GUIDE WAVE ANALYSIS AND FORECASTING - WMO

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110 (e) Insurance inquiries, damage or loss of property at sea. The type of wave climate analysis required depends on the particular application, but includes the following: (a) Long-return-period wave heights (e.g. 100 years), associated periods and directions at sites of interest and over regions; (b) Percentage frequency of wave heights or wave periods by wave direction; (c) Exceedance analyses for wave height and wave period; (d) Persistence analysis for wave heights (or wave periods) greater (or less) than selected thresholds; (e) Joint distribution of significant wave height and wave period; (f) Time series plots of wave heights and wave periods; (g) Relationships between significant wave height and maximum wave height and crest height. In order to produce many of these statistical analyses a long-time series of wave data at one or more locations is required. The information used to produce wave climatologies comes primarily from two sources: (a) wave measurements and observations, (b) wave hindcasts. Each of these will be discussed in more detail in the following paragraphs. Figure 9.6 — Cumulative distribution of H s values from GEOSAT transects of a 2° x 2° bin south of New Zealand from November 1986 to October 1989 plotted on a FT-I scale. The line was fitted by maximum likelihood and extrapolated to give a 50-year return wave height, assuming three-hour values, of 16.5 m (courtesy Satellite Observing Systems, Godalming, UK) -38 -40 -42 -44 -46 -48 -50 -52 -54 -56 Detailed location of data cell 156 158 160 162 164 166 168 170 172 174 176 GUIDE TO WAVE ANALYSIS AND FORECASTING Probability (FT-I scale) 0.9999 0.999 0.99 0.9 0.5 0.1 9.6.1 Climatologies from wave measurements and observations For climatological purposes wave data has traditionally been derived from two major sources: (a) visual observations from vessels participating in the Voluntary Observing Ships scheme; (b) measurements from buoys and ships. Wave data has also in recent years become available from satellite sensors and marine radar, but the frequency of observation, length of record and data quality have until recently limited their routine use in climatology studies. Some investigations into the use of satellite radar altimeter measurements to estimate significant wave height distributions and extreme values have been carried out (Carter et al., 1994). Figure 8.5 (from D. Cotton, Southampton Oceanography Centre) shows the global mean significant wave height distribution from January to March 1996 derived from Topex data. Carter et al. (1991) analysed the global variations in monthly mean wave heights using one year of GEOSAT data. At present, significant wave height data are available globally from GEOSAT (1986–89), Topex/Poseidon (1992–), ERS-1 (1991–) and ERS-2 (launched April 1995), see, for example, Young and Holland (1996), in which detailed climatologies of the world’s oceans are derived from GEOSAT data. Already these data are giving us highquality wave climate information of particular value in GEOSAT data: 47°S, 167°E Loc = 3.139 Scale = 1.119 H s50 = 16.451 0.01 0 5 10 Hs (m) 15 20 50 yrs
emote, data-sparse areas. However, these data are also valuable in giving supplementary information in data-rich areas on the spatial variability, in extending measured series in time and for investigating their temporal representativeness for long-term conditions. A review of applications of satellite data is given in Barstow et al. (1994 (b)). As further years of data from GEOSAT and later satellites, such as ERS-1 and Topex-Poseidon, become available, such analyses will provide invaluable information on wave climate. Papers on estimating extreme values from altimeter data have been published by Tournardre and Ezraty (1990), Carter (1993) and Barstow (1995). Figure 9.6 shows an estimate of wave height in an area 46°–48°S, 166°–168°E based on three years of GEOSAT data, with an estimated 50-year return value of 16.5 m. The method used and other figures of the wave climate around New Zealand are given in Carter et al. (1994). In addition, it is soon expected that SAR data will become more important. A list of available databases composed of visual observations and measurements of waves is shown in Table 9.2. A detailed description of wave observations and measurements is given in Chapter 8. While the data measured by buoys is considered to be of excellent quality, its utility for wave climatology is severely limited. Wave data collection is expensive and logistically difficult. As a result, there are often insufficient data to adequately define the wave climatology over a region, or for deriving with confidence any climate statistic which requires a long-time series (e.g. extremal analysis). Directional wave data from buoys are not yet common but have increased considerably in the last decade. Buoy data can be used for some operability studies, as long as due