At present, with the development of wind power project in China, there are more and more projects located at the complex terrain and complex environment. At the same time, since the large planned area of project, the complex mountain area, and limited number of met mast, even without met mast, in order to the reliable development of the wind power project, it is important that how to do the wind resource assessment without actual measurement wind data and other conditions such as less reliable wind data, and the met mast was not considered representative. This paper will use the atmospheric model to do mesoscale simulation calculation of wind resources, and then combine with CFD technology to downscaling computation to get high resolution wind power assessment result. Finally, in order to confirm the validity of this application in the actual project, the comparison between calculation values and measurement values is carried out. The verification result through the actual data of different met mast shows that the wind resource assessment method which combines the CFD and mesoscale technologies is reliable. The main contribution of the article is to provide the reference model and approach for regional planning and large scale wind resource assessment when there isn’t enough adequate and effective wind data.

1. HIGH RESOLUTION WIND RESOURCE ASSESSMENT METHOD
BASED ON COUPLING MESO-SCALE MODELING WITH CFD
TECHNOLOGY
YIN Jianguang, FU Bin
GUO DIAN UNITED POWER TECHNOLOGY CO.,LTD METEODYN
yinjianguang@gdupc.cn fu.bin@meteodyn.com
Abstract
At present, with the development of wind power project in China, there are more and
more projects located at the complex terrain and complex environment. At the same time,
since the large planned area of project, the complex mountain area, and limited number of met
mast, even without met mast, in order to the reliable development of the wind power project,
it is important that how to do the wind resource assessment without actual measurement wind
data and other conditions such as less reliable wind data, and the met mast
was not considered representative. This paper will use the atmospheric model to do mesoscale
simulation calculation of wind resources, and then combine with CFD technology to
downscaling computation to get high resolution wind power assessment result. Finally, in
order to confirm the validity of this application in the actual project, the comparison between
calculation values and measurement values is carried out. The verification result through the
actual data of different met mast shows that the wind resource assessment method which
combines the CFD and mesoscale technology is reliable. The main contribution of the article
is to provide the reference model and approach for regional planning and large scale wind
resource assessment when there isn’t enough adequate and effective wind data.
Keywords: wind resource assessment, CFD, mesoscale model

2. 1. The mesoscale technology based on atmospheric model
Since the 80s of last century, the mesoscale simulation technology based on the
atmospheric numerical model has been developing for a long time. From the last century in
the 90's, some mesoscale simulation systems have been very advanced and be used all over
the world. With the progress of computer science, the mesoscale simulation platform based on
the atmospheric model has entered the stage of practical operation. At present, there are some
main systems such as the European Centre for Medium-Range Weather Forecasts (ECMWF),
the American NCEP model, Japan Meteorological Agency model, and so on.
Major EU countries constituted the ECMWF model in 1976, established the
global medium range numerical weather prediction system and formally put into operation
from 1979. Until now, the ECMWF can provide the result of global simulation calculation
with very high-resolution. Data assimilation adopted the most popular the four-dimensional
variational technology currently to form model analysis and the initial conditions.
In the early 1980s American establishes the global regional assimilation and prediction
system, and In the 1990s NCEP realized three-dimensional variational assimilation. As a
consequence, a large number of satellite data can be used in numerical weather prediction so
that improve the quality of the analysis and prediction.
The Japan Meteorological Agency has two models, global spectral model and the Far
East regional spectral model. The global spectral model is equivalent to 60km as the
horizontal resolution and 40 layers on the vertical dimension.
The macro wind resource assessment based on mesoscale technology has been widely
used to the planning in the earlier stage of wind power projects, especially in the area that
lack of wind measurement data, or difficult to measure the wind speed and direction such as
open-sea area. Through mesoscale simulation technology, we can get regional meteorology
elements information quickly, not only can get the wind resource information, but also can
obtain the information of precipitation, temperature, humidity and snowfall in one region.
These meteorology elements information are particularly important during the wind power
project construction, the environmental assessment and post-operational phase.
2. Apply the mesoscale and CFD technologies to do downscaling
simulation calculation and analysis
Generally the resolution of the mesoscale simulation isn’t fine, set at “km” level. For
instance, the distribution trends of wind resources are enough for macro perspectives such as
the whole national and provincial region. However, for the micro-planning of wind power
projects, the locations of projects are always in complex mountainous regions, this requires
higher resolution mapping of the wind resource to meet the requirements of developers and
wind resources engineer. For such reason, the CFD technology will be used as the
downscaling method for mesoscale simulation, which combines the two models. The purpose
of downscaling process includes to get higher resolution mapping of wind resource in the
absence of wind measurement data, to do the preparation work in an effective and efficient
way, to supplement and reference to the existing data, to compensate or correlatively analyze

3. for the lack of data. The downscaling simulation process of the different models is shown in
the figure1.
Figure 1. From the global model-Mesoscale simulation-CFD micro-modeling
3. Case Study
This paper studies a practical project located in a mountainous area of Yunnan province
in China. In the planning area of the project, the highest altitude is 3274 meters, the lowest
altitude is 1422 meters, and it has a falling head of 1852 meters and belongs to a typical
complex mountain project. The area is very large, but the number of met mast is limited.
There will be subsequent projects to be developed but existing wind masts are far away from
the subsequent projects. Through the actual project case, the difference between the actual
measurement values and downscaling simulation values can be investigated further. It will
guide the similar actual projects development in the future: make up the wind measurement
data, to analysis the representative of the wind measurement data, to encrypt the wind data
which cooperate with existing wind masts, to install the met mast at more representative
position, etc.
The four met masts of the case project are 7101, 7103, 7105, 7106 along the ridge from
south to north. The height is 70 meters for each met mast, the period of wind measurement is
from July 20, 2009 to June 12, 2010. Due to communication problems, the data is missing
from September 30 to October 6 in 2009 and from March 25 to April 13 in 2010 during the
measuring period. The rest of days have integral and high quality measurement data. And
these data are suitable for comparative analysis with the values calculated by downscaling
simulation.

