Intercomparison of Sea-State and Zero-Crossing Parameters From the WACSIS Field Experiment and Interpretation Using Video Evidence

2004 ◽  
Vol 126 (1) ◽  
pp. 35-42 ◽  
Author(s):  
Stephen F. Barstow, ◽  
Harald E. Krogstad ◽  
Lasse Lønseth ◽  
Jan Petter Mathisen ◽  
Gunnar Mørk ◽  
...  
Keyword(s):  
Time Series ◽  
Field Experiment ◽  
Time Series Data ◽  
Series Data ◽  
Sea State ◽  
Single Wave ◽  
Zero Crossing ◽  
Video Recordings ◽  
Wave Parameters ◽  
Wave Elevation

During the WACSIS field experiment, wave elevation time series data were collected over the period December 1997 to May 1998 on and near the Meetpost Nordwijk platform off the coast of the Netherlands from an EMI laser, a Saab radar, a Baylor Wave Staff, a Vlissingen step gauge, a Marex radar and a Directional Waverider. This paper reports and interprets, with the help of simultaneous dual video recordings of the ocean surface, an intercomparison of both single wave and sea state wave parameters.

Intercomparison of Seastate and Zerocrossing Parameters From the WACSIS Field Experiment and Interpretation Using Video Evidence

2002 ◽  
Author(s):  
Stephen F. Barstow ◽  
Harald E. Krogstad ◽  
Lasse Lo̸nseth ◽  
Jan Petter Mathisen ◽  
Gunnar Mo̸rk ◽  
...  
Keyword(s):  
Time Series ◽  
The Netherlands ◽  
Field Experiment ◽  
Time Series Data ◽  
Series Data ◽  
Sea State ◽  
Single Wave ◽  
Video Recordings ◽  
Wave Parameters ◽  
Wave Elevation

During the WACSIS field experiment, wave elevation time series data were collected over the period December 1997 to May 1998 on and near the Meetpost Nordwijk platform off the coast of the Netherlands from an EMI laser, a Saab radar, a Baylor Wave Staff, a Vlissingen step gauge, a Marex radar and a Directional Waverider. This paper reports and interprets, with the help of simultaneous dual video recordings of the ocean surface, an intercomparison of both single wave and sea state wave parameters.

Estimation of Most Probable Maximum From Short-Duration or Undersampled Time-Series Data

2015 ◽  
Author(s):  
Puneet Agarwal ◽  
William Walker ◽  
Kenneth Bhalla
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Short Duration ◽  
Time Series Data ◽  
Extreme Value ◽  
Series Data ◽  
Sampled Data ◽  
Zero Crossing ◽  
Short Time ◽  
Undersampled Data

The most probable maximum (MPM) is the extreme value statistic commonly used in the offshore industry. The extreme value of vessel motions, structural response, and environment are often expressed using the MPM. For a Gaussian process, the MPM is a function of the root-mean square and the zero-crossing rate of the process. Accurate estimates of the MPM may be obtained in frequency domain from spectral moments of the known power spectral density. If the MPM is to be estimated from the time-series of a random process, either from measurements or from simulations, the time series data should be of long enough duration, sampled at an adequate rate, and have an ensemble of multiple realizations. This is not the case when measured data is recorded for an insufficient duration, or one wants to make decisions (requiring an estimate of the MPM) in real-time based on observing the data only for a short duration. Sometimes, the instrumentation system may not be properly designed to measure the dynamic vessel motions with a fine sampling rate, or it may be a legacy instrumentation system. The question then becomes whether the short-duration and/or the undersampled data is useful at all, or if some useful information (i.e., an estimate of MPM) can be extracted, and if yes, what is the accuracy and uncertainty of such estimates. In this paper, a procedure for estimation of the MPM from the short-time maxima, i.e., the maximum value from a time series of short duration (say, 10 or 30 minutes), is presented. For this purpose pitch data is simulated from the vessel RAOs (response amplitude operators). Factors to convert the short-time maxima to the MPM are computed for various non-exceedance levels. It is shown that the factors estimated from simulation can also be obtained from the theory of extremes of a Gaussian process. Afterwards, estimation of the MPM from the short-time maxima is explored for an undersampled process; however, undersampled data must not be used and only the adequately sampled data should be utilized. It is found that the undersampled data can be somewhat useful and factors to convert the short-time maxima to the MPM can be derived for an associated non-exceedance level. However, compared to the adequately sampled data, the factors for the undersampled data are less useful since they depend on more variables and have more uncertainty. While the vessel pitch data was the focus of this paper, the results and conclusions are valid for any adequately sampled narrow-banded Gaussian process.

Data Mining Pt. Reyes Buoy for Rare Wave Groups

2015 ◽  
Author(s):  
Harleigh C. Seyffert ◽  
Armin W. Troesch
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
University Of California ◽  
Series Data ◽  
Wave Groups ◽  
Naturally Occurring ◽  
Wave Elevation ◽  
Information Program ◽  
Elevation Data ◽  
Definition Of

This paper addresses the existence of rare wave groups, as defined by Kim and Troesch [1], by examining time series data from the Pt. Reyes buoy. The buoy is operated by the Coastal Data Information Program (CDIP), University of California San Diego. The definition of rare wave groups [1] used in this paper differs from the more commonly used wave group definition based on threshold crossings. With the time series data from the Pt. Reyes buoy, these rare wave groups are shown to be a naturally occurring phenomenon. The nature of the data is examined, as well as the analysis methods and findings. By sifting through 17 years of wave elevation data from the Pt. Reyes buoy, this preliminary work addresses not only the question to what extent rare wave groups exist in nature, but also, what their probability of occurrence is.

Data Mining Pt. Reyes Buoy for Rare Wave Groups

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Harleigh C. Seyffert ◽  
Armin W. Troesch
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
University Of California ◽  
Series Data ◽  
Wave Groups ◽  
Naturally Occurring ◽  
Wave Elevation ◽  
Information Program ◽  
Elevation Data ◽  
Definition Of

This paper addresses the existence of rare wave groups by examining time series data from the Pt. Reyes buoy. The buoy is operated by the Coastal Data Information Program (CDIP), University of California San Diego. The definition of rare wave groups, as defined by Kim and Troesch, used in this paper differs from the more commonly used wave group definition based on threshold crossings. With the time series data from the Pt. Reyes buoy, these rare wave groups are shown to be a naturally occurring phenomenon. The essential features of the data are examined, as well as the analysis methods and findings. By sifting through 17 years of wave elevation data from the Pt. Reyes buoy, this preliminary work addresses not only the question to what extent rare wave groups exist in nature but also what their probability of occurrence is.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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