Urban Wind Map for Delft, Rotterdam and Zoetermeer

Urban Wind Map for Delft,
Rotterdam and Zoetermeer
Fikirte M. Yemer B.Sc.
12-12-2010
Urban Wind Map for Delft,
Rotterdam and Zoetermeer
Fikirte M. Yemer B.Sc.
Delft University of Technology
Faculty of Applied Science
Department of Sustainable Energy Technology
DELFT UNIVERSITY OF TECHNOLOGY
DEPARTMENT OF WIND ENERGY
The undersigned hereby certify that they have read and recommend to the faculty of Applied
science sustainable energy department for acceptance a thesis entitled “ Urban Wind Map for
the Delft, Rotterdam and Zoetermeer’’ by Fikirte M. Yemer B.Sc. in partial fulfilment of the
requirements for the degree of Master of Science.
Date ________________
Head of Department and Supervisor
_____________________________
Prof. dr. G.J.W Van Bussel
Reader
_______________________________
dr. Ir. Wim A. A. M. Bierbooms
Reader
_______________________________
dr. Hans Zoetelief
SUMMARY
Urban wind turbines fall within the scope of the green energy goal of municipalities in the
Netherlands. For feasibility and potential green energy contribution assessment of these
turbines, realistic wind speed prediction method is essential.
The objective of this thesis is to develop urban wind resource mapping methodology and apply
it to Delft, Rotterdam, and Zoetermeer. A non-conventional application of WASP is adapted by
treating urban areas as a complex environment. As a topographic map, a Digital surface model
that evolves above individual buildings which is then refined to a smooth synthetic surface
evolving above cluster of buildings is used. Kriging interpolation and a combination of
maximum and moving average grid filtering methods are used to create the synthetic surface.
This smooth synthetic surface has lower ruggedness index around the meteorological sites,
which indicates a gentle and smooth terrain that is relatively within the WASP working
envelop.
Roughness change maps generated by „wasp_map_exe’ and the wind data of Geulhaven and
Zestienhoven meteorological stations, collected from the KNMI website are used in this work.
The roughness change maps are modified to take into account the standardized potential wind
speed of the meteorological sites. Annual mean wind speeds of 5.71 m/s and 4.71 m/s are
registered for Geulhaven and Zestienhoven stations respectively. For both meteorological sites
total frequency greater than 30 % is registered for south and southwest directions.
Minimum prediction error is observed while using Geulhaven as a source than Zestienhoven.
However Zestienhoven predicted the wind speed for Rotterdam with very low percentage error.
Furthermore, the wind speed frequency of each predicted site has comparable distributions to
that of the respective predictor. A 3.5% difference is seen among the omni directional predicted
wind speeds of Rotterdam Noord that are based on the two meteorological sites. The sector
wise wind speed differences range from 0.69% to 23%. Frequency difference among predictor
sites, Ruggedness index and distance between the predictor and predicted sites contribute to the
observed prediction difference.Wind resource maps are developed for the cities of Delft,
Rotterdam, and Zoetermeer. The developed resource maps show a logical trend i.e. lower wind
speed in the urbanized areas and higher wind speed in areas of low building density.
Nonetheless, relatively higher wind speeds are observed at highly elevated locations within the
urban areas. These areas have a good potential for installation of UWTs.
It is recommended to use a raw wind speed data so that the introduction of error while
modifying the roughness length can be reduced. Introduction of number of masts is very
advantageous in refining the input data and validating the wind mapping methodology.
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ACKNOWLEDGEMENT
I would like to extend my sincere gratitude to my supervisor Prof. Dr. Gerard van Bussel for
his invaluable support during the thesis work. I would like to thank Robert Schneider (CEO)
for the opportunity to work with Donqi Urban Windmills.
I would like to acknowledge the stuff members of Map Room of the TUDelft Library for their
prompt supply of data; Kasper van Der Heiden, Paul Ten Hoppen, and Tamiru W. Shire for
their feedbacks and inputs. I would like to thank the stuff members of the Rotterdam Noord
police station for their assistance during my visit to the station.
I would also like to thank Patricia Carrion Gordon and the academic counsellor Mirjam van der
Geur for their assistance during the last months of my study.
My special thanks go to Mesi, Fethea, Edi, and G.J. Wiersma for their hospitality during my
stay in Delft. G5 it was great to share all the wonderful memories.
Finally yet importantly, I would like to praise my mother for her love care and support. She has
been an inspiration throughout my life. Above all, I wish to thank the Almighty God for
holding me up and giving me the strength to finalize my study with a good sprit.
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TABLE OF CONTENTS
SUMMARY ....................................................................................................................................... i
ACKNOWLEDGEMENT............................................................................................................... iii
TABLE OF CONTENTS ................................................................................................................. v
LIST OF FIGURES .......................................................................................................................... ix
LIST OF TABLES ........................................................................................................................ xiii
ABBREVIATIONS ......................................................................................................................... xv
1.
INTRODUCTION ..................................................................................................................... 1
1.1.
2.
1.1.1.
Small Wind Turbines in the Netherlands .................................................................... 2
1.1.2.
Existing wind Atlases of the Netherlands ................................................................... 3
1.2.
Problem Definition ............................................................................................................. 3
1.3.
Objective and Scope ........................................................................................................... 3
1.4.
Approach............................................................................................................................. 4
1.5.
Thesis Outline ..................................................................................................................... 4
OVERVIEW .............................................................................................................................. 5
2.1.
Basics of Wind Resource Estimation ................................................................................. 5
2.2.
Urban Wind Property .......................................................................................................... 8
2.3.
Wind Study Methods ........................................................................................................ 11
2.3.1.
Use of Onsite Measurement ...................................................................................... 11
2.3.2.
Physical Model .......................................................................................................... 12
2.3.3.
Numerical Model ....................................................................................................... 12
2.3.4.
Coupled Meso-scale and Micro-scale Modelling ...................................................... 14
2.4.
3.
Background ......................................................................................................................... 1
Conclusion ........................................................................................................................ 15
WASP....................................................................................................................................... 17
3.1.
Introduction....................................................................................................................... 17
3.2.
WASP Sub-Models ........................................................................................................... 19
v
4.
3.3.
Factors affecting the prediction process........................................................................ 24
3.4.
Ruggedness Index (RIX)............................................................................................... 25
SYNTHETIC DIGITAL SURFACE MODEL ....................................................................... 27
4.1.
Introduction....................................................................................................................... 27
4.1.1.
4.1.1.1.
4.1.2.
5.
Steps of kriging interpolation ................................................................................ 29
Grid Filtering ............................................................................................................. 30
4.2.
DSM Application Software .............................................................................................. 31
4.3.
Elevation Data .................................................................................................................. 31
4.4.
Construction of a synthetic surface above the urban area ................................................ 34
WIND SPEED DATA ............................................................................................................. 43
5.1.
6.
Kriging Interpolation ................................................................................................. 28
Historical Wind Data ........................................................................................................ 43
5.1.1.
Station 344: Zestienhoven ......................................................................................... 44
5.1.2.
Station 343 Rotterdam Geulhaven ............................................................................ 45
5.1.3.
Comparison of Zestienhoven and Geulhaven ........................................................... 46
5.2.
Short Term Wind Data...................................................................................................... 48
5.3.
Wind Speed Correlation ................................................................................................... 50
APPLICATION OF WASP ANALYSIS ................................................................................ 51
6.1.
Cross prediction ................................................................................................................ 51
Summary of the Cross Prediction ............................................................................................ 64
7.
6.3.
Ruggedness index ............................................................................................................. 67
6.4.
Effect of Contour level ..................................................................................................... 67
RESULTS AND DISCUSSIONS ........................................................................................... 69
Introduction ................................................................................................................................. 69
Rotterdam .................................................................................................................................... 69
Delft ............................................................................................................................................. 71
Zoetermeer................................................................................................................................... 73
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8.
CONCLUSIONS AND RECOMMENDATIONS .................................................................. 75
8.1.
Conclusion ........................................................................................................................ 75
8.2.
Recommendation .............................................................................................................. 76
REFERENCES ................................................................................................................................ 77
APPENDICES ................................................................................................................................. 81
Appendix A ............................................................................................................................. 81
Appendix B.............................................................................................................................. 82
Appendix C.............................................................................................................................. 86
Appendix D ............................................................................................................................. 87
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LIST OF FIGURES
Figure 1: Diffuser augmented Donqi wind turbine[5]....................................................................... 2
Figure 2: Neutral Atmospheric Boundary layer shear Profile[10] .................................................... 5
Figure 3: Step change in surface roughness incorporates a changing boundary layer [15] .............. 8
Figure 4 : Results of a CFD calculation of a rectangular building in skewed flow[16] .................... 9
Figure 5: Urban wind regimes showing disturbed regions around simple buildings[18] ................. 9
Figure 6 : vortex formation (left) and 3D vortex along the bordering buildings (right) [15] ......... 10
Figure 7 : Funnelling of parallel approaching wind in a canyon [15] ............................................. 10
Figure 8: Setup to generate an atmospheric Boundary layer in a wind tunnel[15] ......................... 12
Figure 9 : Alaiz test site used for comparison of WASP and FLUENT[26] ................................... 14
Figure 10: The wind Atlas methodology [31] ................................................................................. 18
Figure 11: The Zooming grid of BZ flow model ............................................................................ 20
Figure 12: Ruggedness index of area around Rotterdam Noord (the thick red Points inside the
circle indicate RIX greater than 0.3) ............................................................................................... 25
Figure 13: Difference between DEM and DSM [39] ...................................................................... 27
Figure 14: Variogram Model for Sample Data with scale of 6000 and length of 480 .................... 30
Figure 15: A 3 by 3-filter size grid showing the neighbouring (green) and corresponding (red) grid
nodes ................................................................................................................................................ 31
Figure 16: Location of the Netherlands in RD coordinate [54] ...................................................... 33
Figure 17: AHN data cell representation on RD coordinate system ............................................... 34
Figure 18: DSM (Top) and shaded Relief map (Bottom) of the grid where kriging interpolation is
applied ............................................................................................................................................. 36
Figure 19: DSM of filtered grid (Maximum filter size 5 by 5) ....................................................... 37
Figure 20: 3D model of the double filtered grid ............................................................................. 38
Figure 21: Overlaid 3D surface maps of double filtered (grey) and single filterd grid(red) ........... 39
Figure 22: 3D surface of the final Grid ........................................................................................... 39
Figure 23: Elevation contour of the final grid (synthetic surface) .................................................. 40
Figure 24 : Observed Wind Climate of Zestienhoven ..................................................................... 44
Figure 25: Observed Wind Climate of Geulhaven .......................................................................... 45
Figure 26: Sector wise wind speed frequencies of the KNMI stations ........................................... 47
Figure 27: Blockage effect of the neighbouring areas for Geulhaven [Google earth] .................... 47
Figure 28: Old measurement setup (left) and new measurement setup (right) of Rotterdam Noord
Police station ................................................................................................................................... 48
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Figure 29 : Observed Wind Climate of Rotterdam Noord Police station ....................................... 49
Figure 30: AHN data used for creating the DSM used in cross prediction ..................................... 51
Figure 31: Wind Atlas for R-class 0 at 10 m ................................................................................... 52
Figure 32: Wind atlas trend lines (wind shear) for different roughness classes.............................. 53
Figure 33: Self-Predicted wind climate of Zestienhoven ................................................................ 54
Figure 34 : Wind Speed Frequency difference ................................................................................ 56
Figure 35: Frequency difference of Observed and Predicted wind climate of Geulhaven and the
Predictor site Zestienhoven ............................................................................................................. 56
Figure 36 : Wind Speed Frequency difference of observed and predicted wind climate of
Rotterdam Noord station ................................................................................................................. 58
Figure 37: Frequency distributions of observed, predicted wind climates of Rotterdam Noord and
Zestienhoven (predictor) ................................................................................................................. 58
Figure 38: wind Atlas of Station Geulhaven for R-class 0 and elevation 10 m .............................. 59
Figure 39: Self predicted wind speed of Geulhaven ....................................................................... 60
Figure 40: Frequency difference of predicted and observed wind climate of Zestienhoven .......... 60
Figure 41: Frequency distributions of observed and predicted wind climate of Zestienhoven and
the predictor site .............................................................................................................................. 61
Figure 42: Frequency difference predicted and OWC (Rotterdam Noord Police station) .............. 63
Figure 43: Frequency distribution of Observed and predicted wind climate of Rotterdam Noord
and the Predictor site Geulhaven ..................................................................................................... 63
Figure 44: Relation between the prediction differences and Frequency difference of the predicted
sites .................................................................................................................................................. 66
Figure 45: Wind speed prediction error versus
.................................................................... 67
Figure 46 : Variation of omni directional prediction error with contour level................................ 68
Figure 47: Effect of contour level on the Sector wise predictions (Rotterdam Noord) using
Geulhaven as predictor .................................................................................................................... 68
Figure 48: Wind map for part of Rotterdam.................................................................................... 70
Figure 49 : Shaded relief map of part of Rotterdam for which the wind map was developed ........ 70
Figure 50: 25 m resolution wind map for Part of Delft .................................................................. 71
Figure 51: Shaded Relief map of part delft for which the wind map was developed ..................... 72
Figure 52:25 m resolution wind map for part of Zoetermeer .......................................................... 73
Figure 53: shaded relief map for the area on which the map was developed .................................. 74
Figure 54 : Synthetic surface above area of Rotterdam .................................................................. 82
Figure 55: Snap shot of Rotterdam [source Google earth] .............................................................. 83
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Figure 56 : Synthetic surface evolving above area of Delft ............................................................ 84
Figure 57: Snap shot of Delft [Google Earth] ................................................................................. 84
Figure 58:3D synthetic surface evolving above the areas of Zoetermeer ....................................... 85
Figure 59: Snap shot of Zoetermeer [Google earth]........................................................................ 85
Figure 60 : A 100 m resolution Wind map for Rotterdam .............................................................. 88
Figure 61: A 25 m resolution Wind map of Rotterdam 1 ............................................................... 89
Figure 62: A 25 m resolution Wind map for Rotterdam 2 .............................................................. 90
Figure 63 : A 25 m resolution Wind map for Rotterdam 3 ............................................................ 91
Figure 64: A 25 m resolution Wind map for Rotterdam 4 .............................................................. 92
Figure 65 : A 25 m Resolution wind map of Zoetermeer ............................................................... 93
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LIST OF TABLES
Table 1: Standard roughness class used in WASP [31] ................................................................... 22
Table 2 : Roughness length values depending on density of package and height of buildings[17] 23
Table 3: AHN data sets used for data Request ................................................................................ 32
Table 4: Summary of Ruggedness Index for Geulhaven and Zestienhoven ................................... 41
Table 5: Observed wind climate comparison of the two KNMI stations ........................................ 46
Table 6: Summary of the Observed Wind Climates........................................................................ 49
Table 7: Zero time lag Correlation between the KNMI stations ..................................................... 50
Table 8: Summary of wind atlas of Zestienhoven for different elevations and roughness classes . 53
Table 9: Difference between predicted and observed wind Climates of Geulhaven ...................... 55
Table 10: Difference between predicted and observed wind climates of Rotterdam Noord Police
Station.............................................................................................................................................. 57
Table 11 : Summary of Wind atlas data for Geulhaven .................................................................. 59
Table 12 : Difference between predicted and observed wind climate of Zestienhoven.................. 61
Table 13: Difference between predicted and observed wind climate of Rotterdam Noord station 62
Table 14: Cross prediction results ................................................................................................... 64
Table 15: Summary of statistics of cross predictions ...................................................................... 64
Table 16: Prediction similarity in using Geulhaven and Zestienhoven as predictor ....................... 65
Table 17 : Land-use and roughness classes in LGN3+ used by „wasp_map.exe‟........................... 86
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xiv
ABBREVIATIONS
ABL: Atmospheric Boundary Layer
AHN: Actual Height of the Netherlands
AMS: American Meteorology Society
BLUE: Best linear unbiased estimator
CFD: Computational Fluid Dynamics
DSM: Digital Surface Model
DXF: Drawing Exchange Format
EFRO (ERDF): Europees Fonds Voor Regionale Ontwikkeling (European Regional Development fund)
EIA: Energy Investment Deduction
IBL: Internal Boundary layer
KAMM: Karlsruhe Atmospheric Mesoscale Model
KNMI: Koninklijk Nederlands Meteorologisch Instituut (Royal Netherlands Meteorological Institute)
LIDAR: Light Detection and Ranging
MCP: Measure Correlate and Predict
MEP: Milieukwaliteit Van de Elektricites Productie (Electricity generation Environment Quality)
OWC: Observed Wind climate
P.E: percentage Error
PBL: Planetary Boundary Layer
PWC: Predicted Wind Climate
RADAR: Radio Detection and Ranging
RANS: Reynolds Averaged Navier Stokes Equation
RD: Rijksdriehoekscoördinaten (National Triangular coordinates)
RIX: ruggedness index
SRTM: Shuttle Radar Topography Mission
TIFF: Tagged Image File Format
USGS: United States Geological Survey
UWT: Urban Wind Turbine
W.P.D.: Wind power Density
WASP: Wind Atlas Analysis and Application Program
WRF: Weather Research and Forecasting
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CHAPTER ONE
1. INTRODUCTION
This chapter gives a brief description about the application of urban wind turbines, their
history in the Netherlands and the suitability of the already existing wind maps of the
Netherlands for urban wind energy deployment. It also states the research objectives and thesis
structure.
1.1. Background
Until recently the main renewable energy sources explored in an urban environment are solar
energy and heat pumps [1]. Urban wind turbines or Small Wind Turbines are defined as turbines
