5), 80 coastal 5.1 FFC Mast The FFC mast consists of a 4 measurement levels of anemometers mast (80 m, 60 m, 30 m, 10 m) as well as two wind vanes (78.5 m and 28.5 m). The top level has an anemometer installed at the top of the mast (81.5 m) as well as one on the side, mounted on a boom (80 m). The top anemometer suffers from less interference from the mast effect. Therefore it is the one to be used for validation purposes, with a measured mean wind speed of 7.7 m s−1 . The wind vane data as well as the anemometer data at the top was found to be sometimes faulty. When needed, the data was replaced with the one from the anemometer at 80m mounted on the boom, and the wind vane at 28.5 m. The data from these two additional sources was compared with MERRA 68◦ E 25◦ N to ensure the exactness of their data. Generalised modelling results give a mean annual wind speed of 7.7 m s−1 in good agree- ment with that measured. The measured winds are characteterised by flow from the southwest- west and northeast. This is well captured by the modelling, see Fig. 13. The measured wind speed at 80 m is at a minimum at 08:00 UTC, see Fig. 14a. Thereafter the wind speed increases and it reaches its maximum at 14:00 UTC. The size of the variation is less than 25% of the mean wind speed. The modelling results indicate peak winds at 100 m centered around 16:00 UTC and a minimum centered around 07:00 UTC; the size of the variation is approximately 25% of the mean wind speed, see Fig. 15a. The measured variation of wind speed during the year is characterized by the Monsoon from a South-Westerly direction see Fig. 14b; the size of the variation is approximately 20% of the mean wind speed. The modelling results indicate peak winds centered around July and a minimum in centered around November with a larger variation of approximately 40% of the mean wind speed, see Fig. 15b. 17 5.1 FFC Mast 5 PRELIMINARY VALIDATION a) b) Figure 13 – FFC measured (left) and modelled (right) wind roses at 80 m above ground level. The wind roses indicated the wind direction frequency distribution at the site. The modelled wind rose is derived from the generalized wind climate data from WRF and WAsP modelling. a) b) Figure 14 – FFC measured diurnal cycle of wind speed (left) at 80 m above ground level and the annual cycle (right) at 80 m above ground level. Note: the time is given in UTC. 9.5 11 9.0 10 8.5 |U | m s−1 |U | m s−1 9 8.0 8 7.5 7 7.0 0 3 6 9 12 15 18 21 24 1 2 3 4 5 6 7 8 9 10 11 12 a) Hour (UTC) b) MONTH Figure 15 – FFC modelled diurnal cycle (left) and annual cycle (right) at 100 m above ground level. This cycle data is not generalized or downscaled to the specific site. Note: the time is given in UTC. 18 5.2 Keti Bunder Mast 5 PRELIMINARY VALIDATION 5.2 Keti Bunder Mast The Keti Bunder mast consists of a 4 measurement levels of anemometers mast (85 m, 60 m, 30 m, 10 m) as well as two wind vanes (83.5 m and 28.5 m). The top level has an anemometer on either side of the mast, at 180◦ from each other, mounted on a boom (85 m). This top level is the one to be used for validation purposes as it can be corrected for mast effect, with a measured mean wind speed of 7.1 m s−1 . The wind vane data as well as the anemometer data at the top level was found to be sometimes faulty. When needed, the data from the anemometer at south was replaced with the one from the anemometer on the opposite side. The wind vanes operated normally except from the very end of November 2009 to the very end of March 2010 when both failed. The data was then excluded from the study as it could not be corrected with a functioning wind vane and long term data (like MERRA or CSFR) are not suitable as replacement. Also the anemometer at south was corrected for mast effect by replacing the data between 343◦ and 11◦ (north being 0◦ N) with the data from the north anemometer. Generalised modelling results give a mean annual wind speed of 7.45 m s−1 in good agree- ment with that measured. The measured winds are characteterised by flow from the west. This is well captured by the modelling, see Fig. 16. The measured wind speed at 80 m is lowest during the night and intensifies after sunrise to a maximum at 11:00 UTC. The diurnal variation of wind speed is approximately 10% of the mean wind speed, see Fig. 17a. The modelling