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:
https://github.com/DongGeun-Yoon/DGDM.

该论文介绍了一种用于天气预报的确定性导向扩散模型（DGDM），它整合了确定性和概率方法的优势，既能提供准确预测，又能进行概率预测。通过在正向过程中训练确定性模型和概率模型，并利用确定性模型的预测结果作为概率模型的起始点，在全球和区域预测方面，DGDM 取得了最先进的结果。