Covariance Function for Nearshore Wave Assimilation Systems
Abstract
Data assimilation is widely used to combine observations with dynamic models to improve model prediction. The relative weighting of the original forecast and the observations, the error covariance matrices, determine how the information is transferred from the observations to the model. It is shown that the optimization of the assimilation systems has to be driven by the data taking into account the physics of the wavefield. Whereas the temporal covariance can be modeled by a parameterized Gaussian function, for nearshore wave assimilation applications, the covariance function depends primarily on the local depth and secondly on the distance from the assimilation location. In spectral space, a skewed exponential function is suggested.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 30, 2018
- Accession Number
- AD1048549
Entities
People
- Hans E. Ngodock
- Jay Veeramony
- Mark D. Orzech
- Stylianos Flampouris
Organizations
- United States Naval Research Laboratory