Abstract

In May of 2000 Jason-1, a joint project between NASA and the French space agency CNES, will be launched. Its mission is to continue the highly successful gathering of data which TOPEX/Poseidon has collected since August of 1992. The main goal of Jason-1 is to achieve higher accuracy in measuring the mean sea level (MSL). In order to do so, the electromagnetic (EM) bias must be estimated more accurately because it is the largest contributing error. This thesis presents two different studies which add to the knowledge and improve estimation of the EM bias, and thus assists Jason-1 in achieving its primary goal. Oceanographic data collected from two different experiments are analyzed; on in the Gulf of Mexico (GME) and the other in Bass Strait, Australia (BSE).

The first study is a spatial analysis of the backscattered power versus the phase of the wave. Its purpose is to determine why the normalized EM bias stops increasing and levels out at high wind speeds (about 11 m/s) and then decreases at higher wind speeds. Two possible causes are investigated. First, it could be due to a shift in the backscatter power modulation to the forward or rear face of the wave crests. Second, it may be due to the backscatter power becoming more homogeneous throughout the wave profile. This study is novel because it uses the knowledge of the spatial distribution of both the backscatter and wave displacement for the study of the EM bias. Both contribute to the EM bias decrease, but the latter cause seems to be the dominant effect. This study is performed on GME data.

The second study uses two different nonparametric regression (NPR) techniques to estimate the EM bias. A recent study of satellite data from the TOPEX/Poseidon altimeter supports that the bias is modeled better using NPR regression. A traditional parametric fit is compared to two NPR techniques with GME data. The parametric fit is a variation of NASA's equation used to estimate EM bias for their Geophysical Data Records (GDRs). The two NPR techniques used are the Nadaraya-Watson Regression (NWR) and Local Linear Regression (LLR) estimators. Two smoothing kernel functions are used with each NPR technique, namely the Gaussian and the Epanechnikov kernels. NPR methods essentially consist of statistically smoothing the measured EM bias estimates are compared in the wind and significant wave height plane. Another recent study has shown that wave slope is strongly correlated to EM bias. With this knowledge, EM bias is estimated over several two-dimensional planes which include wave slope in attempt to reduce the residual bias. This portion of the study is performed on GME and BSE data. It is shown that a combination of slope, significant wave height, and wind speed used in conjunction with these NPR methods produces the best EM bias estimate for tower data.

Degree

College and Department

Ira A. Fulton College of Engineering and Technology; Electrical and Computer Engineering

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