1. Introduction
The determination of extreme-value estimates of environmental parameters such as wind speed and wave height is a common requirement for many coastal and offshore applications. In the present context, “extreme value” is used to describe the statistical estimate of the wind speed or wave height, which, for instance, may be expected to be exceeded once in, say, 100 years. That is, the 100-yr return period wind speed or wave height. Alternatively, it can be described as the wind speed or wave height that has a probability of occurrence of 0.01 in any year.
The typical approach for the estimation of such extreme-value parameters is to analyze a long-duration time series of measured wind speed or wave height. As the record is almost always shorter than the desired return period, the procedure used is to fit a chosen form for the probability distribution function (PDF) of the recorded data and then extrapolate to the desired probability level (e.g., 0.01 for a 100-yr return level).
A number of practical challenges arise in this form of analysis. As the focus is on the extreme-value “tail” of the PDF, how well this part of the PDF is defined and how well the, often arbitrary, analytical form for the PDF fits the data is critical. Obviously, it is desirable to reduce the extent of the required extrapolation of the PDF as much as possible. As a result, there is a strong requirement to have as long of a measured time series as possible at the location or locations of interest.
The most obvious approach to obtain long-duration measured records is to use buoy or fixed offshore platform data. Although suitable long-duration records exist at specific sites, the key shortcoming of such data is that it has very limited spatial distribution and hence is seldom at the location required. One approach to overcome both the temporal duration and spatial distribution issues is to use numerical model data. Indeed, long-duration reanalyses of wind speed and wave height, which include data assimilation from satellites, are available [e.g., ERA-40 (Uppala et al. 2005) and ERA-Interim (Dee et al. 2011)]. Naturally, these datasets are only as good as the models used to produce them. Although present-day atmospheric circulation and surface wave models are remarkably reliable, their performance under extreme conditions is still limited.
The advent of Earth-observing satellites has provided a long (approximately 30 year) record of global wind speed and wave height, and a number of previous studies (Alves and Young 2003; Young et al. 2012) have examined the suitability of such data for extreme-value analysis (EVA). Although these studies have shown potential, they highlight a number of issues with such data. These issues include the following: as data invariably come from multiple satellites, careful long-term calibrations are required; the datasets considered were not sufficiently long to apply statistically sound approaches to extrapolation of the PDF; there were questions about the extreme-value performance of such satellite systems; and the spatial separation of satellite ground tracks means that radar altimeters may undersample extremes.
The present study examines a long-term (almost 30 year) calibrated and validated dataset of wind speed and wave height obtained from both altimeter and radiometer systems. As will be shown, the duration of the record is now such that the peaks over threshold (PoT) method can be used for EVA of the data. The resulting global distributions are consistent with the limited point measurements of extreme wind speeds and wave heights from buoys, as well as with numerical reanalyses. In addition, for the first time, global distributions clearly show dominant storm tracks and tropical cyclone activity. As radiometer systems simultaneously measure a broad swath of the ocean, they have the potential to significantly enhance the quantity of data available and hence address issues of perceived undersampling. However, limitations in the performance of radiometer data when applied to EVA will also be highlighted.
The arrangement of the paper is as follows. Section 2 provides a brief review of previous studies of global extreme-value estimates and the statistical approaches adopted. This is followed in section 3 by a description of the satellite dataset used in the present analysis and its calibration, particularly under extreme conditions. Section 4 compares extreme-value estimates from the present satellite measurements with buoy observations. A discussion of global distributions of extreme-value wind speed and wave height using a variety of statistical techniques and both satellite data types (altimeter and radiometer) is provided in section 5. Finally, discussion of the results and conclusions are provided in section 6.
2. Global estimates of extreme wind speed and wave height
a. Extreme-value theory
As outlined by Goda (1988) and Coles (2001), the aim of EVA is to estimate the probability distribution of the extreme values of a variable from a record of empirical samples. To achieve valid estimates of the extremes, the data should be independent and identically distributed (IID). For the present applications, the requirement of independence means that successive observed data points should be statistically uncorrelated. As a result, there should not be multiple data points associated with the same storm. As typical storms may have durations of many hours, this means that successive data points may need to be separated by up to 48 h to ensure independence (Lopatoukhin et al. 2000; Caires and Sterl 2005; Vinoth and Young 2011). The requirement to be identically distributed is satisfied when data points in a sample show a common parent distribution in a population. Should an area be subjected to quite different meteorological phenomena (e.g., trade winds and tropical cyclones), it is likely that these systems will have quite different PDFs and the data should be partitioned and each PDF considered separately (Vinoth and Young 2011). It should be noted that as the present dataset does not provide a mechanism to separate independent storm climate systems, no attempt has been made to partition the data.