that are specially designed for built in environment, and can be installed on buildings or on the
ground next to buildings [2]. The potential of these turbines was undermined by improper site
selection. The accompanying technical challenges such as excessive noise, turbine under
performance and low power density and in some extreme cases cracking of turbine blades lead to
infant mortality of Small wind turbine applications. In addition to the problems listed above,
installation of wind turbines which are not specifically designed for urban wind conditions harmed
the reputation of using wind turbines in urban areas[3].
Nevertheless, in the last few years small wind turbines that are designed to withstand the complex
wind behaviour of an urban environment began to emerge. Some of these wind turbines are even
installed in urban areas. Urban wind turbines (UWTs) have a potential to harness the wind in
cities and towns where space for installation of large wind turbines is not available or large wind
turbines can significantly damage the city outlook [2]. The introduction of UWTs can contribute
to the diversity of national power generation and renewable energy commitment at a household
level. Moreover, depending on their penetration level they may also reduce transmission losses in
the local grid [4]. Their economic viability varies from place to place and depends on the future
developments of electricity market such as government policy on subsides and international
commitment on renewable energy shares.
The Wind Energy Integration in the Urban Environment (WINEUR project) has conducted a
study on the deployability of micro wind turbines in the built-in environment [2].The study
presented the current challenges that undermine the use of micro wind turbines into the following
different aspects; turbine technology, development costs, public awareness and construction
permits.
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CHAPTER ONE
1.1.1. Small Wind Turbines in the Netherlands
The application of small wind turbines in the Netherlands gave birth when Nuon, the Dutch utility
company, presented Tulipo at the roof of the Dutch pavilion at the Hannover Fair Expo 2000.
Tulipo‟s market success inspired other producers such as Turby, Energy Ball and WindWall for a
market share which resulted in other new types of UWTs [1]. However, the design and technology
of UWTs is not yet matured and it had not been proven in the field.
DONQI Urban Windmill is one of the youngest companies that introduced new designs of UWTs
with the aim of increasing penetration level of decentralized generation. The Donqi Turbine uses
an innovative technology with built-in Venturi noise dampener, thus produces high yield while
operating safe and quiet[5].
Figure 1: Diffuser augmented Donqi wind turbine[5]
One interesting fact in the Netherlands is the lively connection existing among environmental
departments of the municipalities which allow them to share experiences about the application of
UWTs[1]. Financial supports such as Energy Investment Deduction (E.I.A) and Electricity
Generation Environment Quality (M.E.P) are made available for commercial organisations .Some
provinces and municipalities also provide other subsidies as part of their renewable energy
development programmes [2].
Despite the remarkable improvements in UWTs technology, lack of full understanding of the
wind behaviour in urban environment and its accurate prediction made UWT applications
challenging [3].
2
CHAPTER ONE
1.1.2. Existing wind Atlases of the Netherlands
The Wind Atlas of the Netherlands is included in the European wind atlas for five different
topographic conditions at an altitude of 50 m [6]. It shows the regional wind climate discarding
the influences of micro-scale topography. In 2005, KEMA Netherlands BV developed another
wind atlas for all the provinces at an elevation of 100 m [7]. It is developed to support policy
makers of large wind projects in the governments, provinces, and municipalities. The wind map
used wind data from KNMI HYDRA project, and WASP computer program. It is prepared for a
grid area of 200 m by 200 m. As the aim of this wind map is for large wind turbine developments,
only wind speeds at an elevation 80-100 m can be deduced from it.
1.2. Problem Definition
The application of UWTs necessitates Urban wind map detailed with the effect of micro-scale
features. The wind Atlases mentioned in Section 1.1.2 do not include micro-scale features of an
environment. They also have low resolution. Hence, they are not able to provide enough
information for urban wind turbine application.
1.3. Objective and Scope
Having an urban wind map will be advantageous in identifying suitable candidate sites of high
wind energy concentration to be used for further verification through measurements and for
installation of UWTs. The wind behaviour in urban environments is very complex. A large-scale
wind study of urban areas using conventional methods is very difficult. The main objective of this
project is to develop an urban wind mapping methodology and apply it to the cities of Delft,
Rotterdam, and Zoetermeer.
The work is carried on in cooperation with Donqi and TUDelft Wind Energy Section, and is
supported by the Europees Fonds Voor Regionale Ontwikkeling (EFRO) also known as European
Regional Development fund (ERDF) subsidy scheme.
3
CHAPTER ONE
1.4. Approach
In this study, a different approach of using the conventional wind resource assessment method,
WASP, is followed. It is based on the assumption that urban areas can be treated as a complex
environment in which „skimming flow‟ takes place. For doing so, a synthetic topography that
evolves above cluster of buildings and other features of the area is developed using the Actual
Height of the Netherlands (AHN) data. In the synthetic topography, higher elevation areas that
resulted from skyscrapers and high buildings are assumed as mountains and the lower elevation
areas as valleys. The effect of density and height variation of buildings is taken into account by
assigning different roughness length values. For classifying the area, Google Earth and the DSM
developed from the AHN data are used. As the blanket like synthetic surface is above buildings
and other features of an urban area, no obstacle model is used. The methodology used in this work
is adapted from a study made in Portugal, Torres Vedras city [8].
1.5. Thesis Outline
The report covers seven main sections and is organized as follows: First, Chapter two covers a
literature review about the basics of wind resource study and urban wind properties. The current
wind study methods and their applicability to complex and urban environments are also
summarized briefly. In the next section, Chapter three, the Wind atlas methodology (WASP) and
its sub models are discussed in detail. Chapter four then addresses the construction of synthetic
surface to be used as a topographic map. In Chapter five, the long term and short-term wind data
used for this study are presented. Chapter six then gives the result of WASP analysis. Chapter
seven discusses the wind maps developed for Delft, Rotterdam, and Zoetermeer. Chapter eight
then gives the conclusions and recommendations of the study.
4
CHAPTER TWO
2. OVERVIEW
Wind characteristics of a particular site can be best expressed using three key parameters;
Annual Mean wind speed, Wind frequency distribution and wind direction. This chapter
introduces atmospheric boundary layer, wind shear, and roughness in addition to the basic wind
resource characteristics aforementioned. It also discusses the different wind study methods that
are currently in use and their applicability to urban environment.
2.1. Basics of Wind Resource Estimation
While performing wind resource estimation a good knowledge about the annual mean wind speed,
frequency, and wind direction distribution are very important. Atmospheric boundary layer, Wind
shear, and Roughness length are also necessary for profound study of the wind property at a
certain site. A brief introduction to these wind properties is presented as follows.
Atmospheric Boundary Layer
Atmospheric Boundary layer (ABL) or Planetary Boundary Layer (PBL) is part of the troposphere
that is directly influenced by the presence of the earth‟s surface and responds to surface forces
within a time scale of an hour or less [9]. The depth of ABL ranges from tens of meters in strongly
statically stable situations, to several kilometres in convective conditions over deserts and varies
with atmospheric conditions and time of the day [9]. It has two main layers: Inner (surface) layer
and Outer (Ekman) layer. Coriolis forces dominate the outer layer and are negligible in the inner
layer. The inner layer constitutes about 10% of the ABL depth adjacent to the ground and is
affected by surface roughness[10].
Figure 2: Neutral Atmospheric Boundary layer shear Profile[10]
5
CHAPTER TWO
Figure 2 shows schematics of wind shear profile within the ABL. Wind shear is the horizontal
wind speed increase with elevation. It directly affects the power available at different wind turbine
hub heights and cyclic loading of the blades[11]. It depends on the wind speed, wind direction,
height above the ground, ground surface roughness variation, atmospheric stability and nature of
terrain [12]. Its dependency on these entire factors weighs up and makes wind shear modelling
difficult. Usually, it is modelled using the logarithmic wind profile which is based on principles of
boundary layer flow Equation 1; and the empirically developed power law model Equation 2. The
log law is applicable only to the inner layer of ABL.
Equation 1
Where
:
:
is the roughness length
and
are the elevations where wind speeds and
(
Where :
:
and
)
are wind speeds measured at
are measured
Equation 2
and respectively
is the power law exponent
In Equation 1, roughness length that shows the effect of roughness of a terrain on the wind profile
is included. It is defined as the height above the Zero displacement plane1 at which the mean wind
becomes zero when extrapolating the logarithmic wind speed profile downward through
the surface layer[9].
Frequency distribution (probability distribution Profile)
Probability distribution of wind speeds shows the range of wind speed and its frequency of
occurrence. The weibull probability density function, Equation 3, gives a very good representation
of wind speed frequency for many areas.
( )
Where
( )
( ⁄ )
Equation 3
( )is the probability of occurrence of wind speed „U‟
: „a‟ [m/s2] is the weibull scale factor
: is the shape factor that describes the distribution of the wind speeds (how peak
the curve is)
1
Zero-plane displacement is the height in meters above the ground at which zero wind speed is achieved because
of flow obstacles such as trees or buildings. It is generally approximated as 2/3 of the average height of the
obstacles.[9]
(2000, The American Meteorological Society's (AMS) Glossary of Meteorology (2nd ed.). Available:
http://amsglossary.allenpress.com/glossary
6
CHAPTER TWO
The weibull scale factor „a‟ and shape factor „k‟ are related to the average wind speed with,
Equation 4.
(
Where
⁄ )
Equation 4
is the mean wind speed
( ) is the gamma fnction
Sometimes sites with different summer and winter wind climates can be represented by double
peaked Weibull distribution. A special case of the probability density function called Rayleigh
distribution occurs when the shape factor k equals two. Equation 4 will then be simplified to
Equation 5.
Equation 5
Annual Mean Wind speed
Annual Mean Wind speed as the name indicates is the average wind speed of a certain site over a
year. It is useful in giving an estimate of the average available energy. Since the available energy
varies as the cube of the wind speed, a very accurate estimate of wind speed is crucial. It is worth
mentioning that sites with the same mean wind speed but dissimilar frequency distribution may
have different energy potential.
Power Density
The power density of the site is the average wind power over one square meter of a turbine; it can
be calculated using individual wind measurements Equation 6. In [13], it is indicated that for a
wind distribution with a shape factor k=2, if the estimate of the power density is done using
annual mean wind speeds underproduction of annual power density by a factor of 1.9 will be
observed.
∑
⁄
Where :
:
:
:
Equation 6
is the power density
is the number of wind records
⁄
⁄
is the ith wind speed
is the air density
7
CHAPTER TWO
Wind Direction
The other essential wind property to be studied while undertaking a wind resource assessment is
wind direction. It is represented using a wind rose, which shows the frequency of occurrence of a
wind at a certain direction. Having knowledge of the prevailing wind direction will help make an
informed decision during the installation of the wind turbine for maximum power production.
2.2. Urban Wind Property
Wind regime in the built environment is generally characterized by the low annual mean wind
speed and high turbulence intensity [14]. When wind flows from an open area to a built-up area,
the inner ABL evolves to the local building density. The wind speed decreases due to increased
roughness caused by physical obstacles Figure 3. The wind speed reduction increases when the
wind flows from sub urban areas to a more built up areas (like a city centre with high buildings).
The roughness increase also causes higher turbulence intensity and directional variability.
Figure 3: Step change in surface roughness incorporates a changing boundary layer [15]
When a wind flows around a building separation occurs and bubbles form at the leading edge and
sides of the building Figure 4. Mertens performed a CFD simulation to visualize the flow around a
single rectangular building [16]. It was found out that above the separating stream there is up to
30% increase in total wind speed. It was also seen that there is a velocity gradient above the roof.
Based on his study he also suggested some guidelines on the installation height of wind turbines
on rooftops.
When more than one building is involved, the wind property varies with type, arrangement, and
density of the buildings found in the area. As cited in [17-18] using a wind tunnel experiment
three different types of flow were demonstrated depending on the density of packing of the
obstacles Figure 5:
8
CHAPTER TWO
1. Isolated flow in which elements are far apart and act as individual wake generators,
2. Wake interference flow in which the spacing is close enough that the wakes reinforce
each other, and
3. Skimming flow in which there is high density of packing and hence the main flow skips
over the top of elements.
Figure 4 : Results of a CFD calculation of a rectangular building in skewed flow[16]
Figure 5: Urban wind regimes showing disturbed regions around simple buildings[18]
In [17] it was indicated that arrays of elements with similar heights are less „rough‟ than one with
variable heights, even when spatially averaged mean heights are the same. Moreover, buildings
that are taller than their surroundings have higher wind speed at their rooftops because the wind is
less affected by the internal urban boundary layer which is very much affected by the surface
roughness [17].
9
CHAPTER TWO
An urban canopy is the assemblage of buildings, trees, and other objects composing a town or city
and the spaces between them [9]. A canopy model has been developed to study the effect of built
up area on wind speed. By applying the model to the city of Los Angeles; it was shown that there
is an increase in wind speed at lower heights (less than 5 m) of the canopy [19].
Urban (street) canyon, which is the characteristic geometry formed by a city street and its flanking
buildings [9], is a type of an urban area in which distinct flow types could be seen depending on
the direction of the wind flow. In [15] it was explained that If the flow is perpendicular to the
street axis a vortex develops in the middle of the canyon, and a three dimensional vortex moves
along the street Figure 6. If the wind is however approaching parallel to the street canyon the
buildings funnel it Figure 7. Furthermore, high roughness is observed if the flow is normal to the
street axis than it is parallel[17].
Figure 6 : vortex formation (left) and 3D vortex along the bordering buildings (right) [15]
Figure 7 : Funnelling of parallel approaching wind in a canyon [15]
10
CHAPTER TWO
2.3. Wind Study Methods
Nowadays, most urban wind studies cover feasibility and application of UWTs besides
investigating the practical problems such as wind force on structures, pedestrian comfort, and
air pollution. In wind energy applications, overestimation of wind speed can lead to an
economic loss and underestimation may leave places of high potential undiscovered. Thus, the
need for accurate wind forecast methods is evident. Different methods have been developed
and are in use for wind resource assessments; namely, physical model, on site measurements
and Numerical models. The wind resource study methods and their applicability to complex
and urban environments are discussed in the following sections.
2.3.1. Use of Onsite Measurement
On site measurement is a method that uses measurement masts installed at the area of interest
for a certain period. The cup and Ultrasonic anemometers are the most commonly used
measuring instruments. The cup anemometers measure average speed and can be coupled with
wind vane for wind direction detection. They provide limited information on turbulence and
vertical wind component, but they are reasonably accurate and inexpensive [3]. Ultrasonic
anemometers measure instantaneous wind speed and wind direction. Unlike Cup anemometer,
it can sufficiently detect turbulence. Though it requires higher fixed cost compared to Cup
anemometer it has lower operational costs as it has no moving parts[3].
When there are no long-term wind data at a target site, Measure Correlate and Predict (MCP)
can be used to correlate the short-term measured data to long-term reference data. It
statistically correlates the short-term on-site wind measurements to a reference weather station
to obtain long-term wind data for the site[20] . For this method, three sets of data: on-site
measured wind data, concurrent measured data at the reference site, and Long-term wind data
at the reference site are needed. The concurrent data sets can be used to create the correlation
and the long-term reference data with the correlation can be used for obtaining long-term wind
data of the site.
The measuring mast can be arranged to a hub height of the proposed wind turbine to avoid
wind shear effect [14]. Wind data need to be collected at least for one full year to grasp firmly
the daily and seasonal variations. This method gives a good accuracy for resource assessment
in an installation area. Nonetheless, it may also lead to higher costs expensive than turbine
cost, due to the tedious data collection and analysis work [8]. Construction of wind maps
through extrapolation of these local data may lead to errors as the data highly depend on
roughness and topography.
11
CHAPTER TWO
2.3.2. Physical Model
Physical models use downscaled models of buildings and other features of the area and an
atmospheric boundary layer wind tunnel. The model is placed in a wind tunnel, and the
boundary layer upstream of it is created using vortex generators Figure 8. Measurements at
different points are taken and adjusted to full scale by keeping the Reynolds number constant
[15]. The main advantage of a physical model is its reproducible environment with an overall
control over the individual parameters. This allows studying the effect of variation of specific
parameters on the overall performance of the model, making it suitable for planning of urban
environment [21]. However, it is expensive to construct a 3D physical model and a wind
tunnel with the possibility of simulating stratified atmosphere [8].
Figure 8: Setup to generate an atmospheric Boundary layer in a wind tunnel[15]
2.3.3. Numerical Model
Numerical Models are methods that use computers to perform large number of calculations of
fluid flow and simulations of flow pattern and fluid structure [21]. Different types of these models
ranging from simple linear models to more complex non-linear models are available for wind
profile study. Linear models have limited accuracy and high resolution with low computational
time and ease of application while complex non Linear models have higher accuracy at the
expense of extensive computational effort [21].
Wind Atlas Analysis and Application Program (WASP) that is developed by Risø Laboratory,
Denmark in 1987 is one of the most widely used Linear Numerical Models. It is limited to a
neutrally stable wind flow over low smooth hills with attached flow. It converges well in an area
of simple terrain but underperforms flow separation and recirculation [21]. Due to the long
12
CHAPTER TWO
familiarity with this model, empirical adjustments such as Ruggedness Index 2(RIX) are available
to improve its results in a complex terrain[22]. The working principle, sub models of WASP, and
its applicability in complex environment and correction factor used for improving the results are
discussed in Chapter 3.
Computational Fluid Dynamics (CFD) is a non-linear numerical method. It is a technique that
solves numerical fluid models using the Reynolds Averaged Navier Stokes (RANS) equations.
ANSYS-CFX and FLUENT are among the commercial CFD software packages used to determine
the wind behaviour in highly complex terrains. The other CFD based software is WindSim that
combines advanced numeric processing with compelling 3D visualization in a user-friendly
interface [21]. In this software, the k- ε turbulence model is used[23]. 3DWind is non-linear
Navier-Stokes solver which uses the 3D finite volume method which divides the solution domain
into finite number of grids and the RANS to describe the interaction between the grids[22].
Generally, CFD modelling is computationally expensive and very difficult to extend the
simulation domain to cover a large area such as cities.
Performance comparison of WASP and WindSim on a complex terrain was performed in Scotland.
The comparison takes into account overall wind speed, sector wind speed, and wind direction
prediction with measurement data from South West Scotland as a baseline for validation. WASP
has shown superior accuracy in predicting sector wind speeds and wind direction whilst WindSim
predicted the overall wind speed with a better accuracy. However, the introduction of
RIX
improved the accuracy of WASP significantly and thus gave it an overall superiority in
accuracy[24]. Moreover, a study in Norway showed that despite the complex terrain the model
was applied, WASP performed better than WindSim and 3DWind models for vertical wind
profiles, and annual average wind speed estimation[25] .The study also mentioned that the CFD
models are advantageous because of the explicit calculation of Turbulence.
Another comparison (between linear (WASP) and non-linear (FLUENT 6.2) wind flow models)
was made for a 4 km long Alaiz hill, North of Spain[26]. The study has shown that Fluent 6.2
predicts wind speed with higher accuracy than (WASP) based on measurement data on the same
hill.
2
RIX is defined as the percentage of terrain within a given area that exceeds a 30% slope .It is used to take in to
account flow separation when WASP is used in a complex terrain
13
CHAPTER TWO
Figure 9 : Alaiz test site used for comparison of WASP and FLUENT[26]
2.3.4. Coupled Meso-scale and Micro-scale Modelling
Meso-scale models make wind prediction for larger regions. Though they can give an overview of
wind speed for an entire area, they cannot be used for the siting purposes as they have big grid
resolution and do not include the local topographical effects. The widely used Meso-scale wind
models are KAMM (Karlsruhe Atmospheric Meso-scale Model) and WRF (Weather Research and
Forecasting). The micro scale wind models include the effects of topographical features such as
obstacles; orography and terrain roughness. They also have small grid resolution, which makes
them suitable for siting purposes. The most widely used micro-scale models include CFD models,
and WASP.
In Norway, the coupling of micro-scale model with meso-scale model was performed and
validated for three wind farms in regions of different terrains[27]. The meso-scale model (WRF)
was used to create a wind map with a resolution of 2 km from the meteorological stations.
Another study [28] performed by combining MMKK with WASP also concluded that the coupled
method improves the accuracy of prediction .
WRF/CFD coupled with environmental library model was tested for Oklahoma City to get an
improved high-resolution wind characterization of an urban area as well as improved meso-scale
data. The model was then found to be accurate and efficient [29].
14
CHAPTER TWO
2.4. Conclusion
As discussed in Section 2.2 urban areas are composed of buildings, canopies, street canyons etc.
each with distinctive wind behaviours. Different models can be (and have been) developed with
certain accuracy to study wind behaviour for the different types of urban areas. However, when a
large-scale wind study such as wind resource mapping is considered the complexity of the urban
wind behaviour makes it difficult.
Of the different wind study methods introduced in Section 2.3, CFD model FLUENT gave high
accuracy in complex environment. The main limitation to this model is its being computationally
expensive and having small computational domain that makes it difficult to do wind mapping in a
larger scale. Despite its limitation in complex terrain, WASP gave comparable (sometimes better)
results to the CFD models WindSim and 3DWind, when used in complex terrains of Scotland and
Norway. Application of ∆RIX also improved its accuracy. However, this cannot prove that
applying WASP in complex environment gives a good result.
It is also seen that Coupling of micro-scale and meso-scale models improves wind prediction
accuracy. However, no comparison studies are made to indicate which coupled methods
WRF/CFD, KAMM/WASP or WRF/WASP performs better in urban area.
Even though WASP has some limitations, such as lower accuracy in a complex environment and
no turbulence intensity analysis, it was adapted for this work because of the following reasons

