| 1. |
- Mackay, Edward B. L., et al.
(författare)
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A comparison of estimators for the generalised Pareto distribution
- 2011
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Ingår i: Ocean Engineering. - : Elsevier BV. - 0029-8018 .- 1873-5258. ; 38:11–12, s. 1338-1346
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Tidskriftsartikel (refereegranskat)abstract
- The generalised Pareto distribution (GPD) is often used to model the distribution of storm peak wave heights exceeding a high threshold, from which return values can be calculated. There are large differences in the performance of various parameter and quantile estimators for the GPD. Commonly used estimation methods such as maximum likelihood or probability weighted moments are not optimal, especially for smaller sample sizes. The performance of several estimators for the GPD is compared by the Monte Carlo simulation and the implications for estimating return values of significant wave height are discussed. Of the estimators compared, the likelihood-moment (LM) estimator has close to the lowest bias and variance over a wide range of sample sizes and GPD shape parameters. The LM estimator always exists, is simple to compute and has a low sensitivity to choice of threshold. It is recommended that the LM estimator is used for calculating return values of significant wave height when the sample size is less than 500. For sample sizes above 500 the NEW estimator of Zhang and Stephens (2009) can give accurate results for low computational cost.
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| 2. |
- Mackay, Edward B. L., et al.
(författare)
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On the use of discrete seasonal and directional models for the estimation of extreme wave conditions
- 2010
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Ingår i: Ocean Engineering. - : Elsevier BV. - 0029-8018 .- 1873-5258. ; 37:5–6, s. 425-442
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Tidskriftsartikel (refereegranskat)abstract
- Extreme value theory is commonly used in offshore engineering to estimate extreme significant wave height. To justify the use of extreme value models it is of critical importance either to verify that the assumptions made by the models are satisfied by the data or to examine the effect violating model assumptions. An important assumption made in the derivation of extreme value models is that the data come from a stationary distribution. The distribution of significant wave height varies with both the direction of origin of a storm and the season it occurs in, violating the assumption of a stationary distribution. Extreme value models can be applied to analyse the data in discrete seasons or directional sectors over which the distribution can be considered approximately stationary. Previous studies have suggested that models which ignore seasonality or directionality are less accurate and will underestimate extremes. This study shows that in fact the opposite is true. Using realistic case studies, it is shown that estimates of extremes from non-seasonal models have a lower bias and variance than estimates from discrete seasonal models and that estimates from discrete seasonal models tend to be biased high. The results are also applicable to discrete directional models.
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| 3. |
- Mackay, Edward B. L., et al.
(författare)
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Uncertainty in wave energy resource assessment. Part 1 : Historic data
- 2010
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Ingår i: Renewable energy. - : Elsevier BV. - 0960-1481 .- 1879-0682. ; 35:8, s. 1792-1808
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Tidskriftsartikel (refereegranskat)abstract
- The uncertainty in estimates of the energy yield from a wave energy converter (WEC) is considered. The study is presented in two articles. This first article deals with the accuracy of the historic data and the second article considers the uncertainty which arises from variability in the wave climate. Estimates of the historic resource for a specific site are usually calculated from wave model data calibrated against in-situ measurements. Both the calibration of model data and estimation of confidence bounds are made difficult by the complex structure of errors in model data. Errors in parameters from wave models exhibit non-linear dependence on multiple factors, seasonal and interannual changes in bias and short-term temporal correlation. An example is given using two hindcasts for the European Marine Energy Centre in Orkney. Before calibration, estimates of the long-term mean WEC power from the two hindcasts differ by around 20%. The difference is reduced to 5% after calibration. The short-term temporal evolution of errors in WEC power is represented using ARMA models. It is shown that this is sufficient to model the long-term uncertainty in estimated WEC yield from one hindcast. However, seasonal and interannual changes in model biases in the other hindcast cause the uncertainty in estimated long-term WEC yield to exceed that predicted by the ARMA model.
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| 4. |
- Mackay, Edward B. L., et al.
(författare)
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Uncertainty in wave energy resource assessment. Part 2 : Variability and predictability
- 2010
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Ingår i: Renewable energy. - : Elsevier BV. - 0960-1481 .- 1879-0682. ; 35:8, s. 1809-1819
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Tidskriftsartikel (refereegranskat)abstract
- The uncertainty in estimates of the energy yield from a wave energy converter (WEC) is considered. The study is presented in two articles. The first article considered the accuracy of the historic data and the second article, presented here, considers the uncertainty which arises from variability in the wave climate. Mean wave conditions exhibit high levels of interannual variability. Moreover, many previous studies have demonstrated longer-term decadal changes in wave climate. The effect of interannual and climatic changes in wave climate on the predictability of long-term mean WEC power is examined for an area off the north coast of Scotland. In this location anomalies in mean WEC power are strongly correlated with the North Atlantic Oscillation (NAO) index. This link enables the results of many previous studies on the variability of the NAO and its sensitivity to climate change to be applied to WEC power levels. It is shown that the variability in 5, 10 and 20 year mean power levels is greater than if annual power anomalies were uncorrelated noise. It is also shown that the change in wave climate from anthropogenic climate change over the life time of a wave farm is likely to be small in comparison to the natural level of variability. Finally, it is shown that despite the uncertainty related to variability in the wave climate, improvements in the accuracy of historic data will improve the accuracy of predictions of future WEC yield.
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| 5. |
- Thompson, Robin N., et al.
(författare)
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Key questions for modelling COVID-19 exit strategies
- 2020
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Ingår i: Proceedings of the Royal Society of London. Biological Sciences. - : The Royal Society. - 0962-8452 .- 1471-2954. ; 287:1932
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Tidskriftsartikel (refereegranskat)abstract
- Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.
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