Research on sea level inversion method from airborne radar altimeter

The airborne radar altimeter can be extrapolated to a variety of parameters, including sea surface height, sea surface wind speed, significant wave height, and the topography of land, sea ice and ice cap. However, the airborne radar altimeter observation data contains signal error terms such as airborne platform jitter and ocean waves, which will lead to a large bias in the observation data. Here, we propose a method based on the combination of bandpass filtering and adaptive feature AI analysis to achieve the inversion of high-resolution sea level anomaly (SLA) data from airborne radar altimeter aliased signals.

For the airborne altimeter along-track data, statistical analyses were first performed. After that, the along-track data are filtered to remove the influence of ocean waves signals and flight platform oscillations, and the secondary interpolation is fitted based on the interval of the airborne altimeter data. According to the sampling interval of the altimeter data, the mean sea surface (MSS) and tide data under the along-track are processed to obtain the corresponding SLA data. The same interpolation method is used to process AVISO and SWOT L3 data. Finally, through the deep learning framework, the adaptive feature AI analysis is constructed to invert the SLA data, optimise the model and achieve accurate SLA prediction. The experimental results show that the RMSE of the SLA of the airborne altimeter inversion data with the along-track SWOT L3 and AVISO data are 1.12cm and 0.44cm, respectively, and the airborne altimeter data can acquire more small-scale change signals. This study verifies the working mechanism of the new system payload and lays a solid data and algorithm foundation for the development of subsequent satellite payloads.