There are three general approaches to EVA that have been used in wind/wave applications—the initial distribution method (IDM; Goda 1988, 1992; Ochi 1992; Tucker 1991; Lopatoukhin et al. 2000; Vinoth and Young 2011), the annual maximum method (AMM; Coles 2001), and the PoT (Goda 1992; Ferreira and Soares 1998; Van Gelder and Vrijling 1999; Alves and Young 2003; Vinoth and Young 2011).
1) IDM
There are still further limitations with the IDM approach. First, in most cases, the approach violates the requirement for independent and identically distributed data. When using in situ data measured at 1- or 3-h intervals, it is almost certain that such data are correlated. As the distribution is fitted to the full PDF, it is highly likely that data at the peak of the PDF (mean conditions) and that in the extreme tail (storms) will be from different meteorological events and hence not identically distributed. Finally, the fit of the chosen PDF to the data is always dominated by the bulk of the data, which is near the peak of the PDF, rather than the extreme tails where interest lies. Hence, the IDM tends to be an extrapolation of these more benign conditions than a model of the extremes. Despite these very significant shortcomings, the IDM has been extensively used (Goda 1992, 1988; Tucker 1991; Ochi 1992), as it is the only alternative when only short time series are available (i.e., less than 15 years). In the case of Earth-observing satellites, the observational record has been so short that previous attempts at EVA have only yielded reasonable results when the IDM has been used (Alves and Young 2003; Chen et al. 2004; Challenor et al. 2005; Wimmer et al. 2006; Vinoth and Young 2011).
2) AMM
Type 1 or Gumbel distribution
Type 2 or Fréchet distribution
Type 3 or Weibull distribution
3) PoT
b. Previous global extreme-value studies
The literature on extreme-value studies of wind speed and wave height is extensive. The vast majority of these studies, however, refer to point locations. We have not attempted to review this literature here, rather we concentrate on the more limited global studies. As a result, attention is confined to either numerical reanalysis model or satellite datasets.
A number of long-duration reanalyses combining numerical models of the atmosphere with data assimilation are now available in public archives. These include ERA-40 (Uppala et al. 2005) and ERA-Interim (Dee et al. 2011), for both of which a wave model (WAM; Hasselmann et al. 1988) is incorporated into the model system. Because of the long duration of the reanalyses, these are attractive for EVA (e.g., Caires and Sterl 2005; Sterl and Caires 2005). The length of the reanalysis model records allows the use of threshold methods (PoT) to determine global distributions of the 100-yr return period wind speed 
An innovative alternative to the use of reanalysis data is to create very long-duration equivalent time series using forecast ensembles (Breivik et al. 2013, 2014; Meucci et al. 2018). By considering ensemble forecasts at long forecast lead times (9–10 days), synthetic datasets of durations longer than 300 years can be formed. With such data, EVA can be performed without the need of any assumed PDF form (the probability level is “in sample” and can be determined directly from the ranked data). This effectively removes issues of extrapolation to the desired probability level, but questions about stationarity and tail biases (the ability of a coarse model to represent extreme events) remain.
