Data-Driven Mapping With Prediction Neural Network for the Future Wide-Swath Satellite Altimetry

Two-dimensional mapping of sea surface height (SSH) for future wide-swath satellite altimetry (WSA) is a challenge at present. So far, considering the utilization of data-driven methods is a new researching direction for SSH mapping. In general, the data-driven mapping methods rely on the spatial-temporal relationship of the observations. These methods require training in large volumes, and the time cost is high, especially for the WSA observations. This paper proposed the prediction neural networks for mapping (Mapping-PNN) method to improve the training efficiency and maintain stable data and mapping capabilities. By 10-year wide-swath satellite along track observing system simulation experiments (OSSEs) on the HYCOM data, the experiment results indicate that the method introduced in this paper can improve the training efficiency and meet the grid mapping expectations. Compared with other methods, the root mean squared error (RMSE) of the mapping-PNN method can be limited within the range of similar to 1.8 cm, and the new method can promote the observation of the ocean phenomena scale with < similar to 40 km, which reaches state of the art.

Automated summary

The two-dimensional mapping of sea surface height for future wide-swath satellite altimetry is currently a challenge, with data-driven methods being a new research direction. The proposed Mapping-PNN method aims to improve training efficiency and maintain stable data and mapping capabilities. Experimental results show that this method can enhance training efficiency and meet grid mapping expectations, with a RMSE limited within approximately 1.8 cm, and promoting observation of ocean phenomena scale within 40 km.