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Optimizing a new de-clustering approach for relatively small samples of wind speed with an application to offshore design conditions
- Source :
- Ocean Engineering. 228:108896
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- The effect of extreme wind speeds in applications for design is of great interest in a variety of fields such as meteorology and coastal engineering. In these fields a common problem is the scarcity of long datasets. To overcome this limitation, a common approach is to utilize the entire available dataset using the Peak-Over-Threshold (POT) approach. In small samples there may be a limited number of extremes and so re-sampling is often beneficial. However, the re-samples are often affected by dependency and the independence limitations are usually disregarded. To alleviate this effect, the DeCA Uncorrelated (DeCAUn) model is proposed taking into account the correlation effect when re-sampling. This model provides an improvement to the current physical De-Clustering Algorithm (DeCA), by re-sampling the samples of DeCA irregularly in time. The methodology proposed in this assessment is illustrated using wind speed data from a high resolution database over the North Sea, the Atlantic Ocean and the Mediterranean Sea. From this evaluation, the DeCAUn model is proposed as an alternative re-sampling strategy for observations irregularly spaced in time.
- Subjects :
- Environmental Engineering
Dependency (UML)
Meteorology
Computer science
020101 civil engineering
Ocean Engineering
02 engineering and technology
01 natural sciences
Wind speed
010305 fluids & plasmas
0201 civil engineering
Current (stream)
Mediterranean sea
0103 physical sciences
Submarine pipeline
Coastal engineering
Cluster analysis
Independence (probability theory)
Subjects
Details
- ISSN :
- 00298018
- Volume :
- 228
- Database :
- OpenAIRE
- Journal :
- Ocean Engineering
- Accession number :
- edsair.doi...........bd8176978deee3af8b78a03bad90750c