As the satellite records lengthened, a number of studies investigated global values of extreme wind speed and wave height. These include Alves and Young (2003; 10 years of data), Chen et al. (2004; 8 years of data), Challenor et al. (2005; 11 years of data), Wimmer et al. (2006; 11 years of data), and Vinoth and Young (2011; 23 years of data). Vinoth and Young (2011) investigated the use of PoT analyses but concluded that the time series was too short and hence adopted an IDM analysis, as did all the previous studies. Although these analyses produce plausible global distributions of 
3. Satellite dataset
In the present study, we will use the calibrated and validated satellite database of Young et al. (2017). This database consists of altimeter (
A full description of the manner in which altimeters measure wind speed and wave height and radiometers measure wind speed can be found in Young et al. (2017) and Young and Donelan (2018). These details are not repeated here. However, there are a number of issues that are important when such data are subjected to EVA. Altimeters are “nadir looking” instruments and measure along a line directly below the satellite. The footprint is approximately 8–10 km in diameter with roughly one measurement per second. As a result, altimeters have very good along-track resolution (approximately every 10 km) but relatively low across-track measurement density. Depending on the orbit, ground tracks are 100–400 km apart at the equator. The exact repeat cycle or time until the satellite repeats the same ground track varies from 3 to 10 days. As a result, altimeters in polar orbits observe the globe from about 80°S to 80°N but may undersample or completely miss small- to medium-size storms. Although there have been very few studies on the impact of rain on altimeter measurements, it appears that they are not greatly affected by rain (Young and Donelan 2018).
In contrast, radiometers (which measure only wind speed), measure over a broad swath, approximately 1400 km wide. Across this swath, they provide data at approximately 25 km resolution. Therefore, a typical radiometer in a polar orbit will visit most points on Earth’s surface twice per day. At a particular location, the radiometer will typically produce approximately 30 times more data than an altimeter. Hence, radiometers should be much less affected by undersampling than altimeters. However, radiometer measurements are heavily influenced by rain and typically cannot measure under heavy rain conditions. As a result, it is very common for radiometers to miss the peaks of storms where there is commonly heavy rain. As a result, this may introduce a “fair weather” bias in radiometer data (Young et al. 2017; Young and Donelan 2018).
In addition to calibrating the instruments, Young et al. (2017) also examined their performance at extreme conditions. This was accomplished by examining quantile–quantile (QQ) plots between altimeter/radiometer and buoy data, as well as QQ plots between altimeter and radiometer winds at crossover points. They concluded that compared to buoys, altimeters measure 
In contrast to altimeters, radiometers appeared to overestimate wind speed compared to buoys above 20 m s−1. However, there is evidence (Large et al. 1995; Zeng and Brown 1998; Taylor and Yelland 2001; Howden et al. 2008; Bender et al. 2010; Jensen et al. 2015) that buoys may underestimate extreme wind speeds and waves due to tilting of the buoy and sheltering by large waves. It is therefore questionable to assume that buoys represent “ground truth” under extreme conditions.
As high-wind performance is critical for EVA, we therefore searched for alternative wind observations to conventional buoys. The obvious alternative is offshore platform data. Data were obtained from the Norwegian Meteorological Institute for offshore oil platforms. The locations where data were available are shown in Fig. 1. Offshore platform data are known to have a number of issues, most notably flow distortion caused by the structure. However, this dataset has been extensively studied by the Norwegian oil industry, and power-law corrections were available for each of the anemometers to correct the data to a standard reference height of 10 m. The same matchup criteria adopted by Young et al. (2017) were used. That is, the satellite data needed to be within 50 km of the platform, and the mismatch in measurement time must be less than 30 min. One of the challenges in carrying out a high wind speed calibration is obtaining sufficient data under these conditions (i.e., there are few collocated observations at high winds).
Locations of offshore platforms used to obtain anemometer data for high wind speed calibration of the radiometer instruments.
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Locations of offshore platforms used to obtain anemometer data for high wind speed calibration of the radiometer instruments.
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Locations of offshore platforms used to obtain anemometer data for high wind speed calibration of the radiometer instruments.
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Radiometer–platform anemometer comparisons: (left) scatterplots and (right) QQ plots with (a),(b) no high wind speed correction and (c),(d) high wind speed correction [(7)].
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Radiometer–platform anemometer comparisons: (left) scatterplots and (right) QQ plots with (a),(b) no high wind speed correction and (c),(d) high wind speed correction [(7)].
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Radiometer–platform anemometer comparisons: (left) scatterplots and (right) QQ plots with (a),(b) no high wind speed correction and (c),(d) high wind speed correction [(7)].
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
4. Validation of satellite EVA analysis against buoys
Following Alves and Young (2003) and Vinoth and Young (2011), the global data were binned into 2° × 2° bins. In section 5, this binned dataset is used to investigate global distributions of extreme values. Here, we validate the results against NDBC buoy data (Evans et al. 2003). We select the same 10 deep-water NDBC buoys used by Vinoth and Young (2011; see their Fig. 2). These buoys were selected, as they are all more than 200 km from shore, have a water depth exceeding 300 m, and were operational for the full duration of the combined satellite datasets (1984–2014).
