95802 Interim mesoscale wind modelling and preliminary validation report for Vietnam DRAFT Jake Badger1 , Patrick J. H. Volker1 , Andrea N. Hahmann1 , Jens Carsten Hansen1 , Brian 0. Hansen1 1 Department of Wind Energy, Technical University of Denmark (DTU), Risø Campus, Denmark February 13, 2015 2 METHOD Abstract This document reports on the methods used in phase 1 of the ESMAP wind map- ping project for Vietnam. 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. A preliminary validation is described. A meteorological phenomena of particular interest is the nocturnal low level jet and this is examined in the model output data. Finally recommendations for the next steps in the project are stated. 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 Vietnam phase 1 output. In Section 5 a preliminary validation of the interim modelling results against observations is presented. Section 6 presents an analysis of nocturnal low level jets, which is a wind energy relevant phenomena seen in the WRF simulations. Section 7 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 as recommendations for the project in general. Finally, Section 8 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. 1 3 MODELLING 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 meteorological phenomena in the model output relevant to wind energy can be investigated, and an understanding of the important meteorological phenonema 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 Section 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 82 × 90 grid points), 15 km × 15 km (first nested domain, D2, with 118 × 157 grid points) and 5 km × 5 km (second nest, D3, with 223 × 361 grid points). Maps of the model domains are displayed in Fig. 1. In the vertical the model was configured with 41 levels with model top at 50 hPa. The 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 × 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 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 is presented in Appendix C. 4 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; 82 × 90 grid points) with 45 km grid spacing; 2 nested domains: D2 (118 × 157 grid points) using 15 km and D3 (223 × 361 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 Programme. Simulation setup: Initial, boundary conditions, and fields for grid nudging come from the The Climate Forecast System Reanalysis [1979 - 2010] (CFSR) at resolution of 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 0.25 degree (OISST) at resolution 0.5 ◦ × 0.5◦ resolution () 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 15; 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. 5 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. Figure 2 – 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 period January 2003 – December 2010, and were run in a series of 11-day long overlapping simulations, with the output from the first day being discarded, see Fig. 28 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 15 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 6 4 RESULTS (e.g., linear or polynomial interpolation of the wind components). For the model grid points inside Vietnam 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 is presented based on the 8 years of simulation, covering the years 2003 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. 4 and 5. 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. Static Data Height 10min Data pro- WRF Raw 60min (WindsT) Time-series Generalization .lib reduction Interpolation cessing Raw 10min (Winds) Figure 3 – 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 10 cm everywhere, see Figs. 6 and 7. 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 Vietnam. An example of generalized wind climate file data is given in Fig. 8. Figure 9 shows the location pertaining to the more than 12000 generalized wind climate files. 7 4 RESULTS Figure 4 – Mean annual simulated wind speed at 100 m above ground level from WRF simulation at 5 km × 5 km grid spacing for the period 2003 to 2010 inclusive. The colour scale indicates the wind speed in m s−1 . 8 4 RESULTS Figure 5 – 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 2003 to 2010 inclusive. The colour scale indicates the wind power density in W m−2 . 9 4 RESULTS Figure 6 – Mean annual generalized wind speed at 100 m above ground level from WRF simulation at 5 km × 5 km grid spacing for the period 2003 to 2010 inclusive. The standard conditions are flat terrain with surface roughness length of 10 cm everywhere. The colour scale indicates the wind speed in m s−1 . 10 4 RESULTS Figure 7 – 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 2003 to 2010 inclusive. The standard conditions are flat terrain with surface roughness length of 10 cm everywhere.The colour scale indicates the wind power density in W m−2 . 