Improving Electromagnetic Bias Estimates
Description
The derivation of an electromagnetic (EM) bias model that includes the physical optics scattering models and the non-Gaussian long wave surface statistics is presented. The final formulation of the model is expressed as a function of hydrodynamic modulation, surface skewness, and tilt modulation. Through the modulation transfer function, the hydrodynamic modulation coefficient is shown to be equivalent to the long wave RMS slope multiplied by a function of the short wave spectrum. With this result the normalized EM bias reduces to a function of long wave surface parameters with coefficients determined by properties of the short ocean waves. EM bias values are computed from the theory, using a realistic surface PSD, and compared with in situ bias measurements. The bias model is shown to be in excellent agreement with the measured values, and includes features of normalized bias not present in previous models.
Improving Electromagnetic Bias Estimates
Salt Lake Community College
The derivation of an electromagnetic (EM) bias model that includes the physical optics scattering models and the non-Gaussian long wave surface statistics is presented. The final formulation of the model is expressed as a function of hydrodynamic modulation, surface skewness, and tilt modulation. Through the modulation transfer function, the hydrodynamic modulation coefficient is shown to be equivalent to the long wave RMS slope multiplied by a function of the short wave spectrum. With this result the normalized EM bias reduces to a function of long wave surface parameters with coefficients determined by properties of the short ocean waves. EM bias values are computed from the theory, using a realistic surface PSD, and compared with in situ bias measurements. The bias model is shown to be in excellent agreement with the measured values, and includes features of normalized bias not present in previous models.