The validation approach used by Vinoth and Young (2011) consisted of determining extreme-value estimates (
As we have longer time series, we have adopted a more challenging validation approach. With 30 years of data, PoT estimates from buoys can be obtained with reasonable confidence. Therefore, we take the PoT estimates from the buoys as the baseline (ground truth) and compare satellite estimates from PoT and IDM with these values. An important issue in applying the PoT analysis is to select an appropriate threshold parameter [A in (5)]. To investigate the sensitivity of extreme-value estimates from the GPD [(5)] to the threshold value, a 2° × 2° region centered on 40°N, 180° (North Pacific) was selected as a representative test point. The values of 
Values of (a) 
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Values of (a)
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Values of (a)
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
When applying a PoT analysis to the satellite data, care must still be exercised to ensure that the data taken above the selected threshold are independent. In the case of altimeter passes, this is seldom an issue as, even with multiple satellites in orbit, satellite passes at a location are typically separated by at least two days (48 h). Radiometer data are potentially more problematic, as a single radiometer will image each location twice a day (12-h separation). To test the sensitivity to these issues, data were filtered such that only values separated by chosen times were considered (e.g., data separated by 48 h). The calculated extreme values were quite insensitive to the chosen time separation, and, hence, data separated by a minimum of 48 h have been used here.
EVA of NDBC buoy and satellite data. Buoy data shown with italics.
As noted earlier, we have not presented IDM estimates for buoys. An examination of the results of Vinoth and Young (2011) shows that 
5. Global distribution of extremes
a. Altimeter PoT analysis
To investigate the global distribution of 
Figure 4 shows color-filled contour plots of 
Global values of (top) 
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Global values of (top)
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Global values of (top)
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Figure 4a shows the maxima of 
Storm tracks of tropical cyclones (and tropical low pressure systems) over the period 1984–2014, obtained from the IBTrACS data archive (Knapp et al. 2010). For clarity, only every second track is plotted.
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Storm tracks of tropical cyclones (and tropical low pressure systems) over the period 1984–2014, obtained from the IBTrACS data archive (Knapp et al. 2010). For clarity, only every second track is plotted.
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Storm tracks of tropical cyclones (and tropical low pressure systems) over the period 1984–2014, obtained from the IBTrACS data archive (Knapp et al. 2010). For clarity, only every second track is plotted.
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Figure 5 shows that North Atlantic and Pacific tropical storms track east to west across the tropics of each ocean basin, respectively, before turning north along the western boundary of each basin. Because of the small spatial scale of tropical cyclones and the relatively large distance between altimeter tracks, it is likely that these systems are undersampled in the present analysis. As such systems move north, they tend to increase in size, making it more likely that they are observed by the altimeter. This is clear in the region of the western North Atlantic, where extreme winds are predicted (Fig. 4a) north of 30°N, but there is no clear indication of tropical cyclones moving across the tropical regions of the Atlantic (east to west). In contrast, extreme winds along the western boundary of the Pacific are predicted as far south as 10°N. There is then a clear path of intense winds shown across the Pacific equatorial regions. North Pacific tropical cyclones (typhoons) tend to be larger in spatial extent than North Atlantic tropical cyclones (hurricanes; Knaff et al. 2014). They are also more frequent, as shown in Fig. 5, making them less affected by undersampling in the altimeter dataset. This explains why the east–west tropical track is clear in the western Pacific (10°N) but not the western Atlantic.