11 4 RESULTS Figure 8 – 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. 12 4 RESULTS Figure 9 – Above: The location of the generalized wind climate data for the whole of Vietnam shown in Google Earth. Below: A detail of generalized 13 wind climate data coverage including how a user of the date can find out about the data filename using Google Earth. 5 PRELIMINARY VALIDATION 5 Preliminary validation Three stations, chosen from the 10 the MoIT/GIZ stations, were used for the purpose of a preliminary validation of the interim mesoscale wind modelling. The three stations were choosen to give a diverse range of conditions. The southernmost and northernmost stations were choosen, both near coastline, and a station midway between these and inland was choosen. The names and locations of the stations are given in Table 2. Some details of the measurements are also given. The locations are shown in the map in Fig. 10. Table 2 – Details of the three MoIT/GIZ measurement stations used for the preliminary validation. Name Locations Heights [m] Start date 1st analysis period Site characteristic Thanh Hai 40,60,80 2012/6 2012/6-2013/5 coastal Ea Drang 40,60,80 2012/6 2012/6-2013/5 inland Hai Ninh 40,60,80 2012/6 2012/6-2013/5 coastal 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 and photographs from the site installation reports. The microscale flow effects due to surface roughness length and orography in the region around the three stationssites are shown in Appendix B. 5.1 Thanh Hai This station is located in the rather flat delta region in the southern part of Vietnam. The measured winds are characteterised by flow concentrated in westerly and eastlerly directions and this is well captured by the modelling, see Fig. 11. The measured variation of wind speed during the day is characterised by peak winds centered around 16:00 LT (local time), and a minumum centered around 06:00 LT, see Fig. 12; the size of the variation is approximately 15% of the mean wind speed. The modelling results indicate peak winds centered around 16:00 LT, and a minimum centered around 07:00 LT; the size of the variation is approximately 20% of the mean wind speed, see Fig. 13. The measured variation of wind speed during the year is characterised by peak winds centered around January, and a secondary peak centered around July, see Fig. 12; the size of the variation is approximately 66% of the mean wind speed. The modelling results indicate peak winds centered around January, and a secondary peak centered around August; the size of the variation is approximately 40% of the mean wind speed, see Fig. 13. 5.2 Ea Drang This station is located in an inland undulating terrain region. The measured winds are char- acteterised by flow concentrated in southwesterly and northeastlerly directions and this is well 14 5.3 Hai Ninh 5 PRELIMINARY VALIDATION captured by the modelling, see Fig. 14. The measured variation of wind speed during the day is characterised by prolonged maxi- mum winds from 10:00 LT to 15:00 LT, and a minumum at 06:00 LT, see Fig. 15; the size of the variation is approximately 40% of the mean wind speed. The modelling results however indicate peak winds at 19:00 LT, and a minimum centered around 07:00 LT; the size of the variation is approximately 20% of the mean wind speed, see Fig. 16. The measured variation of wind speed during the year is characterised by peak winds centered around January, and a secondary peak centered around July, see Fig. 15; the size of the variation is approximately 60% of the mean wind speed. The modelling results indicate peak winds centered around December, and a secondary peak centered around July; the size of the variation is approximately 60% of the mean wind speed, see Fig. 16. 5.3 Hai Ninh This station is located in a coastal region in central Vietnam. The site is located in flat terrain, but there is elevated terrain to the west. The measured winds are characteterised by flow concentrated in northwesterly and southwesterly directions. These main features are well captured by the modelling, see Fig. 17. The modelling also has features winds sometimes coming from southeast, more frequently than was measured in the single year measurement period. The measured variation of wind speed during the day is characterised by peak winds centered around 14:00 LT (local time), and a prolonged minumum from 00:00 LT to 07:00 LT, see Fig. 18; the size of the variation is approximately 25% of the mean wind speed. The modelling results indicate peak winds centered around 13:00 LT, and a minimum centered around 00:00 LT; the size of the variation is approximately 20% of the mean wind speed, see Fig. 19. There is an indication of secondary maxima in wind speed at 05:00 LT. The measured variation of wind speed during the year is characterised by peak winds centered around July, and a secondary peak centered around December, see Fig. 18; the size of the variation is approximately 40% of the mean wind speed. The modelling results indicate peak winds centered around November, and a secondary peak centered around June; the size of the variation is approximately 35% of the mean wind speed, see Fig. 19. 