results indicate peak winds centered around 18:00 UTC and a minimum centered around 07:00 UTC; the size of the variation is approximately 20% of the mean wind speed, see Fig. 18a. The measured variation of wind speed during the year is characterized by the South-Westerly Monsoon see Fig. 17b; the size of the variation is approximately 20% of the mean wind speed. The modelling results show a similar behaviour. They indicate peak winds centered around July and a minimum in centered around November the size of the variation is approximately 60% of the mean wind speed, see Fig. 18b. 19 5.2 Keti Bunder Mast 5 PRELIMINARY VALIDATION a) b) Figure 16 – Keti Bunder Mast measured (left) and modelled (right) wind roses at 85 m above ground level. The wind roses indicated the wind direction frequency distribution at the site. The modelled wind rose is derived from the generalized wind climate data from WRF and WAsP modelling. a) b) Figure 17 – Keti Bunder Mast measured diurnal cycle of wind speed (left) at 80 m above ground level and the annual cycle (right) at 80 m above ground level. Note: the time is given in UTC. 11 8.5 10 8.0 9 |U | m s−1 |U | m s−1 7.5 8 7 7.0 6 0 3 6 9 12 15 18 21 24 1 2 3 4 5 6 7 8 9 10 11 12 a) Hour (UTC) b) MONTH Figure 18 – Keti Bunder Mast modelled diurnal cycle (left) and annual cycle (right) at 100 m a.g.l. This cycle data is not generalized or downscaled to the specific site. Note: the time is given in UTC. 20 5.3 Babur Bund Mast 5 PRELIMINARY VALIDATION 5.3 Babur Bund Mast The Babur Bund mast consists of a 4 measurement levels of anemometers mast (80 m, 60 m, 30 m, 10 m) as well as two wind vanes (78.5 m and 28.5 m). The top level has an anemometer installed at the top of the mast (81.5 m) as well as one on the side (80 m), mounted on a boom (80 m). The top anemometer suffers from less interference from the mast effect therefore it is the one to be used for validation purposes, with a measured mean wind speed of 6.8/ms. The wind vane data as well as the anemometer data at the top was found to be sometimes faulty. When needed, the data was replaced with the one from the anemometer at 80 m mounted on the boom, and the wind vane at 28.5 m. The data from these two additional sources was compared with MERRA 68◦ N 25◦ N to ensure the exactness of their data. Generalised modelling results give a mean annual wind speed of 7.22 m s−1 in reasonable agreement with that measured. The measured winds are characteterised by flow from the southwest and northeast. This is well captured by the modelling, see Fig. 19. The measured wind speed at 80 m shows a maximum at 18:00 UTC. Then during the night it weakens and stays fairly constant until the wind become stronger again in the late afternoon. The measured size of variation of wind speed during the day is around 20% of the mean wind speed, see Fig. 20a. The modelling results indicate peak winds centered around 18:00 UTC and a minimum centered around 07:00 UTC; the size of the variation is approximately 35% of the mean wind speed, see Fig. 21a. The measured variation of wind speed during the year is characterized by the Monsoon and starts to intensify in late spring, see Fig. 17b; the size of the variation is approximately 25% of the mean wind speed. The modelling results indicate peak winds centered around July and a minimum in centered around November the size of the variation is approximately 50% of the mean wind speed, see Fig. 21b. 21 5.3 Babur Bund Mast 5 PRELIMINARY VALIDATION a) b) Figure 19 – Babur Bund Mast measured (left) and modelled (right) wind roses at 80 m above ground level. The wind roses indicated the wind direction frequency distribution at the site. The modelled wind rose is derived from the generalized wind climate data from WRF and WAsP modelling. a) b) Figure 20 – Babur Bund Mast measured diurnal cycle of wind speed (left) at 80 m above ground level and the annual cycle (right) at 80 m above ground level. Note: the time is given in UTC. 12 9 10 |U | m s−1 |U | m s−1 8 8 0 3 6 9 12 15 18 21 24 1 2 3 4 5 6 7 8 9 10 11 12 a) Hour (UTC) b) MONTH Figure 21 – Babur Bund Mast modelled diurnal cycle (left) and annual cycle (right) at 100 m above ground level. This cycle data is not generalized or downscaled to the specific site. Note: the time is given in UTC. 22 5.4 Hawksbay