4. Figure 2. Illustrates the position of Wind masts in Google earth
The Distance between the four wind-masts is shown in the table below:
(The units are in meters)
M7101 M7103 M7105 M7106
M7101 0
M7103 5650m 0
M7105 9700m 4080m 0
M7106 11620m 5970m 1960m 0
Table1. The distance between the four wind-masts
4. Modeling description
In this paper, the mesoscale simulation is based on WRF-ARW core. At first, the
re-analysis data will be inputted to the simulation system as boundary conditions in the
mesoscale simulation. And then the project area and calculation resolution need to be
specified. In this study the mesoscale simulation resolution is 3 kilometers, the whole
mesoscale simulation area is 300km*150km and generates the hourly time serial data for the
same period with met masts (2009.7.20-2010.6.12). The hourly time serial meso wind data is
extracted based on the location of each met mast. And then the extracted meso data is loaded
into Meteodyn WT to make the downscaling computation.
The parameters of CFD modeling in Meteodyn WT: the computation radius of project is
9900 meters, the length and width of meso cells defined in WT are 3000 meters during the
downscaling simulation calculation because of the meos data resolution is 3 kilometers. The
speed-up factors within the scope of meso cells can be uniformly processed to build
a relationship with the entire 70m height interesting zone and interesting points. And then the
50m resolution wind map is computed by WT through downscaling simulation. In the CFD
simulate process, the minimum horizontal resolution of mesh is 50 meters, the minimum

5. vertical resolution of mesh is 6 meters, the horizontal expansion coefficient is 1.1, the vertical
expansion coefficient is 1.2, the verticality parameter keeps the default value 0.7, the
parameter of smoothing data on whole domain also keeps the default value 1. The orography
file used in Meteodyn WT is from ASTER database data, and the roughness file is from
roughness database-UCL. In the whole domain, the max roughness value is 0.6.
In the following figures, the red rectangles represent the four defined meso cells and the
orography and roughness information of entrie project.
Figure 3. The four different meso cells defined in WT
There are sixteen sectors to simulate wind flow fields in the step of the directional
computation. According to the actual measurement wind data and analysis, the
prevailing wind direction is concentrated here, southwesterly winds. Therefore we added 250
degree directional computation in the sector of the prevailing wind direction in order to get
more accurate results in this sector. In each directional sector the number of grid is around 6
million.

6. Figure 4. Directional computation result- speed up factor in whole domain in 247 degree
Figure 5. Directional computation result- speed up factor in 247 degree
The period of meso data is same with the measurement wind data, the
following table provides the comparative analysis between the actual measurement values and
the downscaling simulation calculation values.
Met Mast
-7101
Met Mast
-7103
Met Mast
-7105
Met Mast
-7106
The measured wind speed value 10.04m/s 8.87m/s 9.52m/s 9.75m/s
The downscaling simulation value 10.01m/s 8.42m/s 9.59m/s 9.25m/s
The percentage error of wind speed % -0.3% -5.07% 0.73% -5.13%
Table 2. The error comparison between the result of downscaling simulation
and the actual measurement
Figure 6. The wind-rose of the actual measurement
(From left to right: 7101, 7103, 7105, 7106)

7. Figure 7. The wind-rose diagrams of the result of downscaling simulation
(From left to right: 7101, 7103, 7105, 7106)
The simulated results and the actual measurement have the same
prevailing wind direction. It can meet the requirements of the wind resource preliminary
assessment for large region. The maximum error of the simulated wind speed is 5.23%
in the whole computational domain, and the minimum error is just -0.3%.
Meanwhile, in Meteodyn WT we do multi meso cells downscaling synthesis computation
to generate wind resource mapping of the project. The following figures display the
comparison between wind resource mappings of multi-met mast synthesis computation and
multi-mesocells synthesessis computation. It shows that the entire trend is consistent and is a
meaningful reference method in practical projects.
Figure 8. The left wind resource mapping based on multi-mast synthesis computation, and the
right one based on multi-mesoscale cells downscaling synthesis computation
5. Conclusion
According to the simulation results for the practical project, it shows that the
downscaling method which combines the high-resolution meso data and CFD technology is
suitable under the condition of complex topography and allows getting more accurate wind
power assessment over large area. It also provides references and the useful guide for met
mast location selection, data compensation, increasing the representative of wind data.
In addition, it is helpful for the project investment and return analysis, and to ensure the

8. reliability of the project development.
In the future, the study will apply the finer resolution mesoscale data and re-compare
with the actual measurement in order to find the suitable mesoscale resolution in the complex
terrain and increase operational efficiency of the whole simulation
computation for future work. During downscaling simulation the different
atmospheric thermal stabilities will be considered in the future study.
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