15 5.3 Hai Ninh 5 PRELIMINARY VALIDATION Figure 10 – The location of the sites used for the preliminary validation. 16 5.3 Hai Ninh 5 PRELIMINARY VALIDATION Figure 11 – Thanh Hai 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. The measured wind rose graphic comes from the MoIT/GIZ 1st year measurement reports. Figure 12 – Thanh Hai measured diurnal cycle of wind speed (left) at 80, 60 and 40 m above ground level and the annual cycle (right) at 80 m above ground level. For the annual cycle monthly wind speed (blue) and direction (green) are shown. Note: the time is given in local time. The graphics come from the MoIT/GIZ 1st year measurement reports. 9.0 10 8.5 9 |U | m s−1 |U | m s−1 8.0 8 7.5 7 0 3 6 9 12 15 18 21 24 1 2 3 4 5 6 7 8 9 10 11 12 Hour (UTC) MONTH Figure 13 – Thanh Hai 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. 17 5.3 Hai Ninh 5 PRELIMINARY VALIDATION Figure 14 – Ea Drang 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. The measured wind rose graphic comes from the MoIT/GIZ 1st year measurement reports. Figure 15 – Ea Drang measured diurnal cycle of wind speed (left) at 80, 60 and 40 m above ground level and the annual cycle (right) at 80 m above ground level. For the annual cycle monthly wind speed (red) and direction (green) are shown. Note: the time is given in local time. The graphics come from the MoIT/GIZ 1st year measurement reports. 12 11 9.0 10 |U | m s−1 |U | m s−1 9 8.5 8 7 8.0 0 3 6 9 12 15 18 21 24 1 2 3 4 5 6 7 8 9 10 11 12 Hour (UTC) MONTH Figure 16 – Ea Drang 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 5.3 Hai Ninh 5 PRELIMINARY VALIDATION Figure 17 – Hai Ninh 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. The measured wind rose graphic comes from the MoIT/GIZ 1st year measurement reports. Figure 18 – Hai Ninh measured diurnal cycle of wind speed (left) at 80, 60 and 40 m above ground level and the annual cycle (right) at 80 m above ground level. For the annual cycle monthly wind speed (blue) and direction (green) are shown. Note: the time is given in local time. The graphics come from the MoIT/GIZ 1st year measurement reports. 9 8.0 8 |U | m s−1 |U | m s−1 7.5 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 Hour (UTC) MONTH Figure 19 – Hai Ninh 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. 19 6 THE MODELLED LOW LEVEL JET 6 The modelled low level jet In the previous section we have presented preliminary verification of the WRF modelling, and it has been particularly interesting to see the signature of nocturnal wind features in the modelling particularly at Ea Drang but also to some extent at the other sites. The nocturnal winds could be due to the nocturnal low level jet phenomena and this has an important influence on the wind resources for the country. For example in the mid-West USA the nocturnal low level jet phenomena is very relevant to exploitation of wind energy (Storm et al., 2009). Nocturnal low level jet phenomena have been identified through modelling, over the South- east Asian penisular before. Rife et al. (2010) found significant Low Level Jet (LLJ) indices over Vietnam. For his analysis he used the NCAR’s Climate Four Dimensional Data Assimila- tion system (CFDDA). Their LLJ index was a function of the wind speed difference between midnight and noon 12 hours before at 500 m over surface level, considering also the difference in wind speed above the boundary layer. A LLJ was detected when the 500 m wind speed at midnight was higher than that at noon 12 hours before, together with the condition that the midnight wind speed at 500 m needed to be higher than the wind aloft. In a simular way we identify LLJ features from the WRF simulations from January 2003 to December 2010 for three measurement sides at Ea Drang, Thanh Hai and Hai Ninh. For the detection of a LLJ we apply our own low level jet index. 