Widely used and accepted method even

Less computational time when compared to CFD models

Covers larger domain for wind mapping purpose

Gives high resolution wind map
15
CHAPTER TWO
16
CHAPTER THREE
3. WASP
In this chapter, the working principle and sub models of WASP are discussed. The
limitation of WASP in a complex terrain and the error indicator RIX are also covered.
3.1. Introduction
WASP is based on the concept of linearized flow model. In this flow model, flow over a
complex terrain is viewed as the sum main logarithmic flow over flat terrain and uniform
roughness and field of perturbations caused by the departure from the flat terrain. The set of
steady state equations deduced from mass and momentum flows are linearized and solved by
assuming that these perturbations are small compared to the main flow [30].
The working procedure is based on the use of set models to do some correction on measured
wind data and then analysis of the corrected data in terms of frequency distribution. The steps
are explained as follows and are seen in Figure 10.
1. Analyse the observed wind climate from the measurement site and create a wind rose
and wind distribution:Time-series of wind speed and direction —> observed wind climate (OWC)
2. Remove the local effect such as roughness, terrain and obstacles using the different sub
models and extrapolate it vertically to create a regional wind atlas over a flat and
homogenous terrain and neutral atmosphere at a range of heights and roughness
conditions (up arrow):Observed wind climate + site description —> regional wind climate (wind atlas data
sets)
3. Apply local effect of the predicted site using the sub models again and create a local
wind climate (down arrow) to get a „Predicted wind Climate‟. For wind mapping
purpose, this step can be applied on number of grid cells.
Regional wind climate+ site description Predicted Wind Climate (PWC)
17
CHAPTER THREE
Figure 10: The wind Atlas methodology [31]
18
CHAPTER THREE
Weibull Fitting
While analysing the observed wind climate weibull fitting is the most important step. Usually
the observed wind climates deviate from the weibull distribution .In this case choosing an
appropriate weibull fitting method is very important. In WASP, moment fitting method is
used [31]. In this fitting method for each sector, the two-weibull parameters are determined
so that the following two conditions are met

The total wind energy in the fitted weibull distribution and the observed distribution
are equal and

The frequencies of occurrence of the wind speeds higher than the observed average
speed are the same for both distributions.
3.2. WASP Sub-Models
A wind profile near the surface of the earth is influenced by the topography. For wind power
meteorology, these effects can be summarized into three main categories [32].