Mast 5 PRELIMINARY VALIDATION 5.4 Hawksbay Mast The Hawksbay mast consists of a 4 measurement levels of anemometers mast (80 m, 60 m, 30 m, 10 m) as well as two wind vanes (78.5 m and 28.5 m). The top level has an anemometer on either side of the mast, at 180◦ from each other, mounted on a boom (80 m). This top level is the one to be used for validation purposes as it can be corrected for mast effects, with a measured mean wind speed of 5.9 m s−1 . The wind vane data as well as the anemometer data at the top level was found to be working correctly. The western anemometer was corrected for mast effect by replacing the data between 73◦ and 117◦ (N being 0◦ ) with the data from the eastern anemometer. Generalised modelling results give a mean annual wind speed of 6.73 m s−1 in fair agree- ment with that measured. The measured winds are characteterised by flow from the west- southwest. This is well captured by the modelling, see Fig. 22. The measured variation of wind speed during the day is characterized a peak at 16:00 UTC and minimum at during the night, see Fig. 23a; the size of the variation is approximately 20% of the mean wind speed. The modelling results indicate peak winds centered around 12:00 UTC and a minimum centered around 06:00 UTC; the size of the variation is approximately 20% of the mean wind speed, see Fig. 24a. The measured variation of wind speed during the year is characterized by the Monsoon, see Fig. 23b; the size of the variation is approximately 20% of the mean wind speed. The modelling results indicate peak winds centered around July and a minimum in centered around October the size of the variation is approximately 55% of the mean wind speed, see Fig. 24b. 23 5.4 Hawksbay Mast 5 PRELIMINARY VALIDATION a) b) Figure 22 – Hawksbay Mast measured (left) and modelled (right) wind roses at 80 m above ground level. The wind roses indicated the wind direction frequency distribution at the site. The modelled wind rose is derived from the generalized wind climate data from WRF and WAsP modelling. a) b) Figure 23 – Hawksbay Mast measured diurnal cycle of wind speed (left) at 80 m above ground level and the annual cycle (right) at 80 m above ground level. Note: the time is given in UTC. 9 7.5 8 7.0 |U | m s−1 |U | m s−1 7 6.5 6 6.0 0 3 6 9 12 15 18 21 24 1 2 3 4 5 6 7 8 9 10 11 12 a) Hour (UTC) b) MONTH Figure 24 – Hawksbay Mast modelled diurnal cycle (left) and annual cycle (right) at 100 m above ground level. This cycle data is not generalized or downscaled to the specific site. Note: the time is given in UTC. 24 7 CONCLUSIONS 6 Discussion and recommendations An important issue to investigate further is whether land use (and its associated surface roughness length) in the standard WRF modelling system needs to be changed. It was found that for the Wind Atlas for South Africa project detailed inspection of the standard landuse maps in WRF showed serious problems. Furthermore, the simulation for Pakistan requires the inclusion of the Himalayas. Especially in the outer and intermediate domains with lower grid- spacing (45 km and 15 km, respectively), the mountain height can not be resolved properly. In phase 3 of the project a possible sensitivity to the flow development around unresolved mountains with a Subgrid Scale Orography (Jim´ enez and dudhia, 2012) parametrization are to be investigated. The measurement data is essential to the validation work required in Phase 3. Suggestions for locations for the measurement masts are given in Section D. Through the measurements a better understanding of the wind energy relevant meteorology of the country will be gained, an improved configuration of the modelling system will be developed and tested, and an uncertainty estimate of the final wind atlas can be determined. There are indications of nocturnal low level jets occurring which can be important for wind resources of the country. The jets’ intensities and vertical extensions are expected to depend on the turbulence parametrization, as well as on the surface fluxes parameterised by the surface layer scheme. The presence or absence of boundary layer clouds modelled by the micro-physics scheme influence the surface energy balance. Finally the structure of the profiles could improve with vertical resolution. 