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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
HANDBOOK OF WIND RESOURCE ASSESSMENT
Useful reference text underpinning the theory behind wind resource assessment along with its practical application
Handbook of Wind Resource Assessment provides a comprehensive description of the background theory, methods, models, applications, and analysis of the discipline of wind resource assessment, covering topics such as climate variability, measurement, wind distributions, numerical modeling, statistical modeling, reanalysis datasets, applications in different environments (onshore and offshore), wind atlases, and future climate.
The text provides an up-to-date assessment of the tools available for wind resource assessment and their application in different environments. It also summarizes our present understanding of the wind climate and its variability, with a particular focus on its relevance to wind resource assessment.
Written by a highly qualified professional in the fields of wind resource assessment, wind turbine condition monitoring, and wind turbine wake modeling, sample topics included in Handbook of Wind Resource Assessment are as follows:
- Climate variability, covering temporal scales of variation, power spectrum, short term variation and turbulence, the spectral gap, and long-term variation
- Measurement, covering history of wind speed measurement, types of measurement, terrestrial measurements, anemometers, wind vanes, lidars, sodars and remote sensing
- Distributions, covering synoptic scale wind distributions, turbulent scale distributions, contrast between mean and extreme values, and extreme value statistics
- Physical modeling, covering spatial scales of variability, the governing equations, models of varying complexity, mass consistent models, linearized models and semi-empirical models
- Statistical modeling, covering the use of measure-correlate-predict (MCP), wind indices and spatial interpolation
Handbook of Wind Resource Assessment serves as a comprehensive text that brings together the different aspects of wind resource assessment in one place. It is an essential resource for anyone who wishes to understand the underlying science, models, or applications of wind resources, including postgraduates, academics, and wind resource professionals.
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Handbook of Wind Resource Assessment
Simon Watson Delft University of Technology Netherlands
This edition first published 2023
© 2023 John Wiley & Sons Ltd
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Hardback ISBN: 9781119055297; ePub ISBN: 9781119055396; ePDF ISBN: 9781119055303; oBook ISBN: 9781119055402
Cover Image: © Andrew Zarivny/Shutterstock
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Set in 9.5/12.5pt STIXTwoText by Integra Software Services Pvt. Ltd, Pondicherry, India
Contents
Cover
Title page
Copyright
Preface
Acknowledgments
About the Author
1 Introduction
1.1 Early Wind Measurements
1.2 The Need for Wind Resource Assessment
1.3 A Brief Overview of the Wind Resource Assessment Process
1.4 Layout of this Book
References
2 The Atmospheric Boundary Layer
2.1 The Structure of the Atmospheric Boundary Layer
2.2 The Surface Layer Wind Speed Profile
2.3 The Gradient and Geostrophic Wind
2.4 The Ekman Layer
References
3 Measurement
3.1 History of Wind Speed Measurement
3.2 Types of Measurement
3.3 Terrestrial Measurements
3.4 Remote Sensing
3.5 Meteorological Measurement Stations
3.6 The Effect of Climatic Conditions on Wind Speed Measurements
References
4 Wind Speed Variability and Distributions
4.1 Power Spectrum
4.2 The Spectral Gap
4.3 Long-term Variation
4.4 Synoptic Scale Weibull Wind Speed Distributions
4.5 Weibull Parameter Variation with Height
4.6 Synoptic Scale Wind Direction Distributions
4.7 Turbulent Scale Distributions
4.8 Extreme Value Statistics
References
5 Numerical Modelling
5.1 The Governing Equations
5.2 Microscale Models
5.3 Mesoscale Models
5.4 Global Models
5.5 Model Chains
5.6 The Importance of Atmospheric Stability
References
6 Wind Resource Estimation in Complex Terrain, Offshore, and in Urban Areas
6.1 Coastal and Offshore
6.2 Forested Terrain
6.3 Urban Areas
References
7 Orographic Test Cases
7.1 Askervein Hill
7.2 Bolund Hill
7.3 Big Southern Butte (BSB) and Salmon River Canyon (SRC)
7.4 Perdigão
7.5 Alaiz
References
8 Statistical Methods
8.1 Spatial Coherence
8.2 Measure–Correlate–Predict: An Overview
8.3 Analogue Ensembles
8.4 Wind Indices
8.5 The Robust Coefficient of Variation
8.6 Spatial Interpolation
References
9 Atmospheric Reanalyses and Wind Atlases
9.1 What Is a Reanalysis?
9.2 Global Reanalyses
9.3 Regional Reanalyses
9.4 Comparing Reanalyses
9.5 Wind Atlases
References
10 Mesoscale Phenomena
10.1 Thermally Driven Winds
10.2 Internal Gravity Waves
10.3 Low-level Jets
10.4 Convective Cells
10.5 Roll Vortices
References
11 Long-term Wind Climate Trends
11.1 The Historical Evidence for Long-term Trends in Wind Speeds
11.2 Climate Models
11.3 Possible Future Changes and Uncertainties
11.4 Impact on Resource Assessment
References
Index
End User License Agreement
List of Tables
CHAPTER 01
Table 1.1 Daniel Defoe’s proposed...