An orographic element when the scale of terrain features becomes larger than
the height of the point of interest.

A roughness element when the collective effect of terrain surface and its
roughness lead to the retardation of the wind near the ground.

An obstacle element when the terrain feature (obstacle) has comparable height
to the point of interest and is located close to the point of interest.
In WASP orographic, roughness change, and shelter models are used to address the above
effects of topography on wind profile.
The Orographic Model
The orographic sub model is used to calculate the wind velocity perturbations induced by
orographic features such as single hills or complex terrain. For doing so, WASP utilizes a „BZ
model‟. During the analysis, it first calculates the potential flow perturbation induced by the
terrain and then modifies it to take into account the effects of surface friction in the inner
layer. The detailed mathematical formulas used in this model can be found from [31].
19
CHAPTER THREE
Figure 11 shows the Zooming grid of the BZ model. While performing wind prediction for
the hill site it uses all the grid shown [33] .The gird is zoomed from 25 by 25 sq. km area in
one to 200 by 200 sq. km in four. A BZ flow model has the following advantages [33] .

It employs a high-resolution Fourier-Bessel expansion, zooming, polar grid.
This is coupled with a map analysis routine in order to calculate the potential
flow perturbation profile at the central point of the model.

It integrates the roughness conditions of the terrain surface into the spectral or
scale decomposition. The 'inner-layer' structure is calculated using a balance
condition between surface stress, advection, and the pressure gradient.
 It uses an atmospheric boundary layer thickness of approx. 1 km to force the
large-scale (say, more than a few kilometres) flow around high-elevation areas.
1
2
3
4
Figure 11: The Zooming grid of BZ flow model
20
CHAPTER THREE
Roughness change model
The roughness change model is used to address the influence of the surface roughness around
the site. When wind flows from an area of roughness length
to
the internal boundary
layer grows to a certain height „h‟ downwind from the roughness change [31]. Equation 7 can
be used find the height of the IBL at a distance „x‟ from the roughness change.
(
Where
)
Equation 7
(
)
: h is the boundary layer height
: x is the distance from roughness change
By matching the neutral wind profile at height „h‟ an empirical relation that models the
friction velocity was developed Equation 8 [31]. This equation is used to relate the friction
velocity at the observed site to the friction velocity upstream of the roughness change.
( ⁄
( ⁄
Where:
)
)
Equation 8
is the friction velocity upstream of the roughness change
:
is the friction velocity downstream the roughness change
Upstream/downstream from the roughness change and above the developing IBL the wind
speed is determined using logarithmic wind profile and the upstream/downstream roughness
lengths. The friction velocity at the point of interest can be determined using Equation 8 .
Below this height and downstream the roughness change however, the wind profile is
perturbed and it is impossible to use logarithmic wind profile. It is modified by taking the
height above the ground, the two roughness lengths, and the height „h‟ of IBL in to
consideration [31]. Numerical modelling and experimental evidence proved that the three
logarithmic parts in Equation 9 model perturbed wind profile very well[31].
21
CHAPTER THREE
( ⁄ )
( ⁄ )
(
)
( ⁄ )
( ⁄ )
Equation 9
( ⁄ )
( ⁄ )
{
(
Where
⁄
(
⁄
) and
) and
is the von Karman constant
are the friction velocity and roughness length corresponding to the
measured wind speed respectively
:
:
are the friction velocity and roughness length upstream
s the height where wind speed prediction is to be done
The average surface stress and surface wind speed depends on the surface conditions only up
to a certain upstream distance. While using roughness change map it is advisable to consider
surface conditions up to 10 km. However if there is a considerable roughness change such as
coastal area extending the map up to 20 km will improve results [32]. The roughness length is
dependent on the size and distribution of elements such as vegetation, built up areas it
changes with foliation, snow cover etc., and hence is treated as a climatological parameter.
For generating the wind atlas, WASP uses different standard roughness classes shown in
Table 1 .
Roughness class
Roughness length (m)
Terrain description
0
0.0002
1
0.03
Farmland with very few buildings/trees
2
0.1
Less open farmland with trees and buildings
3
0.4
Rough surface with trees and buildings
4
1.5
Smooth land and water surfaces
Rough surface with dense trees and buildings(urban
areas)
Table 1: Standard roughness class used in WASP [31]
22
CHAPTER THREE
The roughness change maps used for this work are generated “wasp_map.exe” roughness
map generator that is available at KNMI website[10]. It uses the land-use data base
LGN3+[34]. The roughness values used for the different land uses are shown in Appendix C.
By using the RD coordinates as an input, a roughness change map that has suitable format for
use in WASP is generated. The output map was used with some modifications .Of these
modifications, assigning roughness length of 0.03 m around the two KNMI sites to account
for the fact that the observed wind data is potential wind speed is one of them. The other
modifications are based on recommendations given on [17] . These roughness values depend
on the density of package and height variation of the buildings of an urban area Table 2.
Description of area
Medium height and density :- Residential – one or two story single houses,
gardens, small trees ,mixed houses and small shoe warehouse , light industrial
Roughness values [m]
0.3-0.8
Medium height and Density:- Residential –two and three story large or closely
spaced, semidetached and row houses, large trees, less than five story blocks of
flats with open surrounds ,Mixed house with shape ,light industry ,churches,
0.7-0.1.5
schools.
Tall and high density :- Residential-closely spaced<six story row and block
building or major facilities (factory, university ,etc.)town centre
High-rise: - Urban core suburban or suburban with modesty tower blocks in
dense urban surroundings major institutional complexes.
0.8-1.5
>2.0
Table 2 : Roughness length values depending on density of package and height of buildings[17]
Shelter model
Close to an individual obstacle at distance comparable to the height of the obstacle and at
heights likewise comparable to the height of the obstacle the wind profile is perturbed [31].
Hence, for addressing this perturbation the obstacle needs to be treated separately using the
shelter model. Shelter of an obstacle depends on the distance between the obstacle and site,
the height of the obstacle, the height of the point of interest at the site, the length and porosity
of the obstacle[31]. Equation 10 gives the reduction of wind speed due to shelter of infinite
long two-dimensional obstacle of zero porosity [31].
23
CHAPTER THREE
(
Where:
(
(
)
( ⁄ )
)
(
)
Equation 10
)
: p is porosity (open/total area)
:h is height of obstacle
:z is height considered
:x is Downstream distance
3.3. Factors affecting the prediction process
As mentioned in[32] the factors that affect the WASP prediction process are grouped into the
following categories:
 Atmospheric conditions
The atmospheric conditions that affect WASP prediction occur due to location of
predicted and predictor site at different regional wind climate. The existence of two
sites in one climatic condition can be shown using Correlation coefficient. However,
even when the sites are found under the same regional wind condition due nonstandard atmospheric conditions such as atmospheric stability and stratification;
prediction errors can occur[35].
 Wind speed records
While prediction WASP assumes that the two sites (predictor and predicted) are fully
correlated. However, if the averaging time is very small this is not always true unless
the sites are very close to each other. The measurement time is another factor seen to
have effect on the prediction error.
 Weibull fit error and wind direction
Prediction error can occur while forcing the observed data to fit into the weibull
frequency distribution. The directional differences can occur when the incidence flow
is changed due to oblique steeps ridges.
 Orography
The effect of topography is very significant besides atmospheric conditions.
Predictions errors can be caused by site ruggedness, flow separation, and use of
topographic features beyond the terrain map considered by WASP etc.
24
CHAPTER THREE
3.4. Ruggedness Index (RIX)
The ruggedness index (RIX) of a given site is the fractional extent of the surrounding terrain
which is steeper than a critical slope which is usually 0.3 [32]. It was proposed to give a
measure of flow separation in complex terrains, which are outside the operation envelope of
WASP. RIX value of zero means that the terrain is within the working envelop of WASP.
It is calculated for a number of sectors originating from site, by dividing each radius into line
segments defined by the crossing of the radius with contour lines [36]. The sum of segments
whose slope is greater than 0.3 divided by the sum of all the segments (which equals the
radius) gives the RIX value of the radius. The Site ruggedness index is then given as the
mean of the sector wise RIX values.
Figure 12: Ruggedness index of area around Rotterdam Noord (the thick red Points inside the circle indicate RIX
greater than 0.3)
It was mentioned in [32] that ∆RIX that is the difference in RIX value between the predictor
and predicted site (
) is a suitable indicator of the
prediction performance. If the predictor site is more rugged than the predicted (turbine) site
then the wind speed is under predicted and if the turbine (predicted) site is more rugged than
the predictor then the wind speed at the predicted site is over predicted. However if the two
site have comparable ruggedness index (∆RIX
)there is chance for good prediction[32].
25
CHAPTER THREE
26
CHAPTER FOUR
4. SYNTHETIC DIGITAL SURFACE MODEL
In this chapter, DSM and its software applications are discussed. It also presents the procedure
used for creating a smoother synthetic surface that evolves above clusters of buildings.
4.1. Introduction
DSMs are topographic models of the
top (reflective) surfaces of buildings, trees, towers and
other features which are elevated above the „bare earth‟ while Digital Terrain Models (DTM) or
Digital Elevation Models (DEM) are elevation models of „bare earth‟ [37]. DSMs or DEMs can
be represented as Altitude Matrices in Raster Mode (a grid of Squares) and as isoclines or
Triangular Irregular Networks (TIN) [38] . The difference between the DSM and DTM is that
the DSM includes the elevation of trees while the DTM does not Figure 13.
DTM/DEM
Figure 13: Difference between DEM and DSM [39]
In wind Engineering, DSM is useful for estimation of urban wind profiles and wind loads on tall
buildings. It is also helpful for environmental analysis such as shadowing and solar radiation
aside from urban planning [40-42].
Automatic measurement of terrain elevation (such as photogrammetry and airborne laser
scanning) and cartographic digitizing of topographic maps are the widely used DTM
construction methods [38]. Automatic measurement usually results in bulk data which need to
pass through various post processes that use different interpolation methods before generating a
DTM [38]. For the construction of DSM as well, automatic measurement followed by spatial
interpolation method is a commonly used technique.
27
CHAPTER FOUR
Spatial interpolation is a procedure which is used for predicting the value of a field variable at
non-sampled sites within the area covered by the sample locations [43]. Depending on its
applications it is divided into different categories;

Exact/Approximate interpolation depending on whether the surface passes
through the reference point or there exists few degrees of error.

Local /Global interpolation depending on whether pre-defined nearby points or all
the data points influence the interpolated value.

