<ul><li>Machine learning method called score-based diffusion is explored for nowcasting clouds and precipitation in a zero to three hour forecast.</li><li>Three main types of diffusion models were experimented with: standard score-based diffusion model, residual correction diffusion model, and latent diffusion model.</li><li>Results show that these diffusion models can advect existing clouds, generate and decay clouds, and even predict convective initiation.</li><li>The best performing diffusion model was the CorrDiff approach, outperforming traditional U-Net and persistence forecast in root mean squared error.</li></ul>

Score-based diffusion nowcasting of GOES imagery

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