A number of other storm track features can also be seen in the values of 
The eastern side of the South Atlantic (off Africa) shows relatively low values of 
Many of the same features described above are also apparent in model calculations of 
Figure 4b shows color-filled contours of 
It is believed that Fig. 4 represents the first plausible published estimates of 
b. Radiometer PoT analysis
Because of the much higher data rates (30 times more data) the radiometer has the potential to address the undersampling issues noted above for altimeter 
Figure 6 shows color-filled contours of 
Global values of 
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Global values of
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Global values of
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
There are, however, a number of features in Fig. 6b that differ from the altimeter results. The largest values of 
In an attempt to address the issues raised above, an exponential (EXP) distribution was used with the PoT analysis rather than a GPD. The EXP distribution is a special case of the GPD [(5)] with k = 0. This produces an unbounded distribution but without the variability caused by having k determined by the fit to the data (which is problematic in the tail of the radiometer PDF). The resulting values of 
Global values of 
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Global values of
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Global values of
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Although the use of the EXP has produced results that vary spatially in a smooth manner, the spatial distributions are quite different from the altimeter GPD (and previously published results; Vinoth and Young 2011; Breivik et al. 2014). Although the high wind speed correction [(7)] was used for the data in Fig. 7, the magnitude of the values of 
The results of Figs. 6 and 7 indicate that, despite the greater sampling density provided by the radiometer, its inability to provide data during rain events introduces an unacceptable fair-weather bias for extreme-value applications.
c. IDM analysis
As noted earlier, all previous studies of extreme-value estimates from satellite data (altimeter) have opted for an IDM analysis. This is despite the many shortcomings of the approach outlined in section 2a (value of decorrelation scale D, independent and identically distributed data). As the present analysis provides, for the first time, stable estimates of both 
Figures 8a and 8b show 
Global values of (top) 
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Global values of (top)
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Global values of (top)
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Global values of 
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Global values of
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
Global values of
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
The values of 
Figures 10 and 11 show the PDFs for both wind speed and wave height, together with both IDM and PoT fits to the data. Figure 10 shows results from a location in the Pacific Ocean tropical cyclone belt (6°N, 214°E; Figs. 10a–d, 
The altimeter (left) PDF and (right) QQ plot at a 2° × 2° square centered on 6°N, 214°E (Pacific tropical cyclone belt) for wind speed 
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
The altimeter (left) PDF and (right) QQ plot at a 2° × 2° square centered on 6°N, 214°E (Pacific tropical cyclone belt) for wind speed
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
The altimeter (left) PDF and (right) QQ plot at a 2° × 2° square centered on 6°N, 214°E (Pacific tropical cyclone belt) for wind speed
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
As in Fig. 10, but for a 2° × 2° square centered on 14°N, 60°E (Horn of Africa).
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
As in Fig. 10, but for a 2° × 2° square centered on 14°N, 60°E (Horn of Africa).
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
As in Fig. 10, but for a 2° × 2° square centered on 14°N, 60°E (Horn of Africa).
Citation: Journal of Climate 32, 1; 10.1175/JCLI-D-18-0520.1
The PDFs for the Horn of Africa (Fig. 11) are clearly affected by the strong winds of the Somali/Oman CLLJ. The PDFs (both wind speed and wave height) are clearly bimodal and there are clearly two populations of wind speed and wave height. It is also clear that the high wind speed peak (Somali/Oman CLLJ) has a very sharp drop-off with increasing 
The comparisons between the PoT and IDM analyses above clearly show the limitations of the IDM approach. Although this approach has found favor when working with short datasets, the results shown here clearly indicate its significant theoretical and practical shortcomings. As long-duration satellite and model reanalysis datasets are now available, there seems little justification for its continued use.
d. Changing wind and wave climates
The above analysis assumes that the time series considered are stationary. That is, there is no change in the mean conditions over the approximately 30-yr measurement period. In addition, applying such extreme-value analysis to determine probable extremes also assumes that mean conditions will not change in the future. There is evidence to suggest that there have been changes in both wind and wave mean climate over this period (Young et al. 2011). In addition, there is also some evidence that extreme conditions have also changed over this period (Young et al. 2011, 2012). Further, model studies (Hemer et al. 2013) indicate that wave climate may also change in the future. At present, there is still a significant level of uncertainty in these trend estimations. The present estimates of both historical and future trends are relatively small (mean 
6. Conclusions
The present analysis outlines the application of extreme-value analysis to long-duration (30 year) global altimeter and radiometer datasets. In contrast to previous extreme-value analyses of satellite data, the dataset is sufficiently long to enable a PoT analysis to be undertaken. When applied to altimeter data for 
The greater data density provided by radiometer measurements offers the potential to address altimeter undersampling issues. However, issues associated with the radiometer inability to measure wind speed in heavy rain events appears to create an unacceptable “fair weather” bias at extreme wind speeds. This renders the radiometer data of 
Because of the relatively short duration of altimeter data, previous EVA studies have all used IDM analyses for EVA. The extended dataset presented here can now be successfully processed using the more theoretically sound PoT approach. The present analysis shows that the IDM yields quite biased estimates of extreme values and their spatial distributions. As the PoT approach can now be successfully applied to the available longer satellite datasets, there seems little reason for IDM analyses to be used in the future.