Stochastic/Deterministic interpolation whether it incorporates geo-statistical3
theory to produce surfaces with specific levels of errors or not.
Some of the well-known interpolation methods are Inverse distance Weighting, Kriging, Nearest
neighbour, Triangulation with linear interpolation, Minimum curvature, and nearest neighbour
binning [41, 44].
Comparison of DSMs created using six interpolation methods; nearest neighbour, inverse
distance weighting, triangulation with linear interpolation, minimum curvature, kriging and
radial basis functions; was performed [41]. It showed that for constant density and distribution of
data points all interpolation methods give sensibly the same result. It also showed that if there
are less sample densities kriging and radial basis functions are the most robust methods. While in
[45], it was pointed out that if the data locations are dense and uniformly distributed there will be
a fair estimate of the values despite the interpolation methods.
Even though, no technical advantage is found attached to specific interpolation method; due to
its vast application and acceptance in the construction of DSM Kriging interpolation is adopted
for the construction of DSM.
4.1.1. Kriging Interpolation
Kriging is the term given for an interpolation technique that uses information about the
stochastic aspects of spatial nature. It is based on the assumption that, values at a short distance
are more likely to be similar than at a larger distance. Unknown value at a grid node is estimated
as a weighted average of the measured values at reference points, Equation 11. The weights are
based not only on the distance between measured points and prediction locations but also on the
overall spatial arrangement of the measured points which can be expressed using a variogram
[46].
3
Geo-statistics is a branch of statistics focusing on spatial data sets
28
CHAPTER FOUR
̂( )
∑
( )
Equation 11
Where: Z (si) is the measured value at the ith location
: λi is an unknown weight for the measured value at the ith location
: so is the prediction location
: N is the number of measured values
Kriging is optimal in a sense that the interpolation weights are chosen to optimize the
interpolation function hence provide Best Linear Unbiased Estimate (BLUE)4 for the value of a
variable at a given point [43]. It uses Semi-variogram to define the weights that determine the
contribution of each data point to the prediction of un-sampled values. A detailed mathematical
modelling and description of Kriging method can be found in [47-48].
4.1.1.1.
Steps of kriging interpolation
The steps of Kriging interpolation as describe in [46] are explained as follows.
 Calculation of Empirical Semi- Variogram
This step consists of the calculation of experimental variogram from the measured data using
Equation 12. Variogram is a quantitative descriptive statistic that can be represented graphically
in a manner that characterizes the spatial continuity (i.e. roughness) of a data set. It shows a
texture difference (continuity of high and low zones) of a data set in which common descriptive
statistics and histograms fail to identify [49].
(
Where:
)
( (
( ) is variogram of separation (
)
(
))
Equation 12
)
: Z(x, y) is the value of the variable of interest at location (x, y)
:
is the statistical expectation operator
If there are n observed data points, there will be ( −1)/2 unique pairs of observations. Thus, even
a data set of reasonable size generates many pairs. This makes it difficult to plot all variograms
quickly, and hence the pairs are grouped into lag bins and averaged variogram result of a
specific lag distance is used as a representative variogram for the lag distance.
4
BLUE means that it has the smallest variance among all unbiased linear estimators.
29
CHAPTER FOUR
 Variogram model fitting
After the empirical semi-variogram is calculated and plotted against lag distance, the Variogram
Model that fits the scatter plot is chosen. The scale and length parameters are adjusted iteratively
for a better fit. Figure 14 shows a fitted spherical variogram for 10000 sample points with a scale
of 6000 and length of 480.
Column C
Direction: 0.0 Tolerance: 90.0
8000
Fitted variogram Model
Experimental variogram
7000
Scale
6000
Variogram
5000
4000
3000
2000
1000
Length
0
0
50
100
150
200
250
300
350
400
450
500
550
Lag Distance
Figure 14: Variogram Model for Sample Data with scale of 6000 and length of 480
 Prediction using the fitted model
Using the spatial information of the raw data (for computing distances) and semi-variogram
model, weights are determined and predictions of unknown values are made.
4.1.2. Grid Filtering
Grid filtering uses corresponding grid node and its neighbours in the input grid to recalculate
values of output grid nodes [50]. Neighbourhood grids are rectangular sub array of nodes that
surround the corresponding grid Figure 15. Several grid filtering methods suited for different
purposes; such as smoothing (low pass filters) and sharpening (high pass filter) exist in surfer
[50]. These filtering methods are divided into two groups; a linear convolution which computes
weighted averages of the neighbouring input grid nodes and a nonlinear filtering method which
doesn‟t use a weighted average [50].
30
CHAPTER FOUR
Maximum filtering method in which the value of output grid node equals the maximum of the
neighbouring values is one of the non-linear order statistics filtering method used in this work.
The other filtering method used for in this study is moving average, one of the non-linear
convolution methods. It computes the value for the corresponding grid node by averaging the
values of neighbouring grid nodes.
2
5
9
1
3
4
7
11
9
Figure 15: A 3 by 3-filter size grid showing the neighbouring (green) and corresponding (red) grid nodes
4.2. DSM Application Software
For the construction of DSM SURFER or ArcMap computer programs can be used. SURFER
developed by US Golden Company, is a contouring and 3D surface mapping program. It
contains up to 12 interpolation methods. It can create contours, 3D surfaces, and wireframes, etc.
from grids. It has different gridding and contouring methods with more control on the gridding
parameters [51]. It is also possible to export the elevation contour in different formats. ArcMap
has two extensions Spatial Analyst and Geo-statistical Analyst. The Geo-statistical analyst
extension provides several types of interpolation methods, and works best with Raster based
data[46].
4.3. Elevation Data
SRTM and CAD
Shuttle Radar Topography mission (SRTM) is an elevation database which is available for 80%
of the world. The data was collected by a radar system flown on board on the space shuttle
Endeavour on February 2000. The objective of this mission was to obtain RADAR data of most
of the Earth‟s land surface to produce high resolution topographic maps[39]. This data is
available in TIFF format on United States Geological Survey (USGS) website [52] with a
resolution of approximately 90 meters by providing the latitude and longitude of the area. CAD
maps containing elevation data of buildings of the areas of interest can then be draped on the
prepared SRTM data to construct a DSM. However, the SRTM data was not used in this study
31
CHAPTER FOUR
for two main reasons. These are the low resolution of the SRTM data and the lack of CAD map
of the cities with information about the elevation of buildings.
Actual Height of the Netherlands (AHN)
The Actual Height of the Netherlands (AHN) data is an elevation map which is measured with
laser meter, a technique in which a plane or helicopter with a laser beam scans the surface[53].
The measurements of duration of laser reflection and the movement of the aircraft together give
a precise measurement of the height. This data includes elevation of buildings, trees, and other
features on the scanned surface. It was made available by the map room of TUDelft Library in a
.xyz data format.
The coordinate system used in the AHN data is the Empire Triangle Coordinate, which is also
known as Rijksdriehoekscoördinaten or RD-coordinate. RD-coordinate is a Cartesian coordinate
system, which is based on „false origin‟. It has a datum name Amersfoort because the „false
origin‟ is located in Amersfoort. Netherlands is found between the RD-coordinates of 20 km and
300 km false Easting (x coordinate) and 300 km to 600 km false Northing of RD coordinate
Figure 16 [54].
The AHN data is divided in to grids and assigned particular numbers for easy organisation
Figure 17. Each grid cell covers a 40 by 25 sq. km area which is then further divided into 32
smaller cells of area of 5 by 6.25 sq. km and given additional alphabetic representation [53]. The
particular sets of cells used for requesting the data from the TUDelft Map room are listed in
Table 3.
City
Delft
Rotterdam
AHN data name
37en1,37en2
37bz2, 37dn2, 37dz2, 37ez1, 37ez2, 37gn1, 37gn2, 37gz1,
37gz2, 37fz1, 37hn1, 37hz1,
Zoetermeer 30hn1,30hn2,
Table 3: AHN data sets used for data Request
32
CHAPTER FOUR
Figure 16: Location of the Netherlands in RD coordinate [54]
33
CHAPTER FOUR
20
60
100
140
180
220
260
600
575
550
525
500
475
450
425
400
375
350
325
300
Figure 17: AHN data cell representation on RD coordinate system
4.4. Construction of a synthetic surface above the urban area
In this section, the steps followed for creating a synthetic surface that evolves above the urban
areas are presented. For showing the results of each step, an AHN data covering 3 by 3 sq. km
area of Rotterdam is used. All elevations are in centimetre and the coordinates are RD coordinate
system. A flow chart showing the steps is included in Appendix A.
Step 1: Grid Formation
The purpose of any gridding method is to create a regularly spaced rectangular array of Z values
from irregularly spaced XYZ data points using spatial interpolation[41].Each of the AHN data
used in this work have 1048576 data points. A gridding space of 5 m is used to get the similar
grid node values as that of the AHN data. For the formation of the grid the default linear
variogram is used because

The formation of a variogram model for large number of data points takes much
computational time and