6.1 EaDrang At Eadrang (108.19E 13.21N) we found in the 8 years of simulations 1868 LLJs with an index larger than 1. The distribution is plotted in Fig. (20). The figure shows that the bulk of the 300 Frequency 200 100 0 0 5 10 15 20 LLJi (m/s) Figure 20 – Frequency of occurrences of the LLJ at EaDrang. LLJi is between 4 and 7 m s−1 . In the next figures examples of LLJs have been plotted. On the left panel we show the absolute wind speed as a function of the height, in the center panel the potential temperature, θ, which is conserved under dry conditions and on the right panel the modelled turbulence kinetic energy, TKE. The yellow color indicates local noon, whereas the gray colors range from 18:00 to 00:00. In Fig. (21) we plot the profiles for the 12th of March 20 6.1 EaDrang 6 THE MODELLED LOW LEVEL JET Wind speed with WD(200m) = 18 ° Potential temperature Turbulence Kinetic Energy 12 ICT 18 ICT 21 ICT 00 ICT 1.0 1.0 1.0 z (km) z (km) z (km) 0.5 0.5 0.5 0.0 0.0 0.0 0 5 10 15 20 290 295 300 305 310 0 1 2 3 4 U (m s) θ ( K) TKE (m2 s2) Figure 21 – From left to right: Absolute velocity, Potential temperature and turbulence kinetic energy at EaDrang for the 12th of March 2005. The yellow color indicate local noon and the gray colors range from 18:00 LT to 00:00 LT. 2005. The LLJi was 8.1 m s−1 on this day. We find at noon a deep convective layer driven by solar radiation causing surface heating, with weak boundary layer winds of less than 5 m s−1 from the west northwest. The boundary layer is well mixed with an almost constant turbulence kinetic energy in height. For the modelling of turbulence a second order closure approach is used, which accounts for turbulence production from buoyancy and vertical shear in horizontal velocity. At 18:00 LT after sunset we find a stable boundary layer formed by longwave radiative cooling at the surface. It extends from the surface up to around 70 m. Above 70 m, in the residual layer, the temperature is still well mixed and the turbulence kinetic energy is zero. The lack of friction in these layers allows a nocturnal LLJ to develop. Its maximum is found at the top of the inversion at the point the residual layer without turbulence mixing starts. Proceeding in time we find that the surface temperature keeps decreasing due to the outgoing radiation. The surface inversion increases due to the increased turbulence shear production, which arises from the lower part of the jet. Together with the stable boundary layer growth also the height of the maximum velocity increases. The amplitude of the LLJ keeps increasing up until midnight. This agrees with observed LLJs that originate from an inertial oscillation (Blackadar, 1957) and (Van de Wiel et al., 2010). From Fig. (21) we find furthermore that the temperature seems to be conserved fairly well. Therefore, there is no or only little temperature or velocity advection occurring at this night. In Fig. (22) we show the second example the 7th of January 2003 with a LLJi of 8.7 m s−1 . This case shows a shallower boundary layer, which reaches around 700 m at noon. Above the boundary layer we find a decreasing geostrophic wind with height. This indicates on this day that aloft colder air masses are found to the North and warmer air masses to the South. After sunset a stable layer develops from the surface upwards. In this case the surface inversion height is found at around 500 m where also the maximum velocity is found. Furthermore it can be noted that no temperature advection is happening. 21 6.2 ThanhHai 6 THE MODELLED LOW LEVEL JET Wind speed with WD(200m) = 31 ° Potential temperature Turbulence Kinetic Energy 12 ICT 18 ICT 21 ICT 00 ICT 1.0 1.0 1.0 z (km) z (km) z (km) 0.5 0.5 0.5 0.0 0.0 0.0 0 5 10 15 20 290 295 300 305 310 0 1 2 3 U (m s) θ ( K) TKE (m2 s2) Figure 22 – Similar to Fig.(21), this time for the 7th of January 2003. 6.2 ThanhHai At ThanhHai, the most southern station (106.68E 9.88N), the the number of LLJs with an index higher than 1 is 1283, which is lower than at EaDran. Its distribution is wider than that at EaDrang. Here also LLJs with an index higher than 15 are found, see Fig. (23). In 300 Frequency 200 100 0 0 5 10 15 20 LLJi (m/s) Figure 23 – Frequency of occurrences of the LLJ at ThanhHai. Fig. (24) we plot the wind, temperature and TKE conditions for the 6th of February 2004 at ThanhHai. The LLJi was 12.1 m s−1 that day. We find very different conditions at this southern location. We find no cooling of the potential temperature at the surface. Instead a stable inversion layer starts to develop above a mixed layer near to the surface. The top of the inversion remains until mid-night at around 500 m. This is the height at which the residual layer starts and where the wind velocity reaches its maximum, due to the absence of 22 6.3 HaiNinh 6 THE MODELLED LOW LEVEL JET Wind speed with WD(200m) = 11 ° Potential temperature Turbulence Kinetic Energy 12 ICT 18 ICT 21 ICT 00 ICT 1.0 1.0 1.0 z (km) z (km) z (km) 0.5 0.5 0.5 0.0 0.0 0.0 0 10 20 30 290 295 300 305 310 0.0 0.5 1.0 1.5 2.0 U (m s) θ ( K) TKE (m2 s2) Figure 24 – Similar to Fig.