Generating observation guided ensembles for data assimilation with
  denoising diffusion probabilistic model
Yuuichi Asahi Yuta Hasegawa Naoyuki Onodera Takashi Shimokawabe Hayato Shiba Yasuhiro Idomura
This paper presents an ensemble data assimilation method using the pseudo
ensembles generated by denoising diffusion probabilistic model. Since the model
is trained against noisy and sparse observation data, this model can produce
divergent ensembles close to observations. Thanks to the variance in generated
ensembles, our proposed method displays better performance than the
well-established ensemble data assimilation method when the simulation model is
imperfect.