Abstract
Robust integration of physical knowledge and data is key to improve computational simulations, such as Earth system models. Data assimilation is crucial for achieving this goal because it provides a systematic framework to calibrate model outputs with observations, which can include remote sensing imagery and ground station measurements, with uncertainty quantification. Conventional methods, including Kalman filters and variational approaches, inherently rely on simplifying linear and Gaussian assumptions, and can be computationally expensive. Nevertheless, with the rapid adoption of data-driven methods in many areas of computational sciences, we see the potential of emulating traditional data assimilation with deep learning, especially generative models. In particular, the diffusion-based probabilistic framework has large overlaps with data assimilation principles: both allows for conditional generation of samples with a Bayesian inverse framework. These models have shown remarkable success in text-conditioned image generation or image-controlled video synthesis. Likewise, one can frame data assimilation as observation-conditioned state calibration. In this work, we propose SLAMS: Score-based Latent Assimilation in Multimodal Setting. Specifically, we assimilate in-situ weather station data and ex-situ satellite imagery to calibrate the vertical temperature profiles, globally. Through extensive ablation, we demonstrate that SLAMS is robust even in low-resolution, noisy, and sparse data settings. To our knowledge, our work is the first to apply deep generative framework for multimodal data assimilation using real-world datasets; an important step for building robust computational simulators, including the next-generation Earth system models. Our code is available at: this https URL
Abstract (translated)
Robust地将物理知识和数据集成是提高计算模拟的关键，例如地球系统模型。数据同化对于实现这一目标至关重要，因为它提供了一个系统方法来用观测数据校准模型输出，包括遥感和地面站测量数据，并计算不确定性。传统方法，包括Kalman滤波器和变分方法，本质上依赖于简化线性和 Gaussian 假设，并且计算代价较高。然而，随着数据驱动方法在许多计算科学领域的快速采用，我们看到了使用深度学习模仿传统数据同化前景的潜力，特别是生成模型。 特别是，扩散为基础的概率框架与数据同化原理有很多重叠：两者都允许使用贝叶斯反向框架条件生成样本。这些模型在文本条件图像生成或图像控制的视频合成方面取得了显著的成功。同样，可以将数据同化视为观测条件下的状态估计。 在这篇论文中，我们提出了SLAMS：基于分数的多元设置中的局部同化。具体来说，我们将现场气象站数据和外层卫星图像同化，以校准垂直温度剖面。通过广泛的消融，我们证明了SLAMS在低分辨率、嘈杂和稀疏数据环境中也具有鲁棒性。据我们所知，这是第一个将深度生成框架应用于真实世界数据的多模态数据同化；对于构建稳健的计算模拟器（包括下一代地球系统模型）来说，这是一个重要的进展。 我们的代码可在此处下载：https:// this URL
URL
https://arxiv.org/abs/2404.06665
