New models for satellite altimeter sea state bias correction developed using global wave model data

Abstract

[1] Changing surface wave conditions alter the altimeter's estimate of mean sea level. Present-day methods for correcting this bias are solely based on wave and wind information from the altimeter. This paper tests the use of additional information to develop several sea state bias correction models using a yearlong combination of Jason-1 data with wave field statistics generated from the WaveWatch3 ocean wave model hindcast. Each candidate model is produced in the same manner, using a nonparametric mapping between Jason-1 sea surface height anomaly estimates and two correlatives. The first is always the significant wave height from Jason-1 and the model differences come through choice of the second variable. Past studies dictate our selection of these second parameters and they include terms related to the local wind speed, swell energy, wave age, wave period, and wave slope. Model skills are evaluated in terms of explained variance. Several models indicate promise for wave model use in future empirical developments. For instance, results show that in the low latitudes the models developed using the swell height and mean period modestly but systematically outperform the actual operational parameterisation. This study further indicates that systematic regional error in the present sea level corrections may be improved by inclusion of wave model information.

Assessment of Altimetric Range and Geophysical Corrections and Mean Sea Surface Models—Impacts on Sea Level Variability around the Indonesian Seas

“…This method can resolve the problem of parameter-derived SSB due to the complexity of the relation between sea state bias, wave height and wind speed [65]. The Tran (non-parametric) model, proposed by [39,40], uses three-input estimators, SWH, U 10 and the mean gravity wave period (T m ) from a numerical ocean wave model, NOAA's WAVEWATCH III (NWW3). The Tran model gives a good result by reducing SSH variance at global and regional scales.…”

“…The SSB correction is commonly estimated as a function of two input parameters: significant wave height and wind speed. In the SBB Tran model [39,40], ocean wave period data are used within a three-input estimator. For TOPEX, the parameter model BM-4 based on [41] and the non-parametric CLS model [42] were applied.…”

Improving Jason-2 Sea Surface Heights within 10 km Offshore by Retracking Decontaminated Waveforms

“…The SSB is the sum of electromagnetic bias, skewness bias and tracker bias [25,45]. The accuracy of the SSB is mainly up to the accuracy of estimated significant wave height (SWH), varying with different retrackers.…”

“…A summary of the significant achievements was given in [24], including improvements in waveform retracking, tropospheric corrections, tide models, and dynamic atmosphere corrections. There are also some studies on SSB correction, aiming at better modelling the error induced by ocean surface waves, whitecaps and foam [25][26][27][28][29]. Among these improvements, waveform retracking is getting considerable attention in recent years, because of its evident effect on the enhancement of altimeter measurements.…”

A Conceptually Simple Modeling Approach for Jason-1 Sea State Bias Correction Based on 3 Parameters Exclusively Derived from Altimetric Information

“…Before fitting the models, SSB estimates can be retrieved by sea surface height (SSH) differences at crossover points, along collinear tracks or directly estimated from the residuals between SSH and an MSS over the SWH and U10 domain [9]. More recently, [10] suggested that these methods, solely based on wave and wind information from the altimeter may be improved if additional surface gravity wave field measurements become available from numerical ocean wave model data, leading to an enhanced 3-dimensional (3D) SSB model derived from SWH, U10 and a third predictor characterized by the mean wave period (Tm), retrieved from WAVEWATCH III (WW3) [11]. This improved SSB model achieves positive results in reducing SSH variance both at global and regional scales, but the required external information from WW3 adds a new source of uncertainty which may not be directly related to the altimetric signal.…”