(21), this at time at ThanhHai for the 6th of February 2004. turbulence. Below, closer to the surface, in the mixed layer, where friction still is present, the wind is also increasing, driven by the velocity maximum at 500 m. The higher wind speeds at around 200 m are therefore not LLJs. These wind speeds could however be favourable for wind energy, since the shear is not as high as in the LLJ. 6.3 HaiNinh HaiNinh, located in the North of Vietnam (106.77E 17.32N), shows only 721 nocturnal LLJs with an index larger than 1. Its distribution is shown in Fig. (25). The distribution shows that the LLji is almost evenly distributed. Most of the LLJs are occurring between March and May. The findings from above lead to the conclusion that the WRF model is able to simulate 300 Frequency 200 100 0 0 5 10 15 20 LLJi (m/s) Figure 25 – Frequency of occurrences of the LLJ at HaiNinh. 23 8 CONCLUSIONS a nocturnal LLJ that follows its development as described in literature (Blackadar, 1957), (Baas, 2009) and (Van de Wiel et al., 2010). Its intensity and vertical position is expected to depend on the turbulence parametrization, as well as on the surface fluxes parametrize by the surface layer scheme. Also 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 im- prove with vertical resolution. Additional velocity, temperature and pressure measurements at heights to a height of 200 m would lead to additional insight of the presence of nocturnal LLJs. 7 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. Phan (2014) indicates some good suggestions for sources of reliable land cover classificiation, including datasets from the Ministry of Natural Resources and Environment. Experience of updating land use information for the purpose of modelling wind resources over Thailand was reported by Phan (2014). The measurement data is essential to the validation work required in Phase 3. At present there a number of options to develop further the availability of high quality measurement data with a broad coverage of the country. These options and recommendations are detailed in Section D. An increased availability and geographic distribution of measurement data will add value to the final wind atlas in Phase 3, as 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. 8 Conclusions This report has described the phase 1 interim mesoscale wind modelling for Vietnam. 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 simulated and generalized wind maps, and in the form of generalized wind climate data files. A prelim- inary verification is reported for three sites with different settings. Modelled and measured wind direction frequency distributions, wind speed diurnal and annual variations have been compared. There are indications of nocturnal low level jets occurring which can be important for wind resources of the country. So far the most suitable measurement data has not been available to verify the nocturnal low level jet phenomena and the indicated higher wind re- source areas in Vietnam. Recommendation for approaches to increase the availability of the high quality measurement data for the project have been given. 24 REFERENCES REFERENCES References Baas, P., 2009: Turbulence and low-level jets in the stable boundary layer. Wageningen University. 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. Blackadar, A., 1957: Boundary layer wind maxima and their significance for the growth of th nocturnal inversions. Bull. Amer. Meteor. Soc., 38, 282–290. 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. Meteor. Clim., 49 (5), 954–972, doi:{10.1175/2009JAMC2351.1}. Hahmann, A. N., C. L. Vincent, A. Pe˜ na, J. Lange, and C. B. Hasager, 2014: Wind cli- mate estimation using WRF model output: method and model sensitivities over the sea. International Journal of Climatology, doi:10.1002/joc.4217. Horvath, K., D. Koracin, R. Vellore, J. Jiang, and R. Belu, 2012: Sub-kilometer dynamical downscaling of near-surface winds in complex terrain using WRF and MM5 mesoscale models. J. Geophys. Res., 117, D11 111, doi:DOI10.1029/2012JD017432. Kelly, M. and I. Troen, 2014: Probabilistic stability and “tall” wind profiles: theory and method for use in wind resource assessment. Wind Energy, in press. en, X. G., J. Badger, A. N. Hahmann, and N. G. Mortensen, 2013: The selective dynamical Lars´ downscaling method for extreme-wind atlases. Wind Energy, 16, 1167–1182, doi:10.1002/ we.1544. en, X. G., S. Larsen, and A. N. Hahmann, 2012: Origin of the waves in A case-study Lars´ of mesoscale spectra of wind and temperature, observed and simulated’: Lee waves from the Norwegian mountains. Q. J. R. Meteorolog. Soc., 138 (662, Part A), 274–279, doi: {10.1002/qj.916}. Mellor, G. L. and T. Yamada, 1982: Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys. and Space Phys., 20, 851–875. na, A. and A. N. Hahmann, 2012: Atmospheric stability and turbulence fluxes at Horns Rev Pe˜ — An intercomparison of sonic, bulk and WRF model data. Wind Energy, 15, 717–731, doi:DOI:10.1002/we.500. Phan, T. T., 2014: Personal communication. 