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Weather forecasting requires not only accuracy but also the ability toperform probabilistic prediction. However, deterministic weather forecastingmethods do not support probabilistic predictions, and conversely, probabilisticmodels tend to be less accurate. To address these challenges, in this paper, weintroduce the \textbf{\textit{D}}eterministic \textbf{\textit{G}}uidance\textbf{\textit{D}}iffusion \textbf{\textit{M}}odel (DGDM) for probabilisticweather forecasting, integrating benefits of both deterministic andprobabilistic approaches. During the forward process, both the deterministicand probabilistic models are trained end-to-end. In the reverse process,weather forecasting leverages the predicted result from the deterministicmodel, using as an intermediate starting point for the probabilistic model. Byfusing deterministic models with probabilistic models in this manner, DGDM iscapable of providing accurate forecasts while also offering probabilisticpredictions. To evaluate DGDM, we assess it on the global weather forecastingdataset (WeatherBench) and the common video frame prediction benchmark (MovingMNIST). We also introduce and evaluate the Pacific Northwest Windstorm(PNW)-Typhoon weather satellite dataset to verify the effectiveness of DGDM inhigh-resolution regional forecasting. As a result of our experiments, DGDMachieves state-of-the-art results not only in global forecasting but also inregional forecasting. The code is available at:\url{https://github.com/DongGeun-Yoon/DGDM}.

Deterministic Guidance Diffusion Model for Probabilistic Weather Forecasting