Spectral Analysis Applied to Ocean Waves

Click For Summary
SUMMARY

Spectral analysis is a critical tool for validating and processing ocean wave data, including parameters such as wave height, direction, and frequency. It allows engineers to compare recorded wave behavior against theoretical models, identifying and adjusting outliers to enhance data accuracy. Understanding the influence of wind conditions on wave formation is essential, as it can guide expectations regarding wave spectra, such as the Pierson-Moskowitz spectrum for steady-state conditions. This foundational knowledge is vital for engineers working with oceanographic data.

PREREQUISITES
  • Basic understanding of spectral analysis principles
  • Familiarity with ocean wave parameters (height, direction, frequency)
  • Knowledge of theoretical wave patterns and models
  • Awareness of wind conditions affecting wave behavior
NEXT STEPS
  • Study the Pierson-Moskowitz spectrum for steady-state wave predictions
  • Explore different models for developing sea-states in confined areas
  • Learn about data processing techniques for oceanographic measurements
  • Investigate methods for identifying and adjusting outliers in wave data
USEFUL FOR

Engineers, oceanographers, and data analysts involved in ocean wave research and validation of wave data will benefit from this discussion.

Revvie32
Messages
5
Reaction score
0
1. I am currently looking at the area of spectral analysis applied to ocean waves. I have a set of "raw" recorded data of wave height, direction, frequency etc. I understand that spectral analysis is used to validate/process wave data recorded by instruments.

2. Have I more or less understood the principle correctly here? I am an engineer rather than a scientist/mathematician so I don't need to become an expert on the finer details of spectral analysis, a basic level understanding is all that's required!

3. My understanding is that real water waves (to a certain degree at least) behave according to theorectical wave patterns. As such, applying spectral analysis to "raw" data determines the extent to which theoretical wave behaviour matches measured wave behaviour. In this way, outliers or rogue data which do not correspond to theorectical expected patterns can be eliminated/adjusted, thereby yielding more representative results.
 
Physics news on Phys.org
Revvie32 said:
1. I am currently looking at the area of spectral analysis applied to ocean waves. I have a set of "raw" recorded data of wave height, direction, frequency etc. I understand that spectral analysis is used to validate/process wave data recorded by instruments.

2. Have I more or less understood the principle correctly here? I am an engineer rather than a scientist/mathematician so I don't need to become an expert on the finer details of spectral analysis, a basic level understanding is all that's required!

3. My understanding is that real water waves (to a certain degree at least) behave according to theorectical wave patterns. As such, applying spectral analysis to "raw" data determines the extent to which theoretical wave behaviour matches measured wave behaviour. In this way, outliers or rogue data which do not correspond to theorectical expected patterns can be eliminated/adjusted, thereby yielding more representative results.


I think your understanding is reasonably accurate. This is a very complex subject, as you know, so a simple summary can't do the subject justice. But for your purposes, you seem to be in good shape.

Your third statement is particularly valid if you know something about the wind conditions that created the waves. For example, if you know that constant wind velocity existed over a long period of time over a very wide area, then you would expect a Pierson-Moskowitz type spectrum which is a steady state prediction. There are also other models for developing sea-states in more confined areas, and if you know something about the details, you can have some idea of the expected spectrum.
 

Similar threads

  • · Replies 2 ·
Replies
2
Views
3K
  • · Replies 3 ·
Replies
3
Views
7K
  • · Replies 4 ·
Replies
4
Views
2K
  • · Replies 3 ·
Replies
3
Views
3K
  • · Replies 7 ·
Replies
7
Views
3K
  • · Replies 4 ·
Replies
4
Views
4K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 5 ·
Replies
5
Views
3K
  • · Replies 13 ·
Replies
13
Views
3K
  • · Replies 1 ·
Replies
1
Views
3K