scholarly journals THE PREDICTION OF EXTREME VALUE WIND SPEEDS AND WAVE HEIGHTS FROM SATELLITE DATA

2018 ◽  
pp. 1
Author(s):  
Alicia Takbash ◽  
Ian Young
Keyword(s):  
Wind Speed ◽  
Numerical Model ◽  
Wave Height ◽  
Satellite Data ◽  
Extreme Values ◽  
Essential Element ◽  
Extreme Value ◽  
Ocean Engineering ◽  
Wind Speeds

The prediction of extreme value (e.g. 1 in 100 year) estimates of wind speed and wave height is an essential element of coastal and ocean engineering design. Despite decades of research on the statistics of extreme values, the consistent limitation faced by practitioners is the requirement for a long (20 plus years) dataset at the location of interest. Long term insitu buoy deployments have started to provide useful records in some geographic locations. Long term numerical model hindcasts have also proved useful. However, buoy deployments are seldom at the location of interest and the accuracy of numerical model hindcasts more than 20 years in the past is questionable. This paper will investigate the use of long-term satellite data sets of wind speed and wave height to provide global estimates of extreme values.

Global Trends in Extreme Wind Speed and Wave Height

2013 ◽  
Author(s):  
I. R. Young ◽  
S. Zieger ◽  
J. Vinoth ◽  
A. V. Babanin
Keyword(s):  
Wind Speed ◽  
Wave Height ◽  
Global Climate ◽  
Extreme Value ◽  
Global Perspective ◽  
Ocean Engineering ◽  
Change Analysis ◽  
Data Set ◽  
Wind Speeds ◽  
Extreme Wind

Satellite observations of the ocean surface provide a powerful method for acquiring global data on wind speed and wave height. Radar altimeters have now been in operation for more than 25 years, providing a reasonably long term data set with global coverage. This paper presents data from a fully calibrated and validated altimeter dataset. The dataset provides the basis for obtaining a global perspective of a number of parameters critical to ocean engineering design, ship operations and global climate change. Analysis of the data provides ocean climatology of mean monthly values of wind speed and wave height useful for ship operations. The data set is also sufficiently long to provide extreme value (i.e. 100-year return period) estimates of wind speed and wave height. The paper presents such values and describes the approaches most appropriate to obtain statistically significant extreme value estimates from such satellite data. With a data set of this length, it is possible to investigate whether there have been statistically significant changes in the wind and wave climates over the period. Careful trend analysis of the extensive data set shows that there has been a statistically significant increasing trend in mean wind speed over the period. The corresponding increase in wave height is less clear. There is also evidence to suggest that extreme wind speeds and wave heights are increasing and the data set is analysed to investigate these trends. The paper clearly shows the value of this dataset and its application to a range of engineering problems.

Evaluation of altimeter undersampling in estimating global wind and wave climate using virtual observation

2020 ◽  
Author(s):  
Haoyu Jiang
Keyword(s):  
Wind Speed ◽  
Wave Height ◽  
Extreme Values ◽  
Wave Climate ◽  
Altimeter Data ◽  
Global Trends ◽  
Long Duration ◽  
Long Term Trends ◽  
Along Track

<p>Altimeters can provide global long-duration observations of oceanic wind speed and wave height. However, altimeters face the undersampling problem in estimating wind and wave climate because of their sparse sampling pattern and the changing number of in-orbit satellites. In this study, the undersampling error of altimeters was studied by sampling the ERA5 oceanic wind speed and wave height data using the track information of multiplatform altimeters. Comparisons were made between the statistics (mean, 90<sup>th</sup> and 99<sup>th</sup> percentiles, and long-term trends of them) of the original ERA5 data and the gridded along-track sampling of the ERA5 data. The results show a large discrepancy with respect to the extreme values (90<sup>th</sup> and 99<sup>th</sup> percentiles). The undersampling of altimeters can lead to significant underestimations of monthly extreme values of oceanic wind speed and wave height. Meanwhile, this underestimation is alleviated with the increase of the number of in-orbit altimeters, leading to very large overestimations of long-term trends of these extreme values over the period 1985-2018. In contrast, the annual extreme values of oceanic wind speed and wave height and their long-term trends are more reliable, although slight aforementioned biases of extreme values still exist and the data from GEOSAT are not suitable for computing annual statistics. For altimeter data, the annual values are a better option to compute long-term trends than the monthly data. This study also presents a correction scheme of using model data to compensate for the wind and wave events missed by altimeter tracks. After the correction, the global trends in oceanic wind speed and wave height over 1992-2017 are recomputed using annual statistics. The results show a clear discrepancy between the trends of wind speed and wave height during this period: the wind speed increased, while the wave height decreased. However, the reason for this discrepancy is unknown at this stage.</p>

scholarly journals The Characteristics of West Season Wind and Wave as well as Their Impacts on Ferry Cruise in The Kalianget-Kangean Cruise Route, Madura, Indonesia