Table 1.2 The Beaufort scale...
CHAPTER 02
Table 2.1 Wieringa’s updated version...
CHAPTER 04
Table 4.1 Optimised parameters used...
Table 4.2 Kaimal spectrum constants...
Table 4.3 Weibull parameters for...
Table 4.4 Suggested values of...
CHAPTER 05
Table 5.1 Values of A and...
Table 5.2 Commonly used PBL...
Table 5.3 Some commonly used...
CHAPTER 06
Table 6.1 Model constants used...
Table 6.2 Optimum roof mounting...
CHAPTER 08
Table 8.1 Example of wind speed...
Table 8.2 Percentage weighting matrix...
Table 8.3 Percentage weighting matrix...
Table 8.4 A comparison of mean...
CHAPTER 09
Table 9.1 The availability over
List of Illustrations
CHAPTER 01
Figure 1.1 The Tower of...
Figure 1.2 Capacity factor of...
Figure 1.3 The wind resource...
CHAPTER 02
Figure 2.1 The structure of...
Figure 2.2 Logarithmic profiles for...
Figure 2.3 Stability conditions at...
Figure 2.4 Temperature stratification at...
Figure 2.5 Stability conditions by...
Figure 2.6 Schematic representation of...
Figure 2.7 Circulation systems set...
Figure 2.8 Variation in potential...
Figure 2.9 Comparison between observed...
CHAPTER 03
Figure 3.1 An illustration of...
Figure 3.2 Wind speed measurements...
Figure 3.3 Popular cup anemometers...
Figure 3.4 (a) Contribution to...
Figure 3.5 (a) Definition of...
Figure 3.6 Standard deviation of...
Figure 3.7 Pulsed-type cup...
Figure 3.8 Examples of sonic...
Figure 3.9 Schematic showing how...
Figure 3.10 Popular wind vanes...
Figure 3.11 Example barometric pressure...
Figure 3.12 Air temperature and...
Figure 3.13 Typical mast mounting...
Figure 3.14 CFD simulation of...
Figure 3.15 Popular lidar scanning...
Figure 3.16 Example lidar systems...
Figure 3.17 Laser probe volumes...
Figure 3.18 Figure-of-eight...
Figure 3.19 Schematic representation of...
Figure 3.20 Example of sodar...
Figure 3.21 Ceilometers: (a) Campbell...
Figure 3.22 Microwave radiometers (MWRs...
Figure 3.23 A radar profiling...
Figure 3.24 Illustration of a...
Figure 3.25 Streamwise turbulence as...
Figure 3.26 Continuous wave lidar...
Figure 3.27 A radiosonde about...
Figure 3.28 Comparison between the...
Figure 3.29 Processed SAR images...
Figure 3.30 Worldwide locations of...
Figure 3.31 Icing affecting the...
Figure 3.32 Data availability of...
CHAPTER 04
Figure 4.1 Wind speed power...
Figure 4.2 Power spectra of...
Figure 4.3 A UK wind...
Figure 4.4 Example wind speed...
Figure 4.5 A comparison between...
Figure 4.6 Vertical profile of...
Figure 4.7 Example fits of...
Figure 4.8 Example wind rose...
Figure 4.9 Extreme wind speeds...
Figure 4.10 Gust factor based...
CHAPTER 05
Figure 5.1 Steps followed by...
Figure 5.2 An example of...
Figure 5.3 Map of wind...
Figure 5.4 Flow over a...
Figure 5.5 Perturbation of a...
Figure 5.6 Schematic of the...
Figure 5.7 Illustration of the...
Figure 5.8 Wind flow over...
Figure 5.9 A structured mesh...
Figure 5.10 Example of (a)...
Figure 5.11 An example of...
Figure 5.12 The turbulence spectrum...
Figure 5.13 Model chains from...
Figure 5.14 (a) The European...