consideration is given to ensuring that the period of instrumental record is representative of the longer term climate. Buoy data are usually inappropriate for extremal analysis due to short record lengths. Buoy data are particularly useful for validating (or calibrating) numerical models and remote sensing algorithms. Shipborne Wave Recorders are more robust than wave buoys and, fitted in Ocean Weather Ships and moored Light Vessels since the 1960s, have provided several years of data at a few locations (see, for example, Figure 9.5). Shipborne Wave Recorder data, as well as visual estimates, have been used to investigate an apparent upward trend in average wave heights in parts of the North Atlantic by one to two per cent per year over the last few decades (Neu, 1984; Carter and Draper, 1988; Bacon and Carter, 1991). It is likely that long-term monitoring of wave climate trends can now be carried out by satellite altimeters provided that the data are validated and corrections applied to make data from different satellites comparable. Visual wave observations from ships have associated problems of quality and consistency (Laing, 1985) which reduce their utility for climatology. In addition, the fact that most observations are from transient ships makes their use for time-series type applications of climate virtually impos- WAVE CLIMATE STATISTICS 111 sible. Ship data are most useful for climatological purposes when treated as an ensemble for regional descriptive climate applications such as “downtime” estimates for offshore operations. Transient ship data are inappropriate for extremal or time-series analysis. In practice, wave climate studies usually use data from a combination of sources for specifying offshore conditions, and employ shallow water modelling if the site of interest is near the coast. 9.6.2 Wave hindcasts There is no doubt that hindcasts are playing an increasing role in marine climatology. Most recent wave climatologies, especially regional climatologies, are based on hindcast data. The same applies to design criteria produced by offshore oil and gas exploration and production companies, and the regulatory authorities in many countries around the world. The reason is simple: the costs involved in implementing a measuring programme, especially on a regional basis, and the period spent waiting for a reasonable amount of data to be collected, are unacceptable. Therefore, given the demonstrated ability of the present generation of spectral ocean wave models, the timeliness and relatively lower cost of hindcast data becomes quite attractive. The 2nd (Canadian Climate Center, 1989) and 3rd (Canadian Climate Center, 1992 (a)) International Workshops on Wave Hindcasting and Forecasting identified no fewer than 12 separate recentlycompleted regional hindcasts around the world. A number of other such studies are also under way. The quality of hindcast data (or at least confidence in it) can be improved by validating the results against available in situ measurements or satellite altimeter Hs data (for instance, this is currently being carried out in a European Union project, WERATLAS, in which a wave energy resource atlas is being constructed based on ECMWF’s WAM model archive and validated by buoy data and altimeter data, Pontes et al., 1995). Hindcasts can be classified into two basic categories: continuous hindcasts and discrete storm hindcasts; a third possibility arises as a hybrid of the two. The features of each type are described briefly in the following paragraphs. 9.6.2.1 Continuous hindcasts Continuous hindcasts are usually the most useful form of hindcast data, representing a long-term, uniform distribution in space and time of wind and wave information. The time spacing is typically six or 12 hours. The database is then suitable for all manner of statistical analysis, including frequency analysis and persistence, at a single location or over a region. If the period of the hindcast is sufficiently long, the database can also be used for extremal analysis to long-return periods. The major drawback to continuous hindcasts is the time and expense involved in their production. The generation of up to 20 years of data at six-hour intervals requires an enormous amount of effort, especially if it involves the
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WORLD METEOROLOGICAL ORGANIZATION G
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© 1998, World Meteorological Organ
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IV Chapter 7 - WAVES IN SHALLOW WAT
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ACKNOWLEDGEMENTS The revision of th
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VIII has been included particularly
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X considerable attention. Annex III
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2 η Crest Zero level a H = 2a a Tr
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4 Figure 1.5 — Paths of the water
