Reconstructing Coastal Sediment Dynamics with 4DVarNet: Neural Data Assimilation Leveraging Models and Satellite Data

Talk
In session Artificial Intelligence/ Machine Learning methods in Earth System Sciences , Sept. 3, 2025, 13:30 – 15:15
Exact timing: 15:00 – 15:15
Room info: Lecture Hall

This study presents an end-to-end deep learning framework, 4DVarNet, for reconstructing high-resolution spatiotemporal fields of suspended particulate matter (SPM) in the German Bight under realistic satellite data gaps. Using a two-phase approach, the network is first pretrained on gap-free numerical model outputs masked with synthetic cloud patterns, then fine-tuned against sparse CMEMS observations with an additional independent validation mask. The framework architecture embeds a trainable dynamical prior and a convolutional LSTM solver to iteratively minimize a cost function that balances data agreement with physical consistency. The framework is applied for one year data (2020) of real observations (CMEMS) and co-located model simulations, demonstrating robust performance under operational conditions. Reconstructions capture major spatial patterns with correlation R2 = 0.977 and 50% of errors within ± 0.2 mg/L, even when 27% of days lack any observations. Sensitivity experiments reveal that removing 60% of available data doubles RMSE and smooths fine-scale SPM spatial features. Moreover, increasing the assimilation window reduces edge discontinuities between the data-void area and the adjacent data-rich region, whereas degrades sub-daily variability. Extending 4DVarNet to higher temporal resolution (hourly) reconstruction will require incorporating tidal dynamics to account for SPM resuspension, enabling real-time sediment transport forecasting in coastal environments.