Figure 5.15 Flow above and...
Figure 5.16 The effect of...
Figure 5.17 A CFD simulation...
CHAPTER 06
Figure 6.1 Growth of an...
Figure 6.2 A TIBL and...
Figure 6.3 An empirical form...
Figure 6.4 Contour plots of...
Figure 6.5 RANS CFD simulation...
Figure 6.6 Plan view of...
Figure 6.7 Plan view of...
CHAPTER 07
Figure 7.1 A contour map...
Figure 7.2 Askervein Hill profiles...
Figure 7.3 Comparisons between wind...
Figure 7.4 Comparison between speed...
Figure 7.5 A contour map...
Figure 7.6 Bolund hill profiles...
Figure 7.7 Bolund hill blind...
Figure 7.8 Contour map of...
Figure 7.9 Contour map of...
Figure 7.10 Wind flow averaged...
Figure 7.11 The Perdigã...
Figure 7.12 Scanning lidar measurements...
Figure 7.13 The Alaiz mountain...
Figure 7.14 Evidence of a...
Figure 7.15 The left image...
CHAPTER 08
Figure 8.1 Longitudinal turbulence coherence...
Figure 8.2 Correlation of wind...
Figure 8.3 Schematic representation of...
Figure 8.4 Example of a...
Figure 8.5 The method of...
Figure 8.6 Wind index based...
Figure 8.7 Wind index based...
Figure 8.8 Variability of the...
Figure 8.9 Spatially interpolated mean...
Figure 8.10 An example decision...
Figure 8.11 Overview of a...
CHAPTER 09
Figure 9.1 Steps in an...
Figure 9.2 A T62 Gaussian...
Figure 9.3 The number and...
Figure 9.4 Comparison between 20CRv3...
Figure 9.5 The NARR domain...
Figure 9.6 The inner and...
Figure 9.7 Yellow box: COSMO...
Figure 9.8 Left: HARMONIE-ALADIN...
Figure 9.9 The UM reanalysis...
Figure 9.10 The CERRA domain...
Figure 9.11 Frequency plots of...
Figure 9.12 Pearson correlation coefficient...
Figure 9.13 As...
Figure 9.14 Correlation (plotted with...
Figure 9.15 NEWA WRF high...
Figure 9.16 NEWA wind speed...
Figure 9.17 Wind resource at...
Figure 9.18 GWA3.1 wind...
CHAPTER 10
Figure 10.1 A sea breeze...
Figure 10.2 (a) An anabatic...
Figure 10.3 Mountain waves downwind...
Figure 10.4 A processed SAR...
Figure 10.5 Schematic illustration of...
Figure 10.6 Effect of an...
Figure 10.7 Schematic illustration of...
Figure 10.8 An image from...
Figure 10.9 A comparison of...
Figure 10.10 SAR image showing...
Figure 10.11 Schematic representation of...
CHAPTER 11
Figure 11.1 Trends in observed...
Figure 11.2 Global wind speed...
Figure 11.3 Trends in mean...
Figure 11.4 Trends in mean...
Figure 11.5 Median absolute deviation...
Guide
Cover
Title page
Copyright
Table of Contents
Preface
Acknowledgements
About the Author
Begin Reading
Index
End User License Agreement
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Preface
When Wiley first approached me to write this book, I naively thought it would be a process that would take about a year or so. Fast-forward five years and, owing to the pressures of work and a change of job (and country), progress had been painfully slow. Then came the infamous global pandemic. Finally, the constraints of lockdown focused my mind and gave me the required impetus to (electronically) put pen to paper and deliver the completed manuscript to a very patient publisher. I say ‘complete’, though I still consider it a work in progress in many ways. Nonetheless, I hope it now contains sufficient useful information to practitioners in the field of wind resource assessment.
Wind energy has come a long way since I started working in the field back in 1990. It promises to make a major contribution to the energy transition as the world attempts to move away from the climate-damaging effects of burning fossil fuels. Where to site wind turbines and, just as importantly, where not to site them is a very practical and economic consideration. An accurate prediction of the expected wind resource at a potential wind farm site is, therefore, vital. Several books have been written about wind resource assessment, but I wanted something that was a practical guide with enough underlying theory for students and researchers to understand the measurement techniques that are used and the physical and statistical processes that are being modelled in trying to predict the behaviour of the wind. I also wanted to give some impression of the limitations of the measurements and models and their accuracy. In so doing, I have drawn on my experience of over 30 years of research in the field of wind energy and wind resource and the material that I have taught my students. I have also tried to draw on a wide range of literature to cover the different aspects of wind resource assessment in the many environments where wind turbines and wind farms are sited. I have deliberately stayed away from translating wind resource into energy yield (apart from a couple of very brief forays) as to cover the topic of wind farm yield, including wakes, would warrant a book on its own. Maybe this will be my next book when I have had enough time to recover from this venture.
