  In recent years traditional numerical methods for accurate weather prediction
have been increasingly challenged by deep learning methods. Numerous historical
datasets used for short and medium-range weather forecasts are typically
organized into a regular spatial grid structure. This arrangement closely
resembles images: each weather variable can be visualized as a map or, when
considering the temporal axis, as a video. Several classes of generative
models, comprising Generative Adversarial Networks, Variational Autoencoders,
or the recent Denoising Diffusion Models have largely proved their
applicability to the next-frame prediction problem, and is thus natural to test
their performance on the weather prediction benchmarks. Diffusion models are
particularly appealing in this context, due to the intrinsically probabilistic
nature of weather forecasting: what we are really interested to model is the
probability distribution of weather indicators, whose expected value is the
most likely prediction.
  In our study, we focus on a specific subset of the ERA-5 dataset, which
includes hourly data pertaining to Central Europe from the years 2016 to 2021.
Within this context, we examine the efficacy of diffusion models in handling
the task of precipitation nowcasting. Our work is conducted in comparison to
the performance of well-established U-Net models, as documented in the existing
literature. Our proposed approach of Generative Ensemble Diffusion (GED)
utilizes a diffusion model to generate a set of possible weather scenarios
which are then amalgamated into a probable prediction via the use of a
post-processing network. This approach, in comparison to recent deep learning
models, substantially outperformed them in terms of overall performance.

Machine Learning;Artificial Intelligence;Atmospheric and Oceanic Physics