25 REFERENCES REFERENCES Rife, D. L., J. O. Pinto, A. J. Monaghan, C. A. Davis, and J. R. Hannan, 2010: Global distri- bution and characteristics of diurnally varying low-level jets. Journal of Climate, 23 (19), 5041–5064. Skamarock, W. 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. Van de Wiel, B. J., A. Moene, G. Steeneveld, P. Baas, F. Bosveld, and A. Holtslag, 2010: A conceptual view on inertial oscillations and nocturnal low-level jets. Journal of the Atmo- spheric Sciences, 67 (8), 2679–2689. 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: The Wind Atlas for South Africa. [Online], http://wasa.info.org. 26 A DETAILED DESCRIPTION OF GENERALIZATION A Detailed description of generalization A.1 Basic generalization equations The generalization of WRF model winds is an extention 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 27 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 28 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 29 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 ??) in WAsP. 30 B MICROSCALE FLOW EFFECTS B Microscale flow effects B.1 Thanh Hai In this section the microscale flow effects at the three preliminary verification sites are dis- played. 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 and photographs from the site installation reports. Figure 26 – Thanh Hai 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. 31 B.2 Ea Drang B MICROSCALE FLOW EFFECTS B.2 Ea Drang Figure 27 – Ea Drang 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. 32 B.3 Hai Ninh B MICROSCALE FLOW EFFECTS B.3 Hai Ninh Figure 28 – Hai Ninh 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 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 = 1, max_dom = 3, parent_id = 0, 1, 2 34 C WRF NAMELIST parent_grid_ratio = 1, 3, 3 s_we = 1, 1, 1 e_we = 82, 118, 223 s_sn = 1, 1, 1 e_sn = 90, 157, 361 s_vert = 1, 1, 1 e_vert = 41, 41, 41 grid_id = 1, 2, 3 i_parent_start = 1, 17, 23 j_parent_start = 1, 16, 16 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, 35 C WRF NAMELIST num_soil_layers = 4, 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 = 264, 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 = 15, 0, 0 if_zfac_t = 1, 0, 0 k_zfac_t = 15, 0, 0 if_zfac_q = 1, 0, 0 k_zfac_q = 15, 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 36 C WRF NAMELIST scalar_adv_opt = 1, 1, 1 / &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, / 37 D RECOMMENDATIONS D Recommendations The location of masts according to the ToR ANNEX A – both the 10 MoIT/GIZ and the historical masts incl 3 WB masts – are shown in Fig. 29. Fig. 30 shows the locations of the 10 MoIT/GIZ masts as well as the 3 historical WB masts. The locations are shown in a color coded simulated mesoscale wind speed map. It is seen that the MoIT/GIZ (as well as the WB) masts are all located in the south except for one in central Vietnam and no masts in the north. The updated list of available wind data made by MoIT and shared with the WB team at a meeting 23 June 2014 shows some additional mast locations as shown in Fig. 31. The conversion of the coordinates giving the locations to a Google Earth kml file is due to Mathias Hoelzer. The availability, type and quality of data from any stations other than the MoIT/GIZ is yet to be explored further. D.1 Review of MoIT/GIZ station reports The measurement stations for which reports (not yet data) have been made available are the 10 MoIT/GIZ stations. The review of these reports concludes that they are good measurements according to international standards and recommends: • Reliable wind data, suitable for wind power planning and wind power project develop- ment has been provided • Wind measurements from three sample sites covering approximately one full year has been reviewed • High quality wind measurements with low uncertainties are made by following the in- ternational standard IEC 61400-12-1 • By now, wind measurements covering at least two full years should be available from all wind measurement stations • Some deviations to IEC 61400-12-1, e.g. a) Only one wind vane on each wind mea- surement station b) Documentation missing of wind vane north mark orientation • It can be compared to 2001 and 2009 WB atlas mean wind speeds, which suggests that the atlases made are probably not very accurate since the measured mean wind speeds are significantly different to WB atlas mean wind speeds - one site is significantly higher another site is significantly lower It is recommended that • All available raw data (without filtering and corrections) from all