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UNetKF: Ensemble U-Net Kalman Filter
  • Feiyu Lu
Feiyu Lu

Corresponding Author:feiyu.lu@noaa.gov

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Machine learning techniques have seen a tremendous rise in popularity in weather and climate sciences. Data assimilation (DA), which combines observations and numerical models, has great potential to incorporate machine learning and artificial intelligence (ML/AI) techniques. In this paper, we use U-Net, a type of convolutional neutral network (CNN), to predict the ensemble covariances in the Ensemble Kalman Filter (EnKF) algorithm.
Using a 2-layer quasi-geostrophic model, U-Nets are trained using data from existing EnKF systems. The U-Nets are then used to predict the flow-dependent covariance matrices in U-Net Kalman Filter (UNetKF) experiments, which are compared to traditional 3-dimensional variational (3DVar) and EnKF methods. The performance of UNetKF can match or exceed that of 3DVar, or EnKF with ensemble sizes up to 80. We also demonstrate that trained U-Nets can be transferred to a higher-resolution model for UNetKF, which again performs competitively to 3DVar and EnKF, particularly for small ensemble sizes.
08 Sep 2023Submitted to ESS Open Archive
11 Sep 2023Published in ESS Open Archive