Deterministic Guidance Diffusion Model for Probabilistic Weather
  Forecasting
Donggeun Yoon Minseok Seo Doyi Kim Yeji Choi Donghyeon Cho
Weather forecasting requires not only accuracy but also the ability to
perform probabilistic prediction. However, deterministic weather forecasting
methods do not support probabilistic predictions, and conversely, probabilistic
models tend to be less accurate. To address these challenges, in this paper, we
introduce the \textbf{\textit{D}}eterministic \textbf{\textit{G}}uidance
\textbf{\textit{D}}iffusion \textbf{\textit{M}}odel (DGDM) for probabilistic
weather forecasting, integrating benefits of both deterministic and
probabilistic approaches. During the forward process, both the deterministic
and probabilistic models are trained end-to-end. In the reverse process,
weather forecasting leverages the predicted result from the deterministic
model, using as an intermediate starting point for the probabilistic model. By
fusing deterministic models with probabilistic models in this manner, DGDM is
capable of providing accurate forecasts while also offering probabilistic
predictions. To evaluate DGDM, we assess it on the global weather forecasting
dataset (WeatherBench) and the common video frame prediction benchmark (Moving
MNIST). We also introduce and evaluate the Pacific Northwest Windstorm
(PNW)-Typhoon weather satellite dataset to verify the effectiveness of DGDM in
high-resolution regional forecasting. As a result of our experiments, DGDM
achieves state-of-the-art results not only in global forecasting but also in
regional forecasting. The code is available at:
\url{https://github.com/DongGeun-Yoon/DGDM}.