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This paper presents an ensemble data assimilation method using the pseudoensembles generated by denoising diffusion probabilistic model. Since the modelis trained against noisy and sparse observation data, this model can producedivergent ensembles close to observations. Thanks to the variance in generatedensembles, our proposed method displays better performance than thewell-established ensemble data assimilation method when the simulation model isimperfect.

Generating observation guided ensembles for data assimilation with denoising diffusion probabilistic model