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6 When waves propagate into shallow
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8 1.3.2 Wave groups and group veloc
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10 wavelengths in a given sea state
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12 for instance, a frequency of 0.1
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14 in which E(f) is the variance de
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16 2.1.1 Wind and pressure analyses
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18 GUIDE TO WAVE ANALYSIS AND FOREC
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20 Figure 2.2(a) (right) — Usual
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22 As a quick approximation of ocea
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24 GUIDE TO WAVE ANALYSIS AND FOREC
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26 Gr G Gr ∇p C Cnf ∇p C Cnf
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28 POINT C — The effect of warm a
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30 a general sense, and can be appl
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32 Free atmosphere Ekman layer Cons
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3.1 Introduction This chapter gives
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ange of directions. Also, waves at
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small enough that swell can survive
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Figure 3.7 — Structure of spectra
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4.1 Introduction CHAPTER 4 WAVE FOR
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necessary to forecast waves for a p
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TABLE 4.4 Additional wave informati
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TABLE 4.6 Ranges of swell periods a
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this situation, the angular spreadi
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the energy flux is c gH 2 . This is
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α0, degrees Solution: 80° 70° 60
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5.1 Introduction National Meteorolo
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Page 71 and 72: Frequency 0.050 0.067 0.083 0.100 0
Page 73 and 74: The models may differ in several re
Page 75 and 76: The CH class may include many semi-
Page 77 and 78: 6.1 Introductory remarks Since the
Page 79 and 80: in some applications, the zero up/d
Page 81 and 82: OPERATIONAL WAVE MODELS 71 Figure 6
Page 83 and 84: give large differences in the compa
Page 85 and 86: Wind (m/s) Waves (m) Buoy/GSOWM Wav
Page 87 and 88: TABLE 6.2 Numerical wave models ope
Page 89 and 90: TABLE 6.2 (cont.) Country Name of m
Page 91 and 92: 7.1 Introduction The evolution of w
Page 93 and 94: water boundary into shallow water.
Page 95 and 96: eflected waves, although the latter
Page 97 and 98: Energy (cm 2 /Hz) Energy (cm 2 /Hz)
Page 99 and 100: 8.1 Introduction Wave data are ofte
Page 101 and 102: matter and timing the intervals bet
Page 103 and 104: a Directional Waverider (Barstow et
Page 105 and 106: 80°N 60°N 40°N 20°N 0° -20°S
Page 107 and 108: a combination of sea-surface temper
Page 109 and 110: 8.7.1 Digital analysis of wave reco
Page 111 and 112: 9.1 Introduction Chapter 8 describe
Page 113 and 114: Presentation of data and wave clima
Page 115 and 116: the monsoon season of June to Septe
Page 117 and 118: Probability of non-exceedance case
Page 119: individual wave height derived usin
Page 123 and 124: TABLE 9.2 (cont.) Country Source of
Page 125 and 126: For a storm hindcast, select the mo
Page 127 and 128: TABLE 9.3 (cont.) Country Model use
Page 129 and 130: ANNEX I ABBREVIATIONS AND KEY TO SY
Page 131 and 132: Abbreviation/ Definition Symbol MOS
Page 133 and 134: CODE FORM: D . . . . D or IIiii* SE
Page 135 and 136: sensors, spectral peak wave period
Page 137 and 138: cs1cs1 The ratio of the spectral de
Page 139 and 140: M jM j n mn m n smn sm n bn bn b P
Page 141 and 142: 1744 Im Indicator for method of cal
Page 143 and 144: The following distributions are bri
Page 145 and 146: 4. Weibull distribution Probability
Page 147 and 148: 8. Fisher-Tippett Type I (FT-I) (or
Page 149 and 150: ANNEX IV THE PNJ (PIERSON-NEUMANN-J
Page 151 and 152: ANNEX IV 141 Duration graph. Distor
Page 153 and 154: REFERENCES Abbott, M. B., H. H. Pet
Page 155 and 156: REFERENCES 145 Carter, D. J. T., P.
Page 157 and 158: REFERENCES 147 Gumbel, E. J., 1958:
Page 159 and 160: REFERENCES 149 Maat, N., C. Kraan a
Page 161 and 162: Slutz, R. J., S. J. Lubker, J. D. H
Page 163 and 164: SELECTED BIBLIOGRAPHY CERC, 1984: C
Page 165 and 166: 156 Energy . . . . . . . . . . . .
Page 167 and 168: 158 steepness . . . . . . . . . . .
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GUIDE WAVE ANALYSIS AND FORECASTING - WMO

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