Acknowledgments
IRY gratefully acknowledges the support of the Australian Research Council through Grants DP130100215 and DP160100738. This support has been invaluable in completing this extensive study. The raw datasets used in the study were supplied by Globwave (altimeter and buoy) and Remote Sensing Systems (RSS; radiometer/scatterometer). Without these public domain archives, the study would have been extremely difficult. ØB gratefully acknowledges support from the Research Council of Norway through the ExWaMar project (Grant Number 256466) and the European Research Area for Climate Services through the WINDSURFER project.
REFERENCES
Aarnes, O. J., Ø. Breivik, and M. Reistad, 2012: Wave extremes in the northeast Atlantic. J. Climate, 25, 1529–1543, https://doi.org/10.1175/JCLI-D-11-00132.1.
Aarnes, O. J., S. Abdalla, J.-R. Bidlot, and Ø. Breivik, 2015: Marine wind and wave height trends at different ERA-Interim forecast ranges. J. Climate, 28, 819–837, https://doi.org/10.1175/JCLI-D-14-00470.1.
Abdalla, S., 2007: Ku-band radar altimeter surface wind speed algorithm. Proc. Envisat Symp. 2007, Montreux, Switzerland, European Centre for Medium-Range Weather Forecasts, 463250, https://earth.esa.int/envisatsymposium/proceedings/sessions/3E4/463250sa.pdf.
Alves, J. H. G. M., and I. R. Young, 2003: On estimating extreme wave heights using combined Geosat, Topex/Poseidon and ERS-1 altimeter data. Appl. Ocean Res., 25, 167–186, https://doi.org/10.1016/j.apor.2004.01.002.
Anderson, C. W., D. J. T. Carter, and P. D. Cotton, 2001: Wave climate variability and impact on offshore structure design extremes. Shell International Rep., 88 pp.
Bender, L. C., III, N. Guinasso Jr., J. N. Walpert, and S. D. Howden, 2010: A comparison of methods for determining significant wave heights—Applied to a 3-m discus buoy during Hurricane Katrina. J. Atmos. Oceanic Technol., 27, 1012–1028, https://doi.org/10.1175/2010JTECHO724.1.
Breivik, Ø., O. J. Aarnes, J.-R. Bidlot, A. Carrasco, and Ø. Saetra, 2013: Wave extremes in the northeast Atlantic from ensemble forecasts. J. Climate, 26, 7525–7540, https://doi.org/10.1175/JCLI-D-12-00738.1.
Breivik, Ø., O. J. Aarnes, S. Abdalla, J.-R. Bidlot, and P. A. E. M. Janssen, 2014: Wind and wave extremes over the world oceans from very large ensembles. Geophys. Res. Lett., 41, 5122–5131, https://doi.org/10.1002/2014GL060997.
Caires, S., and A. Sterl, 2005: 100-year return value estimates for ocean wind speed and significant wave height from the ERA-40 data. J. Climate, 18, 1032–1048, https://doi.org/10.1175/JCLI-3312.1.
Castillo, E., 1988: Extreme Value Theory in Engineering. Academic Press, 389 pp.
Challenor, P. G., W. Wimmer, and I. Ashton, 2005: Climate change and extreme wave heights in the North Atlantic. Proc. 2004 Envisat and ERS Symp., Salzburg, Austria, European Space Agency, SP-572.
Chen, G., S.-W. Bi, and R. Ezraty, 2004: Global structure of extreme wind and wave climate derived from TOPEX altimeter data. Int. J. Remote Sens., 25, 1005–1018, https://doi.org/10.1080/01431160310001598980.
Coles, S., 2001: An Introduction to Statistical Modelling of Extreme Value Theory. Springer, 209 pp.
Cooper, C. K., and G. Z. Forristall, 1997: The use of satellite data to estimate extreme wave climate. J. Atmos. Oceanic Technol., 14, 254–266, https://doi.org/10.1175/1520-0426(1997)014<0254:TUOSAD>2.0.CO;2.
Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828.
Evans, D., C. L. Conrad, and F. M. Paul, 2003: Handbook of automated data quality control checks and procedures of the National Data Buoy Center. NOAA National Data Buoy Center Tech. Doc. 03-02, 44 pp.
Ferreira, J. A., and C. G. Soares, 1998: An application of the peaks over threshold method to predict extremes of significant wave height. J. Offshore Mech. Arct. Eng., 120, 165–176, https://doi.org/10.1115/1.2829537.
Goda, Y., 1988: On the methodology of selecting design wave height. Proc. 21st Int. Conf. on Coastal Eng., Malaga, Spain, American Society of Civil Engineers, 899–913.
Goda, Y., 1992: Uncertainty in design parameter from the viewpoint of statistical variability. J. Offshore Mech. Arct. Eng., 114, 76–82, https://doi.org/10.1115/1.2919962.
Hasselmann, S., and Coauthors, 1988: The WAM Model—A third generation ocean wave prediction model. J. Phys. Oceanogr., 18, 1775–1810, https://doi.org/10.1175/1520-0485(1988)018<1775:TWMTGO>2.0.CO;2.
Hemer, M. A., Y. Fan, N. Mori, A. Semedo, and X. L. Wang, 2013: Projected changes in wave climate from a multi-model ensemble. Nat. Climate Change, 3, 471–476, https://doi.org/10.1038/nclimate1791.
Howden, S., D. Gilhousen, N. Guinasso, J. Walpert, M. Sturgeon, and L. Bender, 2008: Hurricane Katrina winds measured by a buoy-mounted sonic anemometer. J. Atmos. Oceanic Technol., 25, 607–616, https://doi.org/10.1175/2007JTECHO518.1.
Jensen, R. E., V. R. Swail, R. H. Bouchard, R. E. Riley, T. J. Hesser, M. Blaseckie, and C. MacIsaac, 2015: Field Laboratory for Ocean Sea State Investigation and Experimentation: FLOSSIE: Intra-measurement evaluation of 6N wave buoy systems. 14th Int. Workshop on Wave Hindcasting and Forecasting and Fifth Coastal Hazard Symp., Key West, FL, WMO/IOC JCOMM, A1, http://www.waveworkshop.org/14thWaves/Papers/WW14%20FLOSSIE%20Jensen%20et%20al.pdf.
Knaff, J. A., S. P. Longmore, and D. A. Molenar, 2014: An objective satellite-based tropical cyclone size climatology. J. Climate, 27, 455–476, https://doi.org/10.1175/JCLI-D-13-00096.1; Corrigendum, 28, 8648–8651, https://doi.org/10.1175/JCLI-D-15-0610.1.
Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann, 2010: The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying tropical cyclone data. Bull. Amer. Meteor. Soc., 91, 363–376, https://doi.org/10.1175/2009BAMS2755.1.
Large, W. G., J. Morzel, and G. B. Crawford, 1995: Accounting for surface wave distortion of the marine wind profile in low-level ocean storms wind measurements. J. Phys. Oceanogr., 25, 2959–2971, https://doi.org/10.1175/1520-0485(1995)025<2959:AFSWDO>2.0.CO;2.
Lopatoukhin, L. J., V. A. Rozhkov, V. E. Ryabinin, V. R. Swail, A. V. Boukhanovsky, and A. B. Degtyarev, 2000: Estimation of extreme wind wave heights. WMO/TD-1041, 84 pp., https://www.wmo.int/pages/prog/amp/mmop/documents/JCOMM-TR/J-TR-9-ExtWaveHeight/JCOMM-TR-9-Extr-Wave-Height-Full.pdf.
Meucci, A., I. R. Young, and Ø. Breivik, 2018: Wind and wave extremes from atmosphere and wave model ensembles. J. Climate, 31, 8819–8843, https://doi.org/10.1175/JCLI-D-18-0217.1.
Ochi, M. K., 1992: New approach in estimating the severest sea state from statistical data. Proc. Coastal Eng. Conf., New York, NY, American Society of Civil Engineers, 512–523.
Ranjha, R., and Coauthors, 2015: Structure and variability of the Oman coastal low-level jet. Tellus, 67A, 25285, https://doi.org/10.3402/tellusa.v67.25285.
Sterl, A., and S. Caires, 2005: Climatology, variability and extrema of ocean waves: The web-based KNMI/ERA-40 wave atlas. Int. J. Climatol., 25, 963–977, https://doi.org/10.1002/joc.1175.
Stopa, J. E., and K. F. Cheung, 2014: Intercomparison of wind and wave data from the ECMWF Reanalysis Interim and the NCEP Climate Forecast System Reanalysis. Ocean Modell., 75, 65–83, https://doi.org/10.1016/j.ocemod.2013.12.006.
Taylor, P. K., and M. J. Yelland, 2001: Comments on “On the effect of ocean waves on the kinetic energy balance and consequences for the inertial dissipation technique.” J. Phys. Oceanogr., 31, 2532–2536, https://doi.org/10.1175/1520-0485(2001)031<2532:COOTEO>2.0.CO;2.
Teng, C. C., 1998: Long-term and extreme waves in the Gulf of Mexico. Proc. Conf. on Ocean Wave Kinematics and Loads on Structures, Houston, TX, ASME, 342–349.
Tucker, M. J., 1991: Waves in Ocean Engineering. Ellis Horwood, 431 pp.
Uppala, S. M., and Coauthors, 2005: The ERA-40 Re-Analysis. Quart. J. Roy. Meteor. Soc., 131, 2961–3012, https://doi.org/10.1256/qj.04.176.
Van Gelder, P. H. A. J. M., and J. K. Vrijling, 1999: On the distribution function of the maximum wave height in front of reflecting structures. Proc. Coastal Structures Conf., Santander, Spain, American Society of Civil Engineers, 37–46.
Vinoth, J., and I. R. Young, 2011: Global estimates of extreme wind speed and wave height. J. Climate, 24, 1647–1665, https://doi.org/10.1175/2010JCLI3680.1.
Wimmer, W., P. Challenor, and C. Retzler, 2006: Extreme wave heights in the North Atlantic from altimeter data. Renewable Energy, 31, 241–248, https://doi.org/10.1016/j.renene.2005.08.019.
Young, I. R., 1993: An estimate of the Geosat altimeter wind speed algorithm at high wind speeds. J. Geophys. Res., 98, 20 275–20 285, https://doi.org/10.1029/93JC02117.
Young, I. R., 1994: Global ocean wave statistics obtained from satellite observations. Appl. Ocean Res., 16, 235–248, https://doi.org/10.1016/0141-1187(94)90023-X.
Young, I. R., 1999: Seasonal variability of the global ocean wind and wave climate. Int. J. Climatol., 19, 931–950, https://doi.org/10.1002/(SICI)1097-0088(199907)19:9<931::AID-JOC412>3.0.CO;2-O.
Young, I. R., and M. A. Donelan, 2018: On the determination of global ocean wind and wave climate from satellite observations. Remote Sens. Environ., 215, 228–241, https://doi.org/10.1016/j.rse.2018.06.006.
Young, I. R., S. Zieger, and A. V. Babanin, 2011: Global trends in wind speed and wave height. Science, 332, 451–455, https://doi.org/10.1126/science.1197219.
Young, I. R., J. Vinoth, S. Zieger, and A. V. Babanin, 2012: Investigation of trends in extreme value wave height and wind speed. J. Geophys. Res., 117, C00J06, https://doi.org/10.1029/2011JC007753.
Young, I. R., A. V. Babanin, and S. Zieger, 2013: The decay rate of ocean swell observed by altimeter. J. Phys. Oceanogr., 43, 2322–2333, https://doi.org/10.1175/JPO-D-13-083.1.
Young, I. R., E. Sanina, and A.V. Babanin, 2017: Calibration and cross-validation of a global wind and wave database of altimeter, radiometer and scatterometer measurements. J. Atmos. Oceanic Technol., 34, 1285–1306, https://doi.org/10.1175/JTECH-D-16-0145.1.
Zeng, L., and R. A. Brown, 1998: Scatterometer observations at high wind speeds. J. Appl. Meteor., 37, 1412–1420, https://doi.org/10.1175/1520-0450(1998)037<1412:SOAHWS>2.0.CO;2.
