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Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms

Earth Sciences

Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms

J. Gou and B. Soja

Discover a groundbreaking self-supervised data assimilation model by researchers Junyang Gou and Benedikt Soja that accurately captures global total water storage anomalies (TWSAs) using advanced satellite data. This innovative approach enhances local natural hazard monitoring and reveals insights into the water cycle's dynamics influenced by human activities.

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