7 Conclusions This report has described the Phase 1 interim mesoscale wind modelling for Pakistan. The simulation methodology, the configuration of the WRF model and the generalization method have been reported. The results of the wind modelling are presented in the form of simu- lated and generalized wind maps, and in the form of generalized wind climate data files. A preliminary validation for a limited area of the country was performed. It indicates that while the timing of the maximum and minimum annual cycle is modelled well, the amplitude of the variation seems to be overestimated. For the diurnal cycles, the agreement is fair, in terms of timing of the peak winds. However, it appears that the nocturnal winds drop somewhat more in the measurements at 80 m compared to modelled winds at 100 m. 25 REFERENCES REFERENCES References Badger, J., H. Frank, A. N. Hahmann, and G. Giebel, 2014: Wind-climate estimation based on mesoscale and microscale modeling: Statistical-dynamical downscaling for wind energy applications. J. Appl. Meteor. Climatol., 53, 1901–1919. na, and G. Giebel, 2014: Evaluating winds and vertical wind Draxl, C., A. N. Hahmann, A. Pe˜ shear from WRF model forecasts using seven PBL schemes. Wind Energy, 17, 39–55. Frank, H. and L. Landberg, 1997: Modelling the wind climate of Ireland. Bound.-Layer Me- teor., 85 (3), 359–378, doi:{10.1023/A:1000552601288}. Hahmann, A. N., D. Rostkier-Edelstein, T. T. Warner, F. Vandenberghe, Y. Liu, R. Babarsky, and S. P. Swerdlin, 2010: A Reanalysis System for the Generation of Mesoscale Climatogra- phies. J. Appl. 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C., et al., 2008: A Description of the Advanced Research WRF Version 3. Tech. Rep. NCAR/TN–475+STR, National Center for Atmospheric Research. Storm, B., J. Dudhia, S. Basu, A. Swift, and I. Giammanco, 2009: Evaluation of the weather research and forecasting model on forecasting low-level jets: implications for wind energy. Wind Energy, 12 (1), 81–90. Troen, I. and E. L. Petersen, 1989: European Wind Atlas. Published for the Commission of the European Communities, Directorate-General for Science, Research, and Development, Brussels, Belgium by Risø National Laboratory. Tuller, S. E. and A. C. Brett, 1984: The characteristics of wind velocity that favor the fitting of a Weibull distribution in wind-speed analysis. J. Appl. Meteor. Clim., 23 (1), 124–134, doi:10.1175/1520-0450(1984)0232.0.CO. Vincent, C. L., A. N. Hahmann, and M. C. Kelly, 2012: Idealized mesoscale model simulations of open cellular convection over the sea. Bound.-Layer Meteor., 142 (1), 103–121, doi: DOI10.1007/s10546-011-9664-7. WASA, 2014: Wind atlas for south africa. [Online], http://wasa.info.org. 27 A DETAILED DESCRIPTION OF GENERALIZATION A Detailed description of generalization A.1 Basic generalization equations The generalization of WRF model winds is an extension of the KAMM/WAsP generalization method described in Badger et al. (2014). In the first step, the time series of wind speed and direction are corrected for orography and roughness change, which are a function of wind direction and height. Given a time series of wind speed, u = u(z, t), and wind direction, φ = φ(z, t), which are functions of height and time, intermediate values, u ˆ, are given ˆ and φ by u ˆ= u (1) (1 + δAo )(1 + δAr ) ˆ = φ − δφo , φ (2) where δAo , δφo and δAr and are generalization factors for orography in wind speed and direction and roughness change, respectively. From the time series of corrected wind speed and direction ”wind classes” are determined. The binning is based on wind direction sectors, wind speed and surface stability according to the Obukhov length as described in section A.2. From the binning, mean values of wind speed, u, and wind direction, φ and typical Obuhov length L˜ , together with the frequency of occurrence, F , of each bin are determined. For simplicity, we will drop the over-bar from the equations that follow, but it is understood that they are applied to the mean values of each bin and not the individual time series values. From the corrected wind speed value we obtain an intermediary friction velocity, u ˆ∗ ˆ κu ˆ∗ = u (3) ln[(z/z ˜ )] ˆ0 ) + ψ (z/L where zˆ0 is the downstream surface roughness length and ψ is a stability correction function that adjust the logarithmic wind profile due