I hope you will find this book a useful reference in understanding the science and engineering of wind resource assessment. Comments and feedback are welcome as I work on the next edition of what is an evolving field of research.
Simon Watson
The Hague
Acknowledgments
I would like to thank Dries Allaerts for taking the time to proofread the manuscript and give helpful advice and comments. I would also like to thank the many people and companies for permission to use their figures and photographs, especially Adolf Thies, AQSystem, AWI, Campbell Scientific, Cian Desmond, DTU Wind Energy, Srinidhi Gadde, Gill Instruments, Nergica, Radiometrics, ReSoft, RPG, Scintech, Setra, Richard Stevens, Vaisala, Vector Instruments, WindSensor, and ZX Lidars. Finally, I would like to thank my family for all their support, in particular my wife, Christiane, for her love and support and for the numerous cups of tea provided during the long evenings drafting the manuscript.
About the Author
Simon Watson is Professor of Wind Energy Systems at Delft University of Technology. He has worked in the field of wind energy for over 30 years having graduated with a degree in physics from Imperial College London and a PhD from Edinburgh University. He began his career at the Energy Research Unit of the Rutherford Appleton Laboratory, near Oxford, UK before moving to a small start-up company which later became the green electricity supply company Good Energy. In 2001, he moved to the Centre for Renewable Energy Systems Technology at Loughborough University, Leicestershire, UK, becoming a full professor in 2010. In 2017, he moved to the Netherlands to take up his present position. Professor Watson has published widely in a range of wind energy topics, including resource assessment, wind turbine condition monitoring, and wind energy integration.
1 Introduction
1.1 Early Wind Measurements
Wind power has come a long way since the earliest windmills were built to grind corn for flour or wind pumps installed to drain the marshes for growing crops. The siting of these early machines was driven more by the practical necessity to be close to the local populace than for maximum economic benefit. Estimates of the local wind climate could mostly be obtained from local informed opinion or visual clues such as the angle of growth of trees, or indeed the complete absence of trees at extremely windy sites. Measurement of wind direction, as opposed to wind speed, has been made for far longer. One of the earliest-known examples of this is a weathervane in the shape of the god Triton which stood atop the Tower of the Winds in Athens when it was first built around 50 BCE or possibly earlier (see Figure 1.1). Weathervanes on buildings such as churches in the Western world have also been a common sight for centuries. Although giving an indication of the local wind direction at the time of observation, such early devices were not generally used in the recording of historical wind direction distribution. Before the development of instruments to measure the magnitude of the wind speed, human perception or slightly more objective visual indicators were used to provide a wind speed scale. For example, the famous English writer Daniel Defoe, following the Great Storm of 1703 which caused significant destruction in southern England, proposed an 11-point scale based on common phrases, as detailed in Table 1.1 (The Weather Window, n.d.). Scale point 6, describing ‘a top sail gale’, gives a clue to the importance of such information for the maritime community, and several such empirical wind speed scales devised by sailors were known to exist in the seventeenth century and probably much earlier. Possibly the most famous such scale was developed in 1805 by the Irish hydrographer Francis Beaufort, a Royal Navy officer who later became a rear admiral and trained Robert FitzRoy who, in turn, founded what later became the UK Meteorological Office. The so-called Beaufort scale was designed as a 13-point scale based on the impact the wind speed had on the sails of a ship. These initial visual descriptors would be later converted into an actual measurement scale, which is still in use today for some applications (e.g. shipping broadcasts) and is detailed in Table 1.2.
Figure 1.1 The Tower of Winds in Athens. Source: Joanbanjo / Wikipedia Commons / CC BY-SA 3.0.
Table 1.1 Daniel Defoe’s proposed verbal wind speed scale from around 1704.
Scale point
Description
0
Stark calm
1
Calm weather
2
Little wind
3
A fine breeze
4
A small gale
5
A fresh gale
6
A top sail gale
7
Blows fresh
8
A hard gale of wind
9
A fret of wind
10
A storm
11
A tempest
Table 1.2 The Beaufort scale with modern equivalent units of wind speed.