2021 ◽  
Vol 29 (3) ◽  
Author(s):  
Viv Djanat Prasita ◽  
Lukman Aulia Zati ◽  
Supriyatno Widagdo
Keyword(s):  
Wind Speed ◽  
Wave Height ◽  
The West ◽  
Wind Speeds ◽  
Wave Patterns ◽  
Time Stamps ◽  
The Mean ◽  
Two Peaks ◽  
The Impact ◽  
Descriptive Statistical Analysis

The wind and wave conditions in the waters of the Kalianget-Kangean cruise route in the west season are relatively high so that these winds and waves can have a dangerous impact on that cruise route. The aim of this research was to analyze the characteristics of wind speed and wave height over a 10 year period (2008-2017), as well as to evaluate the weekly patterns for three months (December 2017-February 2018). These time stamps represent the west season in waters at Kalianget-Kangean route, and to identify the impact of winds and wave on this path. The method used in this research is descriptive statistical analysis to obtain the mean and maximum values ​​of wind speed and wave height. Wind and wave patterns were analyzed by WRPlot and continued with mapping of wind and wave patterns in the waters of Kalianget-Kangean and its surroundings. The data used was obtained from the Meteorology, Climatology and Geophysics Agency. The results show wind and wave characteristics with two peaks formed regularly between 2008-2017, marking the west and east monsoons. In addition, the wind speed and wave height were generally below the danger threshold, ie <10 knots and <2 m, respectively. However, there are exceptions in the west season, especially at the peak in January, where the forces are strengthened with a steady blowing direction. The maximum wind speed reaches and wave height reaches 29 knots and 6.7 m, respectively. The weekly conditions for both parameters from December 2017 to February 2018 were relatively safe, for sailing. Moreover, January 23-29, 2018 featured extreme conditions estimated as dangerous for cruise due to the respective maximum values of 25 knots and 3.8 m recorded. The channel is comparably safe, except during the western season time in December, January, February, characterized by wind speeds and wave height exceeding 21 knots and 2.5 m, correspondingly.

Consideration of Epistemic Uncertainty in Extreme Wave Height Estimation

2014 ◽  
Author(s):  
Ryota Wada ◽  
Takuji Waseda
Keyword(s):  
Gulf Of Mexico ◽  
Observational Data ◽  
Wave Height ◽  
Extreme Values ◽  
Epistemic Uncertainty ◽  
Extreme Value Distribution ◽  
Predictive Distribution ◽  
Extreme Value ◽  
Posterior Predictive Distribution ◽  
Value Estimation

Extreme value estimation of significant wave height is essential for designing robust and economically efficient ocean structures. But in most cases, the duration of observational wave data is not efficient to make a precise estimation of the extreme value for the desired period. When we focus on hurricane dominated oceans, the situation gets worse. The uncertainty of the extreme value estimation is the main topic of this paper. We use Likelihood-Weighted Method (LWM), a method that can quantify the uncertainty of extreme value estimation in terms of aleatory and epistemic uncertainty. We considered the extreme values of hurricane-dominated regions such as Japan and Gulf of Mexico. Though observational data is available for more than 30 years in Gulf of Mexico, the epistemic uncertainty for 100-year return period value is notably large. Extreme value estimation from 10-year duration of observational data, which is a typical case in Japan, gave a Coefficient of Variance of 43%. This may have impact on the design rules of ocean structures. Also, the consideration of epistemic uncertainty gives rational explanation for the past extreme events, which were considered as abnormal. Expected Extreme Value distribution (EEV), which is the posterior predictive distribution, defined better extreme values considering the epistemic uncertainty.

Calibration and Cross Validation of Global Ocean Wind Speed Based on Scatterometer Observations

2020 ◽  
Vol 37 (2) ◽  
pp. 279-297 ◽  
Author(s):  
Agustinus Ribal ◽  
Ian R. Young
Keyword(s):  
Wind Speed ◽  
Cross Validation ◽  
Regression Line ◽  
Total Duration ◽  
Global Ocean ◽  
High Wind ◽  
Wind Speeds ◽  
Buoy Data ◽  
Ocean Wind

AbstractGlobal ocean wind speed observed from seven different scatterometers, namely, ERS-1, ERS-2, QuikSCAT, MetOp-A, OceanSat-2, MetOp-B, and Rapid Scatterometer (RapidScat) were calibrated against National Data Buoy Center (NDBC) data to form a consistent long-term database of wind speed and direction. Each scatterometer was calibrated independently against NDBC buoy data and then cross validation between scatterometers was performed. The total duration of all scatterometer data is approximately 27 years, from 1992 until 2018. For calibration purposes, only buoys that are greater than 50 km offshore were used. Moreover, only scatterometer data within 50 km of the buoy and for which the overpass occurred within 30 min of the buoy recording data were considered as a “matchup.” To carry out the calibration, reduced major axis (RMA) regression has been applied where the regression minimizes the size of the triangle formed by the vertical and horizontal offsets of the data point from the regression line and the line itself. Differences between scatterometer and buoy data as a function of time were investigated for long-term stability. In addition, cross validation between scatterometers and independent altimeters was also performed for consistency. The performance of the scatterometers at high wind speeds was examined against buoy and platform measurements using quantile–quantile (Q–Q) plots. Where necessary, corrections were applied to ensure scatterometer data agreed with the in situ wind speed for high wind speeds. The resulting combined dataset is believed to be unique, representing the first long-duration multimission scatterometer dataset consistently calibrated, validated and quality controlled.