to non-neutral stability conditions and κ is the von K´arm´ an constant. The stability correction uses the relationship: −31.58[1 − exp(−0.19z/L)] if x ≥ 0 ψ (z/L) = (4) 2 log[0.5(1 + x)] + log[0.5(1 + x2 )] − 2 tan−1 (x) + 1.5746 if x < 0 where x = (1 − 19z/L). We use this function with a typical value of the Obukhov length from each wind class bin (see table 3). This procedure avoids using the similarity theory on wind profiles that lie outside the bounds of validity of the theory and that sometimes occur in the WRF simulations. In the next step, we use the geostrophic drag law, which is used for neutral conditions to determine nominal geostrophic wind speeds, G ˆ , and wind directions, αG , are calculated, using the intermediate friction velocity and wind direction: 2 ˆ∗ ˆ=u G ln uˆ∗ −A + B2, (5) κ fzˆ0 ˆG = −sin−1 B u sin φ ˆ∗ , (6) κG ˆ 28 A.2 Sectorization A DETAILED DESCRIPTION OF GENERALIZATION where A = 1.8 and B = 5.4 are two empirical parameters and f is the Coriolis parameter, ˆG is the angle between the near-surface winds and the geostrophic wind. and φ ˆ∗G , for a standard roughness length z0,std , To obtain a new generalized friction velocity, u Equation 5 is reversed by an iterative method, 2 ˆ∗G ˆ=u G ln ˆ ∗G u −A + B2, (7) κ f z0,std Finally, the generalized wind speed, uG , is obtained by using the logarithmic wind profile law ˆ ∗G u uG = . (8) κ ln(z/z0,std ) A.2 Sectorization Table 3 – Stability ranges and typical values used in the generalization procedure. Stability class Obukhov length Typical Obukhov value range (m) ˜ (m) L Very unstable -50 < L < -100 -75 Unstable -100 < L < -200 -150 Near unstable -200 < L < -500 -350 Neutral L < -500; L > 500 10000 Near stable 200 < L < 500 350 Stable 50 < L < 200 125 Very stable 10 < L < 50 30 To apply the generalization procedure to the WRF-model output, winds from the mesoscale model simulations are binned according to wind speed (usually in 2.5 m s−1 bins), wind direction (usually 48 sectors of 7.5◦ width) and seven stability class based on the Obukhov length that is also an output from the WRF simulation. The ranges for the stability classes are listed in Table 3 together with the “typical” length used in the generalization. The procedure is carried out for each model grid point independently. In practice, time series of wind speed and direction at the desired vertical levels and 1/L are extracted from the model output files. The generalization procedure is then carried out on each time series file. A.3 Weibull distribution fit The frequency distribution of the horizontal wind speed can often be reasonably well described by the Weibull distribution function (Tuller and Brett, 1984): k w −1 k kw u u F (u) = exp − , (9) Aw Aw Aw 29 A.3 Weibull distribution fit A DETAILED DESCRIPTION OF GENERALIZATION where F (u) is the frequency of occurrence of the wind speed u. In the Weibull distribution the scale parameter Aw has wind speed units and is proportional to the average wind speed calculated from the entire distribution. The shape parameter k (≥1) describes the skewness of the distribution function. For typical wind speed distributions, the kw -parameter has values in the range of 2 to 3. From the values of Aw and kw , the mean wind speed U ( m s−1 ) and mean power density E (W m−2 ) in the wind can be calculated from: 1 U = Aw Γ 1 + (10) kw 1 3 3 E = ρAw · Γ 1 + (11) 2 kw where ρ is the mean density of the air and Γ is the gamma function. We use the moment fitting method as used in the Wind Atlas Analysis and Application Program (WAsP) for estimating the Weibull parameters. The method is described in detail in Troen and Petersen (1989). Basically this method estimates Aw and kw to fit the power density in the time series instead of the mean wind speed. The Weibull fit is done for the ensemble of wind speeds in each wind direction bin (usually 12 direction sectors) for each standard height (usually 5 heights: 10, 25, 50, 100 and 200 m) and standard roughness lengths (usually 5 roughness: 0.0002 (water), 0.03, 0.1, 0.4, 1.5 m). The 25 Weibull fits for each wind direction sector use the method described above. This sector-wise transformation of Weibull wind statistics—i.e. transforming the Weibull Aw and kw parameters to a number of reference heights over flat land having given reference