scholarly journals Global Wind Speed and Wave Height Extremes Derived from Long-Duration Satellite Records

2018 ◽  
Vol 32 (1) ◽  
pp. 109-126 ◽  
Author(s):  
Alicia Takbash ◽  
Ian R. Young ◽  
Øyvind Breivik
Keyword(s):  
Wind Speed ◽  
Wave Height ◽  
Heavy Rain ◽  
Extreme Value ◽  
Altimeter Data ◽  
Extreme Value Analysis ◽  
Validation Data ◽  
Distribution Method ◽  
Value Analysis ◽  
Long Duration

Abstract The application of extreme-value analysis to long-duration (30 year) global altimeter and radiometer datasets is considered. In contrast to previous extreme-value analyses of satellite data, the dataset is sufficiently long to enable a peaks over threshold analysis to be undertaken. When applied to altimeter data for wind speed and significant wave height, this analysis produces values consistent with buoy validation data and previous numerical model reanalysis datasets. The spatial distributions produced are also consistent with the model reanalysis data. However, the altimeter data shows much greater finescale structure for wind speed, which is consistent with known tropical cyclone activity. The greater data density provided by radiometer measurements offers the potential to address altimeter undersampling. However, issues associated with the radiometer’s 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 wind speed largely unusable for the investigation of wind speed extremes. The study also clearly demonstrates the limitations of the initial distribution method for extreme-value analysis, which is heavily biased by mean conditions.

scholarly journals Wind and Wave Extremes from Atmosphere and Wave Model Ensembles

2018 ◽  
Vol 31 (21) ◽  
pp. 8819-8842 ◽  
Author(s):  
Alberto Meucci ◽  
Ian R. Young ◽  
Øyvind Breivik
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wave Height ◽  
Significant Wave Height ◽  
Wave Model ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Ensemble Forecasts ◽  
Value Analysis ◽  
Model Ensembles

The present work develops an innovative approach to wind speed and significant wave height extreme value analysis. The approach is based on global atmosphere–wave model ensembles, the members of which are propagated in time from the best estimate of the initial state, with slight perturbations to the initial conditions, to estimate the uncertainties connected to model representations of reality. The low correlation of individual ensemble member forecasts at advanced lead times guarantees their independence and allows us to perform inference statistics. The advantage of ensemble probabilistic forecasts is that it is possible to synthesize an equivalent dataset of duration far longer than the simulation period. This allows the use of direct inference statistics to obtain extreme value estimates. A short time series of six years (from 2010 to 2016) of ensemble forecasts is selected to avoid major changes to the model physics and resolution and thus ensure stationarity. This time series is used to undertake extreme value analysis. The study estimates global wind speed and wave height return periods by selecting peaks from ensemble forecasts from +216- to +240-h lead time from the operational ensemble forecast dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF). The results are compared with extreme value analyses performed on a commonly used reanalysis dataset, ERA-Interim, and buoy data. The comparison with traditional methods demonstrates the potential of this novel approach for statistical analysis of significant wave height and wind speed ocean extremes at the global scale.

scholarly journals Spatial and Seasonal Variability of Metocean Design Criteria in the Southern South China Sea From Covariate Extreme Value Analysis

2019 ◽  
Author(s):  
Vadim Anokhin ◽  
Emma Ross ◽  
David Randell ◽  
Philip Jonathan
Keyword(s):  
Wind Speed ◽  
South China Sea ◽  
South China ◽  
Upper Bound ◽  
Shape Parameter ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Design Criteria ◽  
Southern South China Sea ◽  
Value Analysis