wind measurement stations should be collected and considered for further analyses • Correct calibrations and possible wind direction offsets should be applied to all raw data 38 D.1 Review of MoIT/GIZ station reports D RECOMMENDATIONS Figure 29 – The location of masts – both the 10 MoIT/GIZ and the historical masts incl 3 WB masts – shown in two different maps in Google Earth a) topography and b) simulated mesoscale wind speed output. • All calibrated data from all wind measurement stations should be carefully filtered to avoid bias due to erroneous data • One or preferably two full years of correctly calibrated and carefully filtered data from each wind measurement station should be applied to avoid bias due to seasonal variations • Long-term correction should be applied using accurate, representative and reliable long- term reference data to avoid bias due to long-term variations The siting of these 10 MoIT/GIZ stations has been performed according to the criteria well described and explained by its site selection report. It is carefully carried out in accordance with those objectives and the methodology described and decided for that MoIT/GIZ wind measurement project that was focused on assessing the selected areas around the masts and not intended for verification of a numerical wind atlas. However, for a state-of-the-art wind atlas project, the siting methodology applied for those 10 MoIT/GIZ stations does not match the recommendations normally applied for siting of measurements to be used for the verification of a numerical wind atlas. For verification of a numerical wind atlas it is recommended to include the following criteria for siting of masts, which also should apply for verification of a wind atlas for Vietnam: • Spaced and spread fairly equally across the entire project area – in this case all Vietnam • Where possible at a reasonable distance from complex terrain gradients, i.e. terrain slopes larger than 30◦ - ideally further than 5-10km away 39 D.1 Review of MoIT/GIZ station reports D RECOMMENDATIONS Figure 30 – The locations of the 10 MoIT/GIZ masts as well as the 3 historical WB masts • At least one mesoscale grid cell diagonally ( 5-7km) away from the coast to ensure 100% land within grid cell • Areas that are fairly uniform within a single mesoscale grid cell in terms of roughness and topography to ensure grid cell overlay - take two grid lengths (5-10km) • Sites should cover the spectrum of different climatological regions - coastal, inland low lying, inland high lying (latter are areas that prove challenging to models). Ideally sites that are not too far away from each other but are situated in different climatological regions. • Sites are needed on interesting large scale terrain that has significant mesoscale forcing • Some sites should be in areas of reasonably good wind climate according to mesoscale maps • Sites should be of reasonably/sufficiently low terrain complexity – e.g. measured as the WAsP RIX number – to allow microscale modelling and generalisation of simulated wind speed The site selection made for the MoIT/GIZ measurement programme does not include these considerations and most of the sites selected are not ideal for the purpose of the verification of mesoscale modelling/numerical wind atlas. 40 D.2 Need for addition to measurements D RECOMMENDATIONS Figure 31 – Location of wind measurement masts known in Vietnam – updated by MoIT June 2014. D.2 Need for addition to measurements Due to the quality of the MoIT/GIZ measurements, the data will most probably be very useful for verification of the mesoscale modelling/numerical wind atlas in the areas and climate zones that they are representative for – even if they are not sited ideally. It will be necessary to carefully consider the influence of local terrain complexities, coastlines and slopes on the quality of the verification, which only can be done when granted access to the actual data and all digital terrain information. If this is made available to DTU in the form of a WAsP Workspace file, such an assessment can quickly be made for each mast, otherwise it requires significant extra work that will need a separate budget. Even more importantly, no station is located in the mountain range along the border to Laos, which is a large area that indicates an area with potentially a large wind resource in the preliminary mesoscale modelling. This has also been shown in other previous studies. No project partner is aware of any measurements performed in this region that seemingly has a special local climate and that would be important for understanding the ability of the mesoscale modelling to model accurately the wind in central Vietnam. The ability of the mesoscale model to realistically simulate the flow in this