roughnesses—uses not only the geostrophic drag law, but also a perturbation of the drag law, with the latter part including a climatological stability treatment. The transformation and stability calculation is consistent with that implemented in WAsP and outlined in Troen and Petersen (1989), with further details given in Kelly and Troen (2014). The transformation is accomplished via perturbation of both the mean wind and expected long-term variance of wind speed, such that both Weibull-Aw and kw are affected. When purely neutral conditions (zero stability effects) are presumed for the wind statistics to be transformed, there is still a perturbation introduced, associated with the generalized (reference) conditions in the wind atlas. This perturbation uses the default stability parameter values found in WAsP; it is negated upon subsequent application of the generalized wind from a given reference height and roughness to a site with identical height and surface roughness, using WAsP with its default settings. The climatological stability treatment in the generalization depends on the unperturbed Weibull parameters and effective surface roughness (Troen and Petersen, 1989), as well as the mesoscale output heights and wind atlas reference heights (though the latter disappears upon application of wind atlas data via WAsP). Figure ?? shows the structure of the resulting WAsP ”lib” file. It is structured as Weibull Aw ’s and kw ’s for each sector, height and standard roughness length. The first row contains information about the geographical location of the wind climate represented in the lib-file. The second row lists the number of roughness classes (5), heights (5), and sectors (12), respectively. In the third and fourth row, the actual roughness (m) and heights (m) are listed. Below these header lines, a succession of frequencies of wind direction (1 line), values of Weibull-Aw (1 line) and Weibull-kw (1 line) for each roughness class and height are printed 30 A.3 Weibull distribution fit A DETAILED DESCRIPTION OF GENERALIZATION for each sector (12 sectors per line). This type of file can be used and displayed (Figure 10) in WAsP. 31 B MICROSCALE FLOW EFFECTS B Microscale flow effects In this section the microscale flow effects at the preliminary verification sites are displayed. The microscale flow effects are calculated by WAsP using elevation data from SRTM and an assessment of roughness length in the area based on satellite imagery. 32 B.1 FFC Mast B MICROSCALE FLOW EFFECTS B.1 FFC Mast Figure 25 – FFC WAsP map Figure 26 – FFC microscale flow effect roses at 80 m above ground level. Left: Roughness change effects. Right: Orographic (elevation change) effects. A green band in a sector indicates a speed-up effect for winds coming from that direction sector. A red band in a sector indicates a slow-down effect for winds coming from that direction sector. If no bands are visible it means that the microscale flow effects due to roughness change or orography are small. However, there will remain an impact on the wind speed due to local surface roughness length. 33 B.2 Keti Bunder Mast B MICROSCALE FLOW EFFECTS B.2 Keti Bunder Mast Figure 27 – BKU WAsP map Figure 28 – Keti Bunder Mast microscale flow effect roses at 85 m above ground level. Left: Roughness change effects. Right: Orographic (elevation change) effects. A green band in a sector indicates a speed-up effect for winds coming from that direction sector. A red band in a sector indicates a slow-down effect for winds coming from that direction sector. If no bands are visible it means that the microscale flow effects due to roughness change or orography are small. However, there will remain an impact on the wind speed due to local surface roughness length. 34 B.3 Babur Bund Mast Mast B MICROSCALE FLOW EFFECTS B.3 Babur Bund Mast Mast Figure 29 – BBU WAsP map Figure 30 – Babur Bund Mast microscale flow effect roses at 80 m above ground level. Left: Roughness change effects. Right: Orographic (elevation change) effects. A green band in a sector indicates a speed-up effect for winds coming from that direction sector. A red band in a sector indicates a slow-down effect for winds coming from that direction sector. If no bands are visible it means that the microscale flow effects due to roughness change or orography are small. However, there will remain an impact on the wind speed due to local surface roughness length. 