Wind force
Description
Wind speed
Specifications
(italics refer to conditions at sea)
Wave height
Sea state
km/h
mph
knots
Probable (m)
Max. (m)
0
Calm
<1
<1
<1
Smoke rises vertically
Sea like a mirror
—
—
0
1
Light air
1–5
1–3
1–3
Direction shown by smoke drift but not by wind vanes
Sea rippled
0.1
0.1
1
2
Light breeze
6–11
4–7
4–6
Wind felt on face; leaves rustle; wind vane moved by wind
Small wavelets on sea
0.2
0.3
2
3
Gentle breeze
12–19
8–12
7–10
Leaves and small twigs in constant motion; light flags extended
Large wavelets on sea
0.6
1.0
3
4
Moderate breeze
20–28
13–18
11–16
Raises dust and loose paper; small branches moved
Small waves, fairly frequent white horses
1.0
1.5
3–4
5
Fresh breeze
29–38
19–24
17–21
Small trees in leaf begin to sway; crested wavelets form on inland waters
Moderate waves, many white horses
2.0
2.5
4
6
Strong breeze
38–49
25–31
22–27
Large branches in motion; whistling heard in telegraph wires; umbrellas used with difficulty
Large waves, extensive foam crests
3.0
4.0
5
7
Near gale
50–61
32–38
28–33
Whole trees in motion; inconvenience felt when walking against the wind
Foam blown in streaks across the sea
4.0
5.5
5–6
8
Gale
62–74
39–46
34–40
Twigs break off trees; generally impedes progress
Wave crests begin to break into spindrift
5.5
7.5
6–7
9
Strong gale
75–88
47–54
41–47
Slight structural damage (chimney pots and slates removed)
Wave crests topple over, spray affects visibility
7.0
10.0
7
10
Storm
89–102
55–63
48–55
Seldom experienced inland; trees uprooted; considerable structural damage
Sea surface largely white
9.0
12.5
8
11
Violent storm
103–117
64–72
56–63
Very rarely experienced; accompanied by widespread damage
Medium-sized ships lost to view behind waves; sea covered in white foam; visibility seriously affected
11.5
16.0
8
12
Hurricane
≥118
≥73
≥64
Devastation
Air filled with foam and spray; very poor visibility
≥14
—
9
Source: taken from The Royal Meteorological Society (n.d.).
Table 1.2 describes three units of measurement including the knot (often abbreviated to ‘kt’, or ‘kts’ if plural) which may be less familiar. This is a unit of speed derived from the days of sailing ships when sailors would use a long length of rope to which pieces of wood would be tied using knots at regular intervals which would be paid out from the stern (rear) of a ship as it travelled for a defined period of time determined using an hourglass. The number of knotted pieces of wood paid out in this time period would be used to calculate the speed of the ship. The modern knot is defined as one nautical mile (1.852 km) per hour which is equivalent to 0.514 ms−1. This unit is still used to describe the speed of ships or aircraft and is sometimes used by meteorological agencies to measure wind speed, though the unit of ms−1 is more widely used nowadays.
These early wind measurements were primarily of interest for mariners where the wind was an important source of motive power. Today, it is generally only leisure shipping which still uses the wind to provide propulsion. However, now our attention has turned to use of the wind for generating electrical power and the quest to decarbonise the energy system. In this book, we look at the science of wind resource assessment from the point of view of measurement and modelling.
1.2 The Need for Wind Resource Assessment
The need for accurate assessment of the wind conditions at a site has been driven by the rapid expansion of wind power worldwide. By the end of 2020, a total capacity of 743 GW had been installed around the globe (Lee and Zhao, 2021). The global weighted-average installed cost for onshore wind energy in 2020 was $1355 kW−1 (IRENA, 2020). For a 100 MW wind farm this equates to a total investment of $140 million. The equivalent cost offshore is somewhat higher at around $3185 kW−1 (though costs have fallen significantly) and a ‘typical’ offshore wind farm may have a combined capacity approaching 1000 MW. This equates to an investment cost of just over $3 billion. In terms of the levelised cost of energy, the most recent figures are stated to be $0.041 kW−1 onshore and $0.084 kW−1 offshore (IRENA, 2020). A well-used measure of the operational efficiency of a wind farm is that of the capacity factor which is the ratio of the long-term power output of a farm to its total rated power capacity. A typical onshore wind farm may have a capacity factor of 25–35%, and an offshore wind farm may exceed 50%. Figure 1.2 shows the dependence of capacity factor on average wind speed (Watson et al., 2015) based on a Rayleigh distribution of wind speeds (wind speed distributions are discussed in Chapter 4). Assuming an onshore average hub height wind speed of 7 ms−1, and a value of 10 ms−1 offshore, a 5% uncertainty in the long-term wind speed estimate represents an uncertainty of 9% in the capacity factor for the onshore farm and 5% for the offshore site in this example.