The xyz data are regularly spaced and hence formation of variogram becomes
insignificant.
34
CHAPTER FOUR
A residual5 of zero that indicates very high accuracy is found while checking the correctness of
the interpolation method. The reason for such a high accuracy is the use of 5 m grid size
(spacing), which is the same spacing as that of the regularly spaced .xyz AHN data.
The 3D surface model and shaded relief map of the grid are shown in Figure 18. From the maps,
the cluster of buildings in an area can be seen. Nevertheless, in the 3D model it is difficult to
differentiate buildings and other features of the urban area.
Step 2: - Grid mosaic
The Grid mosaic is used to combine different grids to get a larger grid that is helpful in placing
all the meteorological sites in one map. In this step, the grid size (resolution) of the bigger map
can also be changed to different values.
5
A residual is the difference in elevation values created by interpolation and the unprocessed AHN data
35
CHAPTER FOUR
Figure 18: DSM (Top) and shaded Relief map (Bottom) of the grid where kriging interpolation is applied
36
CHAPTER FOUR
Step 3: -Grid Filter (Smoothing)
To remove the detailed irregularities in the 3D model and create a surface that evolves above the
cluster of building and other features grid smoothing/filtering is a very important step. The filter
type, number of times the filter is applied, and the filter size are adjusted using the filtering
dialog box. As there are no standard methods suggested for this type of work, a trial and error
method with visual inspection was used for choosing suitable combination of grid filtering
methods.
 First, the non-linear maximum filtering method with a five by five filter size was
used. The DSM is then constructed using the filtered grid. As seen in Figure 19,
the DSM shows a clearer view of buildings. Large ruggedness index values were
observed while using this surface as an elevation contour.
Figure 19: DSM of filtered grid (Maximum filter size 5 by 5)
 Hence, for creating a smooth synthetic surface that evolves above clusters/groups
of buildings a combination of maximum and moving average filtering methods is
applied. First, maximum filtering and then moving average methods are applied to
smoothen the surface. A maximum filter method with filter size of 25 by 25 was
37
CHAPTER FOUR
applied to the grid formed in the step one. Further grid filtering was performed by
selecting a moving average filter method with 39 by 39 filter size.
Figure 20: 3D model of the double filtered grid
 Because of the moving average filtering method at some locations, buildings
might go beyond the synthetic surface Figure 21. To include the elevation of the
buildings that go beyond the smoothened surface, grid math command of surfer is
used. By subtracting, the double filtered grid from the single filtered grid and
blanking out all the negative elevations (i.e. buildings covered by surface) the
elevation of the buildings above the synthetic surface is calculated. The grid
formed with this data is then added to the double filtered grid to find the final
elevation data. Figure 20 shows the DSM created using the final elevation grid.
38
CHAPTER FOUR
Figure 21: Overlaid 3D surface maps of double filtered (grey) and single filterd grid(red)
Figure 22: 3D surface of the final Grid
39
CHAPTER FOUR
Step 4: - Contouring
In this step, an elevation contour is created from the final grid Figure 23. To study the sensitivity
of the contour level on the wind resource; the elevation contours are created with different
contour levels. The results were then exported to DXF files with the contour level to be used as
an input to WASP map editor.
432000
431000
430000
429000
428000
427000
426000
80000
81000
82000
83000
84000
Figure 23: Elevation contour of the final grid (synthetic surface)
40
CHAPTER FOUR
Ruggedness index of the DSM and the Synthetic surface
The following table shows summary of the sector wise RIX calculated for areas around
Geulhaven and Rotterdam Noord are shown Table 4. Threshold of 0.3 and radius of 2500 m
were used. From the table it can be seen that the individual ruggedness index is lower for the
synthetic surface. Having the lower ruggedness index will show that the terrain is relatively
gentle and suits the WASP analysis better.
Geulhaven
Synthetic
DSM
surface
Rotterdam Noord
Synthetic
DSM
surface
Total RIX %
0.22
2.66
0
4.27
1
0
0.84
0
5.78
2
0
1.07
0
4.42
3
0
3.33
0
3.98
4
0.33
2.46
0
0.36
5
0.18
0.94
0.1
2.49
6
0.0
2,64
0
3.74
7
0.11
3.32
0
4.92
8
0.26
4.8
0.2
4.14
9
0.03
6.79
0
5.63
10
0.47
3.25
0.19
6.74
11
0.11
5.57
12
0.68
2,97
0
3,93
13
1
3.59
0
2.72
14
0.38
0.24
0
3.92
15
0
0.39
0
4.67
16
0
0.3
0
5.59
5.54
Table 4: Summary of Ruggedness Index for Geulhaven and Zestienhoven
41
CHAPTER FOUR
42
CHAPTER FIVE
5. WIND SPEED DATA
Long-term wind data with dense measurement masts are very important for wind resource
analysis. In this chapter, the meteorological station and the short-term measurement data
used for this work are discussed.
5.1. Historical Wind Data
The HYDRA project, which started in May 1998 and ended in November 2005 was first
organised to do risk assessment on the Dutch dike systems but later it ended up yielding
important information for the Wind Energy Communities. There are more than 50 measuring
stations throughout the Netherlands[34]. For some of the stations, the measurement started in
the early ‟50s. The wind speed and direction are recorded with a resolution of 0.1 m/s and 10⁰
respectively. The data was very well examined by KNMI and was given different quality
codes. The codes show if the data is valid, questionable, added, or corrected manually etc.
From the website hourly „potential wind speed‟, direction, date, time and quality code can be
downloaded freely in ASCII format [34]. Potential wind speed is wind that is corrected for
the effects of shelter from buildings or vegetation i.e. wind speed at 10 m height with the
station surrounding flat and roughness length of 0.03 m (which is equal to that of grass) and
free of obstacles [34].
The quality of the data that is collected from the measuring stations is of great importance for
quality of the wind map. For this study, a potential wind with a “valid data” code is directly
used without further quality assessment. Information about the location of the measurement
mast, measurement height, the measurement, and recording equipment, and status of the
station were documented. They can be accessed from the website [34]. All these information
allow relying on the KNMI data possible. The meteorological stations used are discussed in
detail in Sections 5.1.1 and 5.1.2.
43
CHAPTER FIVE
5.1.1. Station 344: Zestienhoven
Zestienhoven is found at latitude of 51.955⁰ and longitude of 4.444⁰ or X=90125 m and
Y=44100 m RD coordinate. The measurement period for this station starts from 1981.
However for this study the recent years measurement data (January 2001 - December of
2009) are used. The wind speed and direction are analysed using WASP utility package OWC
to get site-specific wind climate. A directional binning of 16 sectors each with 22.5⁰, which is
similar to the console binning of the measurement mast of the Rotterdam Noord Police
station, is used.
Figure 24 shows the result from OWC analysis: the Weibull distribution and wind rose. The
mean wind speed and power density are 4.94 m/s and 152 W/m2. It can be observed that the
prevailing wind directions are south and southwest direction (sectors 9, 10, 11, 12, and 13)
with an overall frequency of 39%. The strongest wind is 6.45 m/s from sector 12.
16
1
2
1
15
3
14
4
13
5
12
6
11
7
10
9
8
Figure 24 : Observed Wind Climate of Zestienhoven
44
CHAPTER FIVE
5.1.2. Station 343 Rotterdam Geulhaven
This station is located at latitude of 51.893⁰ and longitude of 4.313⁰ in RD coordinate. Even
though the measurement for this meteorological station starts from 1981, the data registered
from January 2001 until December 2009 is used for this study.
The Weibull distribution and the wind rose from OWC analysis are shown in Figure 25. The
mean wind speed, and power density for this site are 5.71 m/s and 196 W/m2 respectively.
The prevailing wind directions are south and southwest (sectors 9, 10, 11, 12) with a total
frequency of 36.2% sectors 9 and 11 each contributing more than 10 % each. The strongest
wind of 7.25 m/s is registered for sector 12
16
1
2
15
3
14
4
13
5
12
6
11
7
10
9
8
Figure 25: Observed Wind Climate of Geulhaven
45
CHAPTER FIVE
5.1.3. Comparison of Zestienhoven and Geulhaven
Even though the wind speeds are standardized to topographies of flat terrain and roughness
length of 0.03 m, there is a difference of 0.77 m/s in mean wind speed with Geulhaven having
the higher value. This is because Geulhaven is located near to the coast of North Sea and
river ‟Nieuwe Maas‟. For both sites, the strongest wind comes from sector 12.
Sector
No.
Zestienhoven
Geulhaven
Frequency
Zestienhoven
Geulhaven
Difference
1
Frequency
[%]
6.8
Wind Speed
[m/s]
3.34
(fz –fg )
6.1
Wind Speed
[m/s]
4.12
2
3.1
3.41
4.5
4.12
-1.4
3
5.9
4.08
5.7
4.69
0.2
4
5.4
4.15
4.0
4.69
1.4
5
6.5
4.31
5.6
5.46
0.9
6
3.7
3.92
2.7
5.61
1
7
1.9
3.65
2.9
4.67
-1
8
4.2
4.32
5.5
4.41
-1.3
9
11.3
5.03
12.3
5.81
-1
10
8.5
6.30
4.9
6.65
3.6
11
9.5
6.35
11.6
7.10
-2.1
12
9.7
6.45
7.4
7.25
2.3
13
11.3
4.98
9.5
6.70
1.8
14
4.7
4.81
5.1
6.06
-0.4
15
3.4
4.78
6.1
5.56
-2.7
16
4.0
4.53
6.2
4.88
0.7
All
4.94
Frequency [%]
0.7
5.71
Table 5: Observed wind climate comparison of the two KNMI stations
46
CHAPTER FIVE
In addition to the difference in the mean wind speed, a sector wise frequency difference exists
between the two KNMI stations. The maximum frequency differences are seen in the
southwest direction especially sectors 10, 11, and 12. The wind speed frequency of
Geulhaven is higher than that of Zestienhoven for sectors 9 and 11 but lower for sector 10
and 12 Figure 26. This is because of the neighbourhood topography that deflects the wind
flowing in the direction of sector 10 (from southwest direction; which is the prevailing wind
direction in the Netherlands) to sectors 11 and 9 Figure 27.
Figure 26: Sector wise wind speed frequencies of the KNMI stations
Figure 27: Blockage effect of the neighbouring areas for Geulhaven [Google earth]
47
CHAPTER FIVE
5.2. Short Term Wind Data
Rotterdam Noord Police station
The wind data collected from the Rotterdam Noord police station is used for Validation .The
building has a height of 35 m and is located at latitude of 51⁰ 54‟0‟‟ N and longitude of
4 ⁰ 30‟0” E or
in RD coordinate. The Davis Vantage Pro2
weather stations were installed in October 2009 (stations 1, 2, 3, and 4 at an elevation of 5
m). The middle station (station 5) was installed on a 9 m pole on November 2010. The data
from these stations were used to estimate mean wind speed and power density of the different
locations of the site. The setup has then been changed from January 2010 onwards to perform
wind shear analysis. The measurement heights of the new setup are 9 m, 7 m, and 5 m for
TOP, MIDDLE and BOTTOM weather stations respectively Figure 28. A 10-minutes
logging interval was selected, and a visit was made to the station every two weeks to collect
the data.
Figure 28: Old measurement setup (left) and new measurement setup (right) of Rotterdam Noord Police station
For analysing the wind data for the Rotterdam Noord police station, the wind data from
„station 5‟ of the old setup and „TOP station‟ of the new setup are combined. The wind data
from November 11, 2009 until October 15 of 2010 are used. Some wind data are lost due to
malfunctioning of the console and failure to collect the data on time.
48
CHAPTER FIVE
Figure 29 shows the results from the OWC analysis. The prevailing wind direction is south
(sectors 8, 9 and 10) with a total frequency of 26.5 %. However, sectors 1 and 16 also have
high frequency. The 10-minute average wind speed for the measurement period is 4.42 m/s
and strongest wind speed of 5.57 m/s is registered for sector 10.
16
1
2
1
15
3
14
4
13
5
12
6
11
7
10
9
8
Figure 29 : Observed Wind Climate of Rotterdam Noord Police station
The statistics of the wind data used for this work are summarized in Table 6. Of the different
types of prediction errors of WASP, one of them occurs while trying to fit the observed wind
data into a weibull distribution. It is quantified using percentage error. Table 5 shows the
weibull fit error for all the sites.
Station
Measurement Period
Weibull
shape factor
K
Weibull
scale factor
A [m/s]
Mean
wind speed
U [m/s]
Geulhaven
Jan 2001 - Dec 2009
2.21
6.4
5.71
Weibull
Fit errors
[%]
0.36
Zestienhoven
Jan 2001 - Dec 2009
1.81
5.5
4.94
0.49
Rotterdam
Noord Police
station
Nov 11 2009 - Oct 15
2010
1.95
5.0
4.42
0.67
Table 6: Summary of the Observed Wind Climates
49
CHAPTER FIVE
5.3. Wind Speed Correlation
The two meteorological stations, Geulhaven and Zestienhoven, are 11.4 km apart. A zero
time lag cross correlation between the meteorological stations was calculated neglecting the
directional data to check if the two stations have a strong linear correlation for the whole
measurement period; i.e. from January 2001 until December 2009. The results show that
there is a strong linear correlation between the hourly mean wind speeds of the two
meteorological stations Table 7. This shows that the two stations are found in the same
regional wind climate. It was not possible to calculate the cross correlation between the
Rotterdam Noord police station and the two meteorological sites due to the difference in the
measurement period.
Station 344 Zestienhoven
Station 343 Geulhaven
Station 344 Zestienhoven
1.0000
0.9164
Station 343 Geulhaven
0.9164
1.0000
Table 7: Zero time lag Correlation between the KNMI stations
50
CHAPTER SIX
6. APPLICATION OF WASP ANALYSIS
This chapter discussed the results of WASP analysis performed by using the synthetic surface
as a topographic map.
6.1. Cross prediction
In WASP, the use of more than one meteorological station to define a wind atlas is not possible.
However, the different masts/meteorological sites can be used to validate the wind resource
modelling by performing a systematic set of comparative predictions known as cross prediction. It
is the procedure of using one meteorological station to calculate the regional wind climate and
then use it to perform prediction for the wind climate at the second station; and then repeating the
procedure but using the second station as a predicting station. In this study, the two
meteorological stations, Geulhaven and Zestienhoven are used for cross prediction. In addition,
