Learning 4DVAR inversion directly from observations
This work addresses data assimilation challenges for fields like weather forecasting or environmental modeling, though it appears incremental as it combines existing techniques.
The paper tackled the problem of learning variational data assimilation directly from partial and noisy observations by integrating deep learning with the mechanistic constraints of the 4DVAR algorithm, resulting in a method that achieved the desired inversion with regularizing properties and computational benefits.
Variational data assimilation and deep learning share many algorithmic aspects in common. While the former focuses on system state estimation, the latter provides great inductive biases to learn complex relationships. We here design a hybrid architecture learning the assimilation task directly from partial and noisy observations, using the mechanistic constraint of the 4DVAR algorithm. Finally, we show in an experiment that the proposed method was able to learn the desired inversion with interesting regularizing properties and that it also has computational interests.