Figure 1.2 Capacity factor of a Vestas V80 2MW wind turbine as a function of wind speed. Source: adapted from Watson et al. (2015).
This uncertainty equates to $24 million for a 100 MW onshore wind farm and $460 million for a 1000 MW offshore wind farm. It is clear from these numbers that a modest investment to reduce the uncertainty in a long-term mean wind speed estimate for a site can have a large impact in terms of the viability of a wind farm, where frequently the lowest projected energy yield given the expected uncertainties is used in a financial investment decision.
1.3 A Brief Overview of the Wind Resource Assessment Process
To assess the long-term energy yield of a wind farm is a multistep process and is shown schematically in Figure 1.3. Initially, a wind atlas may be used to assess the approximate wind resource over a wide area to identify a candidate wind farm site. This site will then be the subject of a more comprehensive measurement campaign based on measurements made onsite at one or more masts covering the area of potential interest. A measurement campaign, for practical reasons, may last only 1–2 years, which is insufficient to capture the climatological variability that would be expected over the lifetime of a typical wind farm (20–25 years). To account for this, some form of long-term correction is required. This, in turn, will only give detailed information about the long-term resource at the mast (or masts). The next step is to extrapolate the wind speed conditions from the mast (or masts) over the wider area within which a wind farm is expected to be built. This area will depend on several factors, including the area available; the expected total capacity of the wind farm, which will be driven by factors that may be economically related or linked to the capacity of the nearby grid; and the size of the turbines. The spatial extrapolation of the wind speed, both horizontally and vertically, to take account of different potential turbine sizes, is generally carried out using a physical or statistical model which can be of various levels of sophistication depending on the complexity of the terrain and the size of the wind farm, and thus the size of the investment. The final step is to determine the optimum siting of the turbines considering the spatial variation in the wind speed and the aerodynamic interactions between the turbines (i.e. array effects, including wakes). This last step may require an iterative process, so it may be that a simpler, faster numerical, or physically based empirical model is used first that may be slightly less accurate than a more sophisticated model but is accurate enough to determine an optimum layout. A final stage of fine-tuning the layout and determining the expected long-term energy yield may then be carried out using a more sophisticated but computationally intensive numerical model. It should be noted that the emphasis of this book is on assessing the long-term wind speed rather than the energy yield and so only the steps within the dashed box in Figure 1.3 are covered in the remaining chapters. Array effects are relatively complex and warrant a detailed book in themselves. It is for this reason that they are considered outside the scope of this book.
Figure 1.3 The wind resource assessment process. The scope of this book is shown within the dashed box. Source: the 3D surface plots were reproduced from ReSoft WindFarm software (resoft.co.uk) / with permission of Alan Harris.
1.4 Layout of this Book
The remainder of this book describes the important elements in the wind resource assessment process. Chapter 2 introduces the basic atmospheric properties of the wind and the structure of the atmospheric boundary layer. Chapter 3 looks at the measurement of the key variables in the process of assessing wind resource, including a description of the most commonly used instruments. Chapter 4 considers the temporal variability in wind speed over different scales, ranging from years to less than a second, and their importance in resource assessment and reviews the most popular mathematical distributions which are used to characterise the statistical properties of the wind for the long and short term. Chapter 5 describes how the spatial variation of the wind is modelled, starting with the governing equations for fluid flow and the models which are based on these. Chapter 6 looks at empirical and semi-empirical modelling approaches which can be used to predict spatial variation in the wind in specific cases. Chapter 7 reviews some of the well-known orographic test cases which have been used to validate and test different wind resource model predictions. Chapter 8 considers statistical approaches which are used to extrapolate short-term wind speed measurements to the long-term and techniques to interpolate spatial data that do not require complex physical models. Chapter 9 describes the technique of reanalysis to reconstruct long-term wind speed time series. It also looks at some of the more popular reanalysis datasets, and their use in wind resource assessment, including the production of wind atlases. Chapter 10 reviews mesoscale phenomena and how they might potentially affect the variability of the local wind resource. Chapter 11 reviews the evidence for potential future trends in global wind speed patterns and how this may affect wind resource assessment.
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