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

Yuuichi Asahi, Yuta Hasegawa, Naoyuki Onodera, Takashi Shimokawabe, Hayato Shiba, Yasuhiro Idomura

Keywords: Deep Learning, Data Assimilation

TL;DR: We propose a data assimilation method to apply local ensemble transform Kalman filter to pseudo ensembles generated with the observation guided denoising diffusion probabilistic model.

Abstract: 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 consistent with 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.

Submission Number: 51