35 B.4 Hawksbay Mast Mast B MICROSCALE FLOW EFFECTS B.4 Hawksbay Mast Mast Figure 31 – HBA WAsP map Figure 32 – Hawksbay Mast microscale flow effect roses at 80 m above ground level. Left: Rough- ness change effects. Right: Orographic (elevation change) effects. A green band in a sector indicates a speed-up effect for winds coming from that direction sector. A red band in a sector indicates a slow-down effect for winds coming from that direction sector. If no bands are visible it means that the microscale flow effects due to roughness change or orography are small. However, there will remain an impact on the wind speed due to local surface roughness length. 36 C WRF NAMELIST C WRF namelist &time_control start_year = YY1, YY1, YY1 start_month = MM1, MM1, MM1 start_day = DD1, DD1, DD1 start_hour = HH1, HH1, HH1 start_minute = 00, 00, 00 start_second = 00, 00, 00 end_year = YY2, YY2, YY2 end_month = MM2, MM2, MM2 end_day = DD2, DD2, DD2 end_hour = HH2, HH2, HH2 end_minute = 00, 00, 00 end_second = 00, 00, 00 interval_seconds = 21600, input_from_file = .T., .T., .T. history_interval = 180,180, 60 frames_per_outfile = 1, 1, 3 restart = .false., restart_interval = 100000, io_form_history = 2 io_form_restart = 2 io_form_input = 2 io_form_boundary = 2 auxhist3_outname = "winds_d_", auxhist3_interval = 0, 0, 10 frames_per_auxhist3 = 1, 1, 6 io_form_auxhist3 = 2, auxinput4_inname = "wrflowinp_d", auxinput4_interval = 360,360,360 io_form_auxinput4 = 2, debug_level = 0, / iofields_filename = "WAfields.txt","WAfields.txt","WAfields.txt" ignore_iofields_warning = .false., &domains time_step = 180, time_step_fract_num = 0, time_step_fract_den = 22, max_dom = 3, parent_id = 0, 1, 2 parent_grid_ratio = 1, 3, 3 37 C WRF NAMELIST s_we = 1, 1, 1 e_we = 96, 187, 325, s_sn = 1, 1, 1 e_sn = 96, 187, 397, s_vert = 1, 1, 1 e_vert = 41, 41, 41 grid_id = 1, 2, 3 i_parent_start = 1, 17, 27, j_parent_start = 1, 17, 27, num_metgrid_levels = 38, p_top_requested = 5000, eta_levels = 1.0000, 0.9965, 0.9930, 0.9895, 0.9860, 0.9825, 0.9714, 0.9539, 0.9308, 0.9034, 0.8724, 0.8388, 0.8034, 0.7669, 0.7298, 0.6926, 0.6558, 0.6196, 0.5842, 0.5499, 0.5168, 0.4848, 0.4540, 0.4244, 0.3958, 0.3683, 0.3417, 0.3158, 0.2906, 0.2659, 0.2415, 0.2174, 0.1934, 0.1694, 0.1453, 0.1212, 0.0969, 0.0698, 0.0454, 0.0215, 0.000 dx = 45000,15000, 5000, dy = 45000,15000, 5000, parent_time_step_ratio = 1, 3, 3 feedback = 0, smooth_option = 0, / &physics mp_physics = 4, 4, 4 ra_lw_physics = 1, 1, 1 ra_sw_physics = 1, 1, 1 radt = 10, 10, 10 sf_sfclay_physics = 2, 2, 2 sf_surface_physics = 2, 2, 2 bl_pbl_physics = 2, 2, 2 bldt = 0, 0, 0 cu_physics = 1, 1, 0 cudt = 5, 5, 5 fractional_seaice = 1, seaice_threshold = 0., isfflx = 1, ifsnow = 0, icloud = 1, surface_input_source = 1, num_land_cat = 21, num_soil_layers = 4, 38 C WRF NAMELIST sst_update = 1, maxiens = 1, maxens = 3, maxens2 = 3, maxens3 = 16, ensdim = 144, / &fdda grid_fdda = 1, 0, 0 gfdda_inname = "wrffdda_d", gfdda_end_h = 300, 0, 0 gfdda_interval_m = 360, 0, 0 fgdt = 0, 0, 0 if_no_pbl_nudging_uv = 0, 0, 0 if_no_pbl_nudging_t = 1, 0, 0 if_no_pbl_nudging_q = 1, 0, 0 if_zfac_uv = 1, 0, 0 k_zfac_uv = 10, 0, 0 if_zfac_t = 1, 0, 0 k_zfac_t = 10, 0, 0 if_zfac_q = 1, 0, 0 k_zfac_q = 10, 0, 0 guv = 0.0003, 0.000075, 0.000075, gt = 0.0003, 0.000075, 0.000075, gq = 0.0003, 0.000075, 0.000075, if_ramping = 0, dtramp_min = 60.0, io_form_gfdda = 2, / &dynamics w_damping = 1, diff_opt = 1, km_opt = 4, diff_6th_opt = 2, 2, 2 diff_6th_factor = 0.06, 0.08, 0.1 base_temp = 290. damp_opt = 0, zdamp = 5000., 5000., 5000. dampcoef = 0.15, 0.15, 0.15 khdif = 0, 0, 0 kvdif = 0, 0, 0 non_hydrostatic = .true.,.true.,.true. moist_adv_opt = 1, 1, 1 scalar_adv_opt = 1, 1, 1 39 C WRF NAMELIST / &bdy_control spec_bdy_width = 5, spec_zone = 1, relax_zone = 4, specified = .true., .false.,.false. nested = .false., .true., .true. / &grib2 / &namelist_quilt nio_tasks_per_group = 0, nio_groups = 1, / 40 D SUGGESTION FOR PHASE 2 MEASUREMENT REGIONS D Suggestion for Phase 2 measurement regions The regions indicate areas in Pakistan with wind resource and distinct climate type. The regions can be used as guidance in decisions related to locating verification masts. 41