The core codes for the algorithm presented in our paper Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms.
The product (April 2002 to December 2019) is available at https://doi.org/10.3929/ethz-b-000648738.
The core codes for our model are included in ./code_v2019.
The trained model and weights for our paper are provided in ./model.
Contact: Junyang Gou (jungou@ethz.ch)
2024-02-12: Our paper is published in Nature Water