central region of Vietnam with its possibly very special climate, including nocturnal low-level jets summer and winter predicted by the mesoscale model, may affect the quality of any mesoscale modelling of wind resources in the entire region and thus all of Vietnam. As presented during 23-25 June 2014 at the workshops and training, it is generally expected that the climate in the region could be one of the places in the world that has significant nocturnal low-level jet activity, which possibly could cause high wind speeds in the mountain regions in the south and central regions of Vietnam. However, it is not possible to determine 41 D.3 Recommendations for location of masts D RECOMMENDATIONS its magnitude and exact placement in time and space purely from modelling, so measurements to verify the modelling will be essential to either confirm or reject this hypothesis that it has a great impact on the wind resource available. Such measurements should in central Vietnam preferably be located in the mountain range on the border with Laos. It is furthermore foreseen that it could potentially be of importance to measure wind at heights above ground level as high as possible – preferably several hundred meters, since such a low-level jet as simulated has its maximum wind speeds in about 200-600 m agl. This should, according to the best available knowledge, be the case in the flat plain in Laos (and across the sea in winter). Mixing down of momentum to lower levels could be created as the flow crosses the mountain range (both from southwest and from northeast) and it could therefore cause high wind speeds to occur much nearer to the ground in the mountain range and/or generate gap flows through mountain gaps. This is possibly a mechanism that provides significant contribution to the high wind resources mesoscale models simulate in those mountain ranges, and therefore something that could be essential to validate. Measurements up to 300 m agl can today be made with industry standard LIDAR tech- nology available and much used by the wind industry worldwide. It is therefore recommended (if at all possible) to develop a measurement programme that combines both tall long-term mast measurements and short-term campaign measurements with a LIDAR that can be moved between locations and calibrated at one of the high quality masts. D.3 Recommendations for location of masts In addition to the LIDAR it seems recommendable to add 6 masts similar to the MoIT/GIZ 80 m measurement masts in order to cover all climate zones and the geographical areas of all Vietnam sufficiently for verification of a Wind Atlas for Vietnam. This number could be lower if it turns out that one or more other masts (historical or preferably with ongoing measurements) have data that can be used for the purpose of verifying the numerical wind atlas for Vietnam or if other data exist that is not known to the project partners. The possible locations of such 6 masts are roughly indicated in Fig. 32. Please consider this a very preliminary suggestion. A more thorough siting exercise is necessary. It is further recommended to acquire wind data and the associated detailed information about the measurement programmes from the following stations (for their location see Fig. 31): Table 4 – Wind measurement stations from which it is recommended to acquire data (in addition to the data from the 10 MoIT/GIZ stations and the 3 WB stations). Region Station Southwest Hon Chong North (coastal) Ky Anh, Sam Son, Hai Hau, Quan Lan, Mong Cai Northest Tay Trang Offshore/islands Ly Son, Bac Dao, Nam Dao, Con Dao 42 D.3 Recommendations for location of masts D RECOMMENDATIONS Figure 32 – Preliminary suggestion for 6 additional 80 m masts for verification of Wind Atlas for Vietnam It is not expected that any other data can improve the data availability for verification of the numerical wind atlas when data is already available from the 10 MoIT/GIZ stations and the 3 WB stations. As it appears (from looking at Figs 31, 32 and Table 4), none of the stations listed in Table 4 will replace the need for additional measurements M2, M3 and M6 of Fig. 32 and it still also a recommendation to seek verification at high heights above ground level of the low-level jet using a LIDAR . Finally, it is possible that savings on the budget are possible if high quality data can be made available from the locations around the suggested M1, M4 and M5. It is therefore suggested to get access to station description reports from all stations mentioned in Table 4 as quickly as possible (for Offshore/island stations it is not urgent). The decision of what data to acquire and the further discussion whether verification masts M1, M4 and M5 are needed can then be made on a more informed basis. 43