Abstract This paper describes spatial and seasonal variability of metocean design criteria in the southern South China Sea. Non-stationary extreme value analysis was performed using the CEVA approach (Covariate Extreme Value Analysis,[1]) for a 59-year long SEAFINE hindcast of winds and waves, estimating metocean design criteria up to 10,000-year return period. Wind design criteria are mostly driven by large-scale monsoonal events; at higher return periods infrequent cyclonic events have strong influence on the tail of the extreme value distribution but confined to a limited geographical area. The CEVA analysis of waves showed much less dependence on the tropical cyclone events; the spatial metocean design criteria were smoother, mostly influenced by the monsoonal wind strength, fetch and local bathymetry. Return value estimates illustrate the strong seasonality of metocean design criteria, with boreal winter (December-February, Northeasterly monsoon) contributing most to the extremes, while April and May are the mildest months. Estimates for the ratio of 10,000/100-year return values are also presented, both for winds and waves. There is empirical evidence that the range of “typical” values of generalised Pareto shape parameter observed for Hs is different to that observed for wind speed. For this reason, an upper bound of +0.2 for generalised Pareto shape was specified for wind speed analysis, compared to 0.0 for Hs. In some cases, increase of upper bound for waves to 0.1 is justified, leading to slightly more conservative Hs values. We confirmed that the upper end point constraint was not too influential on the distributions of generalised Pareto shape parameter estimated. Nevertheless, it is apparent that specification of bounds for generalised Pareto shape is a critical, but problematic choice in metocean applications.

Extreme Value Analysis and Joint Densities of Wind Directionality, Wind Speed and Wave Height for Malaysian Waters

2014 ◽  
Vol 935 ◽  
pp. 159-162
Author(s):  
Wan Sharil Ahmad Termizi ◽  
Mohd Shahir Liew
Keyword(s):  
Fluid Dynamics ◽  
Wind Speed ◽  
Computer Science ◽  
Robust Design ◽  
Joint Effect ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Offshore Platforms ◽  
Value Analysis ◽  
Maximum Value

Prediction of extreme environmental loadings is crucial in the design of offshore platforms. Combinations of independent 100-year loadings are usually used assuming that it will bring the maximum value that is suitable for the design. It is rather impossible for such independent combinations to occur at the same time. Addressing this issue requires approaches in two methods. The first would be to find the probabilities of joint effect of these parameters, while the second would be to forecast the extreme value of each parameter. Joint densities and extreme value analysis have become available due to significant advancements in fluid dynamics and computer science. By using the combination of these two techniques, the actual loading of wind and wave can be obtained, leading to optimum and robust design.

Uncertainties in Extreme Value Analysis of Wave Climate Data and Wave Climate Projections

2015 ◽  
Author(s):  
Erik Vanem
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Wave Climate ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Climate Projections ◽  
Data Sets ◽  
Climate Data ◽  
Value Analysis ◽  
Significant Wave

The extreme values of climate data are of interest in design of marine structures and the return values of certain met-ocean parameters such as significant wave height is of particular importance. However, there are various ways of analyzing the extremes and estimating the required return values, which introduce additional uncertainties. These are investigated in this paper by applying different methods to particular data sets of significant wave height, corresponding to the historic climate and two future projections of the climate assuming different forcing scenarios. In this way, the uncertainty due to the extreme value analysis can also be compared to the uncertainty due to a changing climate. The different approaches that will be considered is the initial distribution approach, the block maxima approach, the peak over threshold (POT) approach and the average conditional exceedance rate method (ACER). Furthermore, the effect of different modelling choices within each of the approaches will be explored. Thus, a range of different return value estimates for the different data sets is obtained. This exercise reveals that the uncertainty due to the extreme value analysis method is notable and, as expected, the variability of the estimates increases for higher return periods. Moreover, even though the variability due to the extreme value analysis is greater than the climate variability, a shift towards higher extremes in a future wave climate can clearly be discerned in the particular datasets that have been analysed.

Extreme Value Analysis of Multivariate High-Frequency Wind Speed Data

2013 ◽  
Vol 7 (1) ◽  
pp. 73-94 ◽  
Author(s):  
Christina Steinkohl ◽  
Richard A. Davis ◽  
Claudia Klüppelberg
Keyword(s):  
Wind Speed ◽  
High Frequency ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Wind Speed Data
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