prediction was made for Rotterdam Noord Police station using the two meteorological stations.
A synthetic surface of size 19.995 by 18.745 sq. km was used as topographic map. The synthetic
surface was created using 12 AHN topographic data sets shown in Figure 30. It has a resolution of
5 m and contour level of 3 m.
18.745 Km
37bz2
37ez1 37ez2
37fz1
37dn2
37gn1 37gn2
37hn1
37dz2
37gz1
37gz2
37hz1
19.995 Km
Figure 30: AHN data used for creating the DSM used in cross prediction
The roughness change map used in this analysis is generated by the KNMI „wasp_map.exe’ and
then modified to consider the standardized „potential wind speed‟. An area of 0.04 sq. km around
both meteorological sites is assumed to have a roughness length of 0.03 m. A change in these
areas creates a difference in the wind speed prediction.
51
CHAPTER SIX
For evaluating the prediction performance, the results of each analysis are compared to the
Observed Wind Climates using the statistical measurements Root mean Square Error (RMSE) and
Percentage error (P.E.), which are calculated using Equation 13 and Equation 14 respectively.
∑ (
)
Equation 13
(
)
Equation 14
Where:
is the observed wind speed and
: n is number of sectors
is the predicted wind speed
The difference in prediction of the KNMI was also checked using percentage Difference (P.D.)
that is calculated using Equation 15.
|
Where:
|
Equation 15
wind speeds predicted using the two different meteorological sites
6.1.1. Zestienhoven as a Predictor Site
Wind Atlas
Using the measured wind data and description of terrain (contour map of the synthetic surface and
roughness change maps) wind atlas or regional wind climate of Zestienhoven is calculated in the
form of weibull parameters and wind roses. The wind atlas data for the different roughness classes
and elevations are summarised in Table 8. Figure 31 shows the wind atlas for R-class 0
(roughness length 0 m) and 10 m elevation.
16
1
2
1
15
3
14
4
13
5
12
6
11
7
10
9
8
Figure 31: Wind Atlas for R-class 0 at 10 m
52
CHAPTER SIX
Height 1
(z = 10 m)
Height 2
(z = 25 m)
Height 3
(z = 50 m)
Height 4
(z = 100 m)
Height 5
(z = 200 m)
m/s
Wm-2
m/s
Wm-2
m/s
Wm-2
m/s
Wm-2
m/s
Wm-2
R-class 0
(0.00 m)
8.59
726
9.3
917
9.82
1071
10.32
1276
10.76
1540
R-class 1 (0.03
m)
6.14
293
7.25
461
8.23
631
9.46
893
11.2
1477
R-class 2
(0.10 m)
5.35
193
6.53
336
7.54
488
8.75
710
10.39
1173
R-class 3
(0.40 m)
4.21
93
5.49
200
6.54
322
7.76
498
9.3
842
R-class 4
(1.50 m)
2.8
27
4.21
90
5.34
176
6.59
308
8.07
552
Table 8: Summary of wind atlas of Zestienhoven for different elevations and roughness classes
A trend line has been fitted for the wind speeds of each roughness classes at different elevations
Figure 32. From the trend lines, it can be seen that the wind speed increases with an increase in
elevation and a decrease in roughness class. For R-class 0, there is steeper trend line showing a
small increase in wind speed with elevation. This resulted in a lower wind speed for R-class 0
than R-class 1 at an elevation of 200 m. The reason for the steeper trend line for R-class 0 is the
low surface roughness of water that causes a small increase of wind speed with elevation when
compared to the increase on a land surface. This results in smaller wind speed over a water body
than on land at a very high elevation.
Figure 32: Wind atlas trend lines (wind shear) for different roughness classes
53
CHAPTER SIX
Self-Prediction for Zestienhoven
Figure 33 shows the weibull distribution and wind rose of the self-predicted Wind climate of
Zestienhoven. The red and green colours show the frequency deviations of predicted and observed
wind climates. These deviations are put in percentage in table. As mentioned in help manual of
WASP 10 [33] these differences arise due to the limited resolution (speed and direction) when
analyzing the time-series of measured wind.
1
16
2
15
3
14
4
13
5
12
6
11
7
10
9
8
Figure 33: Self-Predicted wind climate of Zestienhoven
54
CHAPTER SIX
Prediction for Geulhaven
After the wind atlas is created, prediction was made for Geulhaven Meteorological site. The sector
wise wind speed and percentage error are calculated and tabulated as shown in Table 9. Maximum
sector wise percentage difference of 26.87 % for sector 4 and the minimum of
-0.21% for
sector 3 are observed. The maximum wind speed for both predicted and observed wind climates is
observed for sector 12. The omni directional (all sector) wind prediction has a percentage error of
8.41 %.
Sector
#
Sector
angle [°]
OWC
U [m/s]
PWC
U [m/s]
Percentage
Error [%]
1
0
4.12
3.43
-16.75
2
22.5
4.12
3.84
-6.8
3
45
4.69
4.68
-0.21
4
67.5
4.69
5.95
26.87
5
90
5.46
6.7
22.71
6
112.5
5.61
5.25
-6.42
7
135
4.67
5.55
18.84
8
157.5
4.41
4.94
12.02
9
180
5.81
5.31
-8.61
10
202.5
6.65
8.16
22.71
11
225
7.1
7.74
9.01
12
247.5
7.25
8.84
21.93
13
270
6.7
6.1
-8.96
14
292.5
6.06
6.36
4.95
15
315
5.56
5.71
2.7
16
337.5
4.88
4.94
1.23
All
All
5.71
6.19
8.41
Table 9: Difference between predicted and observed wind Climates of Geulhaven
55
CHAPTER SIX
The wind roses of predicted and observed wind climates are put side by side for easy visual
comparison Figure 34. The wind rose of the PWC has the same shape as that of Zestienhoven
(predictor), which resulted in large sector wise frequency discrepancies between the observed and
predicted site of Geulhaven. As seen in Figure 34, the value of the sector wise frequency of the
predictor (Zestienhoven) and predicted site (Geulhaven) are comparable for each sector.
16
1
2
15
16
3
14
13
6
7
10
9
OWC
8
3
14
5
11
2
15
4
12
1
4
13
5
12
6
11
7
10
9
8
PWC
Figure 34 : Wind Speed Frequency difference
Figure 35: Frequency difference of Observed and Predicted wind climate of Geulhaven and the Predictor site Zestienhoven
56
CHAPTER SIX
Prediction for Rotterdam Noord Police Station
The prediction performed for the Rotterdam Noord police station gave a comparable omni
directional annual mean wind speed to that of the observed wind climate with only -0.45% error.
However, the sector wise wind speeds have very large prediction errors of up to a -34.72% and
34.67 % for sector 1 and 6 respectively and minimum of -1.61% for sector 16.
Sector
#
Sector
angle [°]
OWC
U [m/s]
PWC
U [m/s]
Percentage Error
[%]
1
0
4.81
3.14
-34.72
2
22.5
4.26
3.83
-10.09
3
45
3.94
4.19
6.35
4
67.5
4.05
4.42
9.14
5
90
3.89
4.2
7.97
6
112.5
3.55
4.78
34.65
7
135
4.39
3.54
-19.36
8
157.5
4.7
3.41
-27.45
9
180
4.96
3.71
-25.2
10
202.5
5.57
4.6
-17.41
11
225
4.16
4.92
18.27
12
247.5
3.94
5.19
31.73
13
270
4.11
4.87
18.49
14
292.5
4.08
4.76
16.67
15
315
3.92
4.84
23.47
16
337.5
4.35
4.28
-1.61
All
All
4.42
4.4
-0.45
Table 10: Difference between predicted and observed wind climates of Rotterdam Noord Police Station
57
CHAPTER SIX
The wind rose of the predicted wind climate has the same shape as that of the observed wind
climate of the predictor site. The sector wise frequencies of the predicted site are also comparable
to the predictor (Zestienhoven) site Figure 37. Hence, large frequency variation is seen between
the observed and predicted wind speeds.
16
1
1
15
16
2
14
5
12
6
11
7
10
9
OWC
8
3
14
4
13
2
1
15
3
1
4
13
5
12
6
11
7
10
9
8
PWC
Figure 36 : Wind Speed Frequency difference of observed and predicted wind climate of Rotterdam Noord station
Figure 37: Frequency distributions of observed, predicted wind climates of Rotterdam Noord and Zestienhoven (predictor)
58
CHAPTER SIX
6.1.2. Geulhaven as predictor
Using Geulhaven meteorological site and the same procedures as in Section 6.1.1 predictions were
performed for Zestienhoven and Rotterdam Noord Police station. The wind atlas data are
summarised in Table 11. Figure 38 shows the wind atlas for a height of 10 m and Roughness class
of zero.
16
1
2
1
15
3
14
4
13
5
12
6
11
7
10
9
8
Figure 38: wind Atlas of Station Geulhaven for R-class 0 and elevation 10 m
Height 1
(z = 10 m)
Height 2
(z = 25 m)
Height 3
(z = 50 m)
Height 4
(z = 100 m)
Height 5
(z = 200 m)
m/s
Wm-2
m/s
Wm-2
m/s
Wm-2
m/s
Wm-2
m/s
Wm-2
R-class 0
(0.00 m)
R-class 1
(0.03 m)
R-class 2
(0.10 m)
R-class 3
(0.40 m)
R-class 4
(1.50 m)
8.31
545
8.99
686
9.5
802
9.98
959
10.4
1158
5.88
214
6.99
342
8
482
9.34
729
11.33
1306
5.12
141
6.28
249
7.31
371
8.6
568
10.42
1015
4.03
68
5.28
148
6.33
244
7.6
392
9.27
715
2.69
20
4.06
67
5.18
133
6.44
241
8.01
460
Table 11 : Summary of Wind atlas data for Geulhaven
59
CHAPTER SIX
Self-Prediction for Geulhaven
The weibull distribution and wind rose were shown in Figure 39. As described earlier sections the
green and red colours show the sector wise frequency difference between predictor and predicted
site.
1
16
2
1
15
3
14
4
13
5
12
6
11
7
10
8
9
Figure 39: Self predicted wind speed of Geulhaven
Prediction for Zestienhoven
Wind Climate prediction and comparison with the OWC were done for Zestienhoven
meteorological station. Large frequency differences are seen for all the sectors. The predicted
wind rose has similar shape and comparable sector wise frequency to that of Geulhaven station
(predictor). Figure 40
16
1
1
15
16
2
14
5
12
6
11
7
10
9
OWC
8
3
14
4
13
2
1
15
3
1
4
13
5
12
6
11
7
10
9
8
PWC
Figure 40: Frequency difference of predicted and observed wind climate of Zestienhoven
60
CHAPTER SIX
Figure 41: Frequency distributions of observed and predicted wind climate of Zestienhoven and the predictor site
The results of the prediction are summarised in Table 12. A maximum percentage difference of
30.54% is observed for sector one and minimum of 0.92% for sector 8. The omni directional (all
sector) percentage difference is - 0.81%.
Sector
#
Sector
angle [°]
OWC U [m/s]
PWC U [m/s]
Percentage
Error [%]
1
0
3.34
4.36
30.54
2
22.5
3.41
4.04
18.48
3
45
4.08
4.2
2.94
4
67.5
4.15
3.49
-15.9
5
90
4.31
3.7
-14.15
6
112.5
3.92
4.41
12.5
7
135
3.65
3.42
-6.3
8
157.5
4.32
4.28
-0.93
9
180
5.03
5.59
11.13
10
202.5
6.3
5.31
-15.71
11
225
6.35
5.9
-7.09
12
247.5
6.45
5.33
-17.36
13
270
4.98
6.02
20.88
14
292.5
4.81
4.86
1.04
15
315
4.78
4.87
1.88
16
337.5
4.53
4.59
1.32
All
All
4.94
4.9
-0.81
Table 12 : Difference between predicted and observed wind climate of Zestienhoven
61
CHAPTER SIX
Prediction for Rotterdam Noord Police station
The sector wise wind speeds predicted using Geulhaven are summarised in Table 13. Maximum
percentage difference of 37.22% for sector 13 and a minimum of -0.91% for sector 16 are found.
The omni directional wind speed prediction has a percentage error of -3.85%.
Sector
#
Sector
angle [°]
OWC
U [m/s]
PWC
U [m/s]
Percentage
Error[%]
1
0
4.81
3.97
-17.46
2
22.5
4.26
4.34
1.88
3
45
3.94
4.15
5.33
4
67.5
4.05
3.65
-9.88
5
90
3.89
3.52
-9.51
6
112.5
3.55
4.54
27.89
7
135
4.39
3.1
-29.39
8
157.5
4.7
3.24
-31.06
9
180
4.96
4.04
-18.55
10
202.5
5.57
3.84
-31.06
11
225
4.16
4.48
7.69
12
247.5
3.94
4.4
11.68
13
270
4.11
5.64
37.23
14
292.5
4.08
4.57
12.01
15
315
3.92
4.79
22.19
16
337.5
4.35
4.31
-0.92
All
All
4.42
4.25
-3.85
Table 13: Difference between predicted and observed wind climate of Rotterdam Noord station
62
CHAPTER SIX
The Frequency discrepancy for this prediction is shown in Figure 42. As observed in the previous
predictions similar shape of the wind rose and comparable sector wise frequency percentages as
that of the respective predictor site (Geulhaven) are observed.
16
1
2
1
15
16
3
14
13
6
11
7
10
9
OWC
8
3
14
5
12
2
1
15
4
1
4
13
5
12
6
11
7
10
9
8
PWC
Figure 42: Frequency difference predicted and OWC (Rotterdam Noord Police station)
Figure 43: Frequency distribution of Observed and predicted wind climate of Rotterdam Noord and the Predictor site
Geulhaven
63
CHAPTER SIX
Summary of the Cross Prediction
By using the default WASP parameters, the following predictions were made for the two
meteorological sites. Even though the prediction for Zestienhoven is relatively good, higher
prediction errors were observed for Geulhaven.
Predictor
/Predicted site
Zestienhoven
Omni directional Mean
wind speed [m/s]
Geulhaven
Omni directional Mean
wind speed [m/s]
Measured
Omni directional Mean
wind speed [m/s]
Zestienhoven
4.94
6.19
4.94
Geulhaven
5.74
4.9
5.71
Table 14: Cross prediction results
Table 15 shows the percentage error and RMSE. It can be seen that even though lower percentage
error in the omni directional wind speed is seen for Rotterdam Noord when predicted using
Zestienhoven higher RMSE indicating larger sector wise prediction errors is observed.
Predictor site
Predicted site
Percentage Error in Omni
RMSE
Directional wind speed[%]
Zestienhoven
Geulhaven
Geulhaven
-8.41
0.68
Rotterdam Noord Police
station
0.45
0.86
Zestienhoven
0.81
0.4
Rotterdam Noord Police
station
3.85
0.83
Table 15: Summary of statistics of cross predictions
64
CHAPTER SIX
6.2. Comparison of predicted wind speeds of Rotterdam Noord
The Percentage difference of the two Predicted wind speeds of Rotterdam Noord Police station
was calculated. Most of the sector wise predictions have larger percentage difference than the
omni directional predictions. Combinations of various factors lead to the prediction differences.
The first one is the distance between the predictor and Predicted site. Having a reference site near
to the prediction site gives a better prediction performance. The other factor is the in
between predictor and predicted sites. It was found that the
between Geulhaven and
Rotterdam Noord is larger than that of Zestienhoven and Rotterdam Noord. Hence, predictions
made by Zestienhoven are better.
Sector
#
Sector
angle [°]
PWC(Zestienhoven) PWC(Geulhaven)
U [m/s]
U [m/s]
Percentage
Difference[%]
1
0
3.14
3.97
23.35
2
22.5
3.83
4.34
12.48
3
45
4.19
4.15
0.96
4
67.5
4.42
3.65
19.08
5
90
4.2
3.52
17.62
6
112.5
4.78
4.54
5.15
7
135
3.54
3.1
13.25
8
157.5
3.41
3.24
5.11
9
180
3.71
4.04
8.52
10
202.5
4.6
3.84
18.01
11
225
4.92
4.48
9.36
12
247.5
5.19
4.4
16.48
13
270
4.87
5.64
14.65
14
292.5
4.76
4.57
4.07
15
315
4.84
4.79
1.04
16
337.5
4.28
4.31
0.7
All
All
4.4
4.25
3.47
Table 16: Prediction similarity in using Geulhaven and Zestienhoven as predictor
65
CHAPTER SIX
Frequency difference of the predictor sites also has a contribution for the prediction differences.
Figure 44 shows the relation between the sector wise percentage difference in the predicted wind
speeds of Rotterdam Noord and the Predictor sites (Zestienhoven and Geulhaven). For most of the
sectors, the highs and lows of the predictor‟s frequency difference match the high and lows of
frequency difference of the two predictions. For majority of the sectors higher wind speed
prediction differences occur when large predictors‟ frequency differences occur.
Percenatage Difference [%]
60
50
40
30
20
10
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Sector Number
