  The generation of initial conditions via accurate data assimilation is
crucial for weather forecasting and climate modeling. We propose DiffDA as a
denoising diffusion model capable of assimilating atmospheric variables using
predicted states and sparse observations. Acknowledging the similarity between
a weather forecast model and a denoising diffusion model dedicated to weather
applications, we adapt the pretrained GraphCast neural network as the backbone
of the diffusion model. Through experiments based on simulated observations
from the ERA5 reanalysis dataset, our method can produce assimilated global
atmospheric data consistent with observations at 0.25 deg (~30km) resolution
globally. This marks the highest resolution achieved by ML data assimilation
models. The experiments also show that the initial conditions assimilated from
sparse observations (less than 0.96% of gridded data) and 48-hour forecast can
be used for forecast models with a loss of lead time of at most 24 hours
compared to initial conditions from state-of-the-art data assimilation in ERA5.
This enables the application of the method to real-world applications, such as
creating reanalysis datasets with autoregressive data assimilation.
