Sea State Bias Estimation with Least Absolute Shrinkage and Selection Operator (LASSO)

Abstract

With the sea-level rising, the measurement of sea surface height (SSH) is attracting more and more attention in the area of oceanography. Satellite radar altimeter is usually used to measure the SSH. However, deviation between the measured value and the actual one always exists. Among others, the sea state bias (SSB) is a main reason to cause the deviation. In general, one needs to estimate SSB first to correct the measured SSH. Currently, existing SSB estimation methods more or less have shortcomings, such as with many parameters, high prediction error and long training time. In this paper, we introduce an effective and efficient linear model called LASSO to the SSB estimation. The LASSO algorithm minimizes the residual sum of squares under the condition that the sum of the absolute values of each coefficient is less than a certain constant. In the implementation of LASSO, we use the significant wave height and wind speed to fit the LASSO model. Hence, the applied model has only 3 parameters, corresponding to the two inputs and a bias. Experimental results on the data of JASON-2, JASON-3, T/P and HY-2 radar altimetry show that LASSO performs better than geophysical data records (GDR) and ordinary least squares (OLS) estimator. Moreover, from the running time, we can see that LASSO is very efficient.