Nonstationary Extreme-Value Predictions in the Gulf of Mexico

2007 ◽  
Author(s):  
Christos N. Stefanakos
Keyword(s):  
Time Series ◽  
Gulf Of Mexico ◽  
Wave Height ◽  
Missing Values ◽  
Extreme Value ◽  
Altimeter Data ◽  
Dependence Structure ◽  
Measured Time ◽  
Definition Of

In the present work, return periods of various level values of significant wave height in the Gulf of Mexico are given. The predictions are based on a new method for nonstationary extreme-value calculations that have recently been published. This enhanced method exploits efficiently the nonstationary modeling of wind or wave time series and a new definition of return period using the MEan Number of Upcrossings of the level value x* (MENU method). The whole procedure is applied to long-term measurements of wave height in the Gulf of Mexico. Two kinds of data have been used: long-term time series of buoy measurements, and satellite altimeter data. Measured time series are incomplete and a novel procedure for filling in of missing values is applied before proceeding with the extreme-value calculations. Results are compared with several variants of traditional methods, giving more realistic estimates than the traditional predictions. This is in accordance with the results of other methods that take also into account the dependence structure of the examined time series.

Long Term Satellite Observations of the Global Wind Resource

2018 ◽  
Author(s):  
I. R. Young
Keyword(s):  
Wind Speed ◽  
Extreme Value ◽  
Satellite Observations ◽  
Satellite Measurements ◽  
Global Estimates ◽  
Wind Resource

A database of global satellite measurements of wind speed is calibrated and validated to provide a consistent set of global measurements over a period of 30 years. This database is used to describe the global wind resource including: mean monthly climatology, extreme value estimates of global wind speed and global estimates of trend (changes) in wind speed.

Alat Ukur Kecepatan Angin Permukaan Air Laut Berbasis Mikrokontroler

2020 ◽  
Vol 2 (2) ◽  
pp. 80-88
Author(s):  
Waluyo Waluyo ◽  
Meli Ruslinar
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Wind Data ◽  
Measuring Instrument ◽  
Ocean Engineering ◽  
Sea Surface Wind Speed ◽  
Wind Speeds ◽  
Sea Surface Wind ◽  
Engineering Modeling

The microcontroller is one technology that is developing so rapidly with various types and functions, one of which is Arduino Uno which can be used as a microcontroller for various functions in the field of electronics technology. This research was conducted at the Laboratory of Ocean Engineering Modeling, Marine and Fisheries Polytechnic of Karawang in March-June 2020. The purpose of this study was to create a microcontroller-based sea surface wind speed measuring instrument. Based on the results of the acquisition of wind data using a fan simulation and natural wind gusts with different wind speeds in the field show a significant tool response. The results of the comparison of data recording between the results of research with the existing wind speed measuring instrument show that there is an average tool error of 3.24%, a relative error of 3.78%, and an instrument accuracy rate of 96.76%. Thus it can be said that the ability of the tool is able to record wind data with high accuracy.

Height Extrapolation of Wind Data

1985 ◽  
Vol 107 (1) ◽  
pp. 10-14 ◽  
Author(s):  
A. S. Mikhail
Keyword(s):  
Wind Speed ◽  
Power Law ◽  
Wind Data ◽  
Power Law Model ◽  
Short Term ◽  
Shape Factors ◽  
Wind Speeds ◽  
Average Wind ◽  
Modified Power Law

Various models that are used for height extrapolation of short and long-term averaged wind speeds are discussed. Hourly averaged data from three tall meteorological towers (the NOAA Erie Tower in Colorado, the Battelle Goodnoe Hills Tower in Washington, and the WKY-TV Tower in Oklahoma), together with data from 17 candidate sites (selected for possible installation of large WECS), were used to analyze the variability of short-term average wind shear with atmospheric and surface parameters and the variability of the long-term Weibull distribution parameter with height. The exponents of a power-law model, fit to the wind speed profiles at the three meteorological towers, showed the same variability with anemometer level wind speed, stability, and surface roughness as the similarity law model. Of the four models representing short-term wind data extrapolation with height (1/7 power law, logarithmic law, power law, and modified power law), the modified power law gives the minimum rms for all candidate sites for short-term average wind speeds and the mean cube of the speed. The modified power-law model was also able to predict the upper-level scale factor for the WKY-TV and Goodnoe Hills Tower data with greater accuracy. All models were not successful in extrapolation of the Weibull shape factors.

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