Sector Wise Wind Speed Difference(Rot'm Noord)
Frequency Difference of predictor sites(Gulhaven and Zestienhoven)
Figure 44: Relation between the prediction differences and Frequency difference of the predicted sites
66
CHAPTER SIX
6.3. Ruggedness index
The total
of cross prediction results are plotted against the prediction errors. It is seen that if
values are positive wind speed is under predicted but if negative then the wind speed is over
predicted. Because there are a few numbers of predictions, a good trend line could not be fit.
Hence, it is not reasonable to use a fitted line of this graph to estimate a range of prediction error.
40
30
20
∆Rix[%]
10
-6
0
-4
-2
-10
0
2
4
6
8
10
-20
-30
-40
Prediction Error in Wind speed[%]
Figure 45: Wind speed prediction error versus
6.4. Effect of Contour level
The influence of contour level on prediction accuracy has been investigated using the predicted
wind speeds of Rotterdam Noord Police station and cross prediction results. Elevation contour
levels of 3 m, 8 m, 10 m, 15 m, and 20 m were used. As seen in Figure 46 with increase in the
contour level the prediction error of the omni directional wind speed for Rotterdam Noord and
Geulhaven decreases. However, for station Zestienhoven the opposite trend is observed. It was
also observed that with decrease in contour level the
has increased for all the staions. Even
though this should have reduced the predciton error it was not the case for Zestienhoven.The
same thing is also seen in sectorwise predictions,for most of the underpredicted wind speeds the
increase in contour level increases their prediction error however for the overpredicted wind
speeds reduction in prediction errors is observed.
67
CHAPTER SIX
Figure 46 : Variation of omni directional prediction error with contour level
Percentage Error[%]
60
40
3m
20
8m
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
10 m
-20
15m
-40
20m
-60
Sector Number
Figure 47: Effect of contour level on the Sector wise predictions (Rotterdam Noord) using Geulhaven as predictor
68
CHAPTER SEVEN
7. RESULTS AND DISCUSSIONS
This section presents an overview of the wind maps developed for Delft, Rotterdam, and
Zoetermeer using the methodology described in the previous sections.
Introduction
Using the methodology described in the previous sections it was possible to develop a 25 m
resolution wind map for the areas of Delft, Rotterdam and Zoetermeer and at an elevation of 10 m
above the synthetic surfaces covering the areas. This height is selected because the hub height of
most UWTs installed on rooftops is around 10 m .An additional 100 m resolution wind map was
developed for Rotterdam .The wind maps show the spatial wind speed difference in urban and
rural areas. In all of the wind maps, the RD coordinate system is used. The use of Amersfoort
program from the KNMI website is recommended to change coordinate of the point of interest.
Rotterdam
For the area of Rotterdam 100 m and 25 m resolution wind maps covering
area were
developed. Zestienhoven meteorological site is used as a predictor site. Within the urban areas,
higher wind speeds are observed at elevated places, which in this case are tall buildings. It was
also possible to see very low wind speeds in the highly congested urban areas. The 25 m
resolution wind maps are presented in Appendix D.
The wind map developed for areas around Zestienhoven meteorological station is shown in Figure
49. It is developed using the wind atlas of Geulhaven. It covers an area of 2.5 by 2.5 sq. km and
has a resolution of 25 m. Within the urban areas, higher wind speeds are observed at elevated
places, which in this case are tall buildings .The shaded relief map for which the wind map was
developed is also shown in Figure 49. It shows the density of package of buildings. From the wind
map it is possible to see higher wind speed in lower density building areas and lower wind speed
in the highly dense areas.
69
CHAPTER SEVEN
Figure 48: Wind map for part of Rotterdam
Figure 49 : Shaded relief map of part of Rotterdam for which the wind map was developed
70
CHAPTER SEVEN
Delft
For developing the Delft wind map, the wind atlas generated using the Zestienhoven
meteorological station is used. As observed in the previous sections lower wind speeds were
observed in the urban areas. However within the urban area where tall buildings and street
canyons are located higher wind speed up to 6.9 m/s were observed. The areas where small
numbers of buildings are located (open areas) also have higher wind speed. Figure 50 and Figure
51 shows the wind map and shaded relief map of an area.
446400
446200
7
6.8
6.6
6.4
6.2
6
5.8
5.6
5.4
5.2
5
4.8
4.6
4.4
4.2
4
3.8
3.6
3.4
3.2
3
2.8
2.6
446000
Northing [m]
445800
445600
445400
445200
445000
444800
444600
82200
82400
82600
82800
83000
83200
83400
83600
83800
84000
Easting[m]
Figure 50: 25 m resolution wind map for Part of Delft
71
CHAPTER SEVEN
Figure 51: Shaded Relief map of part delft for which the wind map was developed
72
CHAPTER SEVEN
Zoetermeer
The wind resource map for Zoetermeer covers an area of 62.5 sq. km .It has a resolution of 25 m.
It is developed for an elevation of 10 m above the synthetic surface. For developing this wind
map, Zestienhoven meteorological station was used Appendix D (Figure 65). This wind map is
developed to show the general trend in the wind speed. There is big uncertainty in its correctness
as Zestienhoven is located far from Zoetermeer.
Figure 52 and Figure 53 show the wind map and shaded relief maps of part of Zoetermeer. From
these maps, it can be seen that in the open areas high wind speed up to 6 m/s are predicted.
However high wind speeds of up to 7.6 m/s were observed in area where high elevation buildings
are located. Areas in between the cluster of buildings also have a relatively high wind speed.
454400
454200
7.6
7.4
7.2
7
6.8
6.6
6.4
6.2
6
5.8
5.6
5.4
5.2
5
4.8
4.6
4.4
4.2
4
3.8
3.6
3.4
3.2
3
2.8
2.6
2.4
2.2
454000
Northing [m]
453800
453600
453400
453200
453000
452800
452600
92000
92200
92400
92600
92800
93000
93200
93400
93600
93800
Easting [m]
Figure 52:25 m resolution wind map for part of Zoetermeer
73
CHAPTER SEVEN
Figure 53: shaded relief map for the area on which the map was developed
74
CHAPTER EIGHT
8. CONCLUSIONS AND RECOMMENDATIONS
8.1. Conclusion
The large density of buildings and their height variation in the cities resulted in complex Digital
surface model i.e. high ruggedness. The implemented model, smooth synthetic surface, however
resulted in a better surface in terms of ruggedness index hence the topographic model is within the
WASP working envelop.
Large sector wise prediction errors are observed for both stations during cross predictions
however, omni directional predictions were relatively better. The wind rose of the Predicted wind
climate has similar shape as that of the predictor site, which resulted in high sector wise wind
speed and frequency prediction errors. It was also seen that for the particular case tested here
Zestienhoven was a better predictor for Rotterdam Noord than Geulhaven.
The influence of contour level on wind speed prediction showed two opposite trends. In case of
under prediction, the error decreases with increase in contour level while in case of over
predication, the error increases with increase in contour level. Roughness length has Wind speed
has large correlation with prediction error.
The wind resource maps show the trend of spatial wind variation. In urban areas, lower wind
speeds in range of 3-5 m/s while in transitional areas (rural to urban) and high elevation places
higher wind speed were observed. These places are areas where there is good wind energy
potential.
The methodology gave a logical first estimate of the wind potential of sites .The results and drawn
conclusions are limited to cases tested here .It should be known that the application of WASP for
complex terrain might introduce large uncertainties. Furthermore, calculation of turbulence
intensity falls out of scope of the study.
75
CHAPTER EIGHT
8.2. Recommendation
While developing the synthetic surface even though the overall ruggedness index has decreased,
the reduction was not uniform. This might result in larger
between predictor and predicted
sites, which will then result in higher prediction error. Hence devising an approach that reduced
the ruggedness index of the synthetic surface uniformly will improve the results.
For improving the results, and reducing one of the many uncertainties of the methodology, use of
raw wind data that is not standardized to a certain type of terrain roughness length is
recommended.
The use of onscreen digitised roughness length is advantageous to address different types of urban
areas but at the same time, it is very subjective. Hence, it must be done with great care. In order to
avoid the subjectivity on roughens length use of morphometric (geometric) method which uses
Actual DSM for the calculation of roughness length would be helpful.
In this study first estimate of the wind speed in the urban areas is given. The methodology needs
further refinement and validation. Installation of more measurement masts in the urban areas is
very important for improving and validating the methodology. While doing so installation of the
measurement mast in areas where high wind speeds were seen is very advantageous.
76
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79
80
APPENDICES
APPENDICES
Appendix A
The flow chart shows the procedure used for creating a synthetic surface that evolves above the
urban area.
AHN data
GRID FORMATION (Step 1)
(Kriging interpolation)
GRID A1,A2,A3 …
GRID MOSAIC (Step 2)
GRID B
GRID FILTERING (step 3)
Double filtered grid
1. Max (25 by 25)
2. MAV (41 by 41)
GRID D
Single Filtered
gird (Max 5 by 5)
GRID C
GRID C - GRID D
DATA PREPARATION
 Filter
the
data(remove all –ve Z
values)
 Crate a grid (kriging)
Data E
(.xyz format)
GRID F
Contour GRID G
(Step 4)
GRID D + GRID F
GRID G
END
81
APPENDICES
Appendix B
Rotterdam
The synthetic surface that is constructed for Rotterdam is shown in Figure 54. The coordinate
system used is RD and elevation is in centimetre.
Figure 54 : Synthetic surface above area of Rotterdam
82
APPENDICES
Figure 55: Snap shot of Rotterdam [source Google earth]
83
APPENDICES
Delft
The 3D synthetic surface and image map used for developing wind map for the area of Delft are
shown in Figure 56 and Figure 55.
.
Figure 56 : Synthetic surface evolving above area of Delft
Figure 57: Snap shot of Delft [Google Earth]
84
APPENDICES
Zoetermeer
Figure 58:3D synthetic surface evolving above the areas of Zoetermeer
Figure 59: Snap shot of Zoetermeer [Google earth]
85
APPENDICES
Appendix C
The roughness length used in “wasp_map_exe”
ID
Zo (m)
Class names
0
1
2
3
4
5
6
7
8
9
10
11
12
16
17
18
19
20
21
22
23
24
25
26
27
28
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
0.03
0.03
0.17
0.07
0.07
0.16
0.07
0.15
0.1
0.39
0.07
0.75
0.75
0.001
0.001
1.6
0.5
1.1
1.1
2
0.03
0.001
0.1
0.5
0.0003
0.1
0.0002
0.0003
0.02
0.06
0.04
0.0003
0.03
0.04
0.06
0.06
0.75
0.03
0.1
0.75
0.07
0.03
0.001
no data
grass
maize
potatoes
beets
cereals
other agricultural crops
foreign land
greenhouses
orchards
bulb cultivation
deciduous forest
coniferous forest
fresh water
salt water
continuous urban area
built-up in rural area
deciduous forest in urban area
coniferous forest in urban area
built-up area with dense forest
grass in built-up area
bare soil in built-up area
main roads and railways
buildings in rural area
runways
parking lots
salt marshes
beaches and dunes
sparsely vegetated dunes
vegetated dunes
heathlands in dune areas
shifting sands
heath lands
heath lands with minor grass influence
heath lands with major grass influence
raised bogs
forest in raised bogs
miscellaneous swamp vegetation
reed swamp
forest in swamp areas
swampy pastures in peat areas
herbaceous vegetation
bare soil in natural areas
Table 17 : Land-use and roughness classes in LGN3+ used by ‘wasp_map.exe’
86
Northing [m]
444000
445000
446000
447000
448000
449000
81000
83000
84000
Easting [m]
85000
86000
87000
2.4 2.9 3.4 3.9 4.4 4.9 5.4 5.9 6.4 6.9 7.4 7.9 8.4
82000
88000
89000
APPENDICES
Appendix D
Wind Map of Delft
87
APPENDICES
Wind maps of Rotterdam
Figure 60 shows the 100 m resolution wind map of Rotterdam. It shows a pattern of lower wind
speed (3-4 m/s) in the urban areas while rural (very lower density of buildings) areas have higher
wind speed of 4 to 5 m/s. In areas where transition from the non-built up area to more built up
areas occurs the wind speed ranges from 4-5 m/s. In this wind map, however it is impossible to
see the wind speed variation within the urban areas. For the 25 m resolution, wind map the area of
Rotterdam was divided into four parts. The wind maps of each section are shown in Figure 61
Figure 62, Figure 63 and Figure 64.
442000
440000
Northing[m]
438000
436000
434000
432000
430000
428000
426000
76000
78000
80000
82000
84000
86000
88000
90000
92000
94000
Easting [m]
2
3
4
5
6
7
8
9
Figure 60 : A 100 m resolution Wind map for Rotterdam
88
North [m]
437000
438000
439000
440000
441000
442000
443000
76000
77000
7.4
78000
6.9
6.4
79000
5.9
5.4
4.9
4.4
81000
East[m]
80000
3.9
3.4
82000
2.9
2.4
83000
84000
85000
86000
APPENDICES
Figure 61: A 25 m resolution Wind map of Rotterdam 1
89
APPENDICES
436000
8.6 8.1 7.6 7.1 6.6 6.1 5.6 5.1 4.6 4.1 3.6 3.1 2.6
435000
434000
433000
North [m]
432000
431000
430000
429000
428000
427000
426000
87000
88000
89000
90000
91000
92000
93000
94000
East [m]
Figure 62: A 25 m resolution Wind map for Rotterdam 2
90
APPENDICES
436000
10 9.5 9 8.5 8 7.5 7 6.5 6 5.5 5 4.5 4 3.5 3 2.5
435000
434000
433000
North [m]
432000
431000
430000
429000
428000
427000
426000
76000
77000
78000
79000
80000
81000
82000
East [m]
Figure 63 : A 25 m resolution Wind map for Rotterdam 3
91
APPENDICES
443000
8
7.5
442000
7
6.5
441000
5.5
5
4.5
North [m]
6
440000
4
439000
3.5
3
438000
2.5
2
437000
87000
88000
89000
90000
91000
92000
93000
94000
East [m]
Figure 64: A 25 m resolution Wind map for Rotterdam 4
Wind map of Zoetermeer
One general Pattern observed in this wind map is the high wind speed variation in the urban areas.
For most of the urban areas, the wind speed ranges from 3.5-4.5 m/s. Nevertheless, at the very
elevated places (where tall buildings are located), the wind speed reached up 6.5 m/s.
92
Northing [m]
451000
452000
453000
454000
455000
456000
2
91000
2.5
3
92000
3.5
4
93000
4.5
5
94000
5.5
6
Easting [m]
95000
6.5
7
96000
7.5
97000
98000
99000
APPENDICES
Figure 65 : A 25 m Resolution wind map of Zoetermeer
93