An Improvement of the One-dimensional Ocean Wave Description based on SWIM Observations

Yihui Wang,Xingou Xu

crossref(2024)

Cited 0|Views2
No score
Abstract
Abstract. The one-dimensional ocean wave spectra (1D spectra) describing the total energy of the ocean waves are vital for providing ocean surface roughness information in remote sensing simulations. Most existing wave spectrum models deviate from real ocean surface descriptions due to limitations of observation methods and approximation in theories applied to generating them. In this research, the widely applied Goda and Elfouhaily spectra in their 1-D form are compared with the remote sensing products from the Surface Waves Investigation and Monitoring instrument (SWIM) on-board the China France Oceanography Satellite (CFOSAT). Differences between models and the measurements are addressed, then the causes are analyzed and concluded in terms of sea states. Then, a Combined spectrum (C spectrum) considering varied sea states is proposed as a closer model to the observations of the real sea, where parameterization of the spectral peak enhancement factor (γ) is achieved by the inverse wave age and wave steepness for multiple sea states. Then the specific values of the all-state sea are obtained from SWIM observations. The validation of the C spectrum is achieved by comparisons with SWIM measurements not utilized during the model establishment, and with buoy measurements. The difference index (DI) and the R-squared (R2), are calculated for evaluation of the results, indicating that the C spectrum demonstrates closer fitting to the SWIM and buoy measurements than both Goda and Elfouhaily spectra. The DI and R2 for the C spectrum are compared to the Goda spectrum, which is closer to SWIM measurements than E spectrum, and values are 0.780 and 0.909 respectively. Results suggest the C spectrum is suitable for providing information required for remote sensing applications. Further research would be focusing on implementing description in different azimuthal directions.
More
Translated text
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined