Data assimilation addresses the problem of identifying plausible state trajectories of dynamical systems given noisy or incomplete observations. In geosciences, it presents challenges due to the high-dimensionality of geophysical dynamical systems, often exceeding millions of dimensions. This work assesses the scalability of score-based data assimilation (SDA), a novel data assimilation method, in the context of such systems. We propose modifications to the score network architecture aimed at significantly reducing memory consumption and execution time. We demonstrate promising results for a two-layer quasi-geostrophic model.

翻译：数据同化旨在解决基于含噪或不完整观测识别动力系统合理状态轨迹的问题。在地球科学领域，由于地球物理动力系统的高维特性（通常超过百万维度），该问题面临严峻挑战。本研究评估了一种新型数据同化方法——基于分数的数据同化（SDA）在此类系统中的可扩展性。我们提出对分数网络架构进行改进，旨在显著降低内存消耗与执行时间。针对两层准地转模型，我们展示了具有前景的实验结果。