  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:
https://github.com/yongquan-qu/SLAMS
