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}.

翻译：天气预报不仅需要精确性，还需要具备概率预测能力。然而，确定性天气预报方法不支持概率预测，而概率模型往往精度较低。为解决这些挑战，本文提出了**确定性引导扩散模型（DGDM）**用于概率天气预报，融合了确定性与概率方法的优势。在前向过程中，确定性与概率模型均以端到端方式训练；在反向过程中，天气预报将确定性模型的预测结果作为概率模型的中间起点。通过将确定性模型与概率模型以这种方式融合，DGDM能够提供精确预报的同时实现概率预测。为评估DGDM，我们在全球天气预报数据集（WeatherBench）和通用视频帧预测基准（Moving MNIST）上进行了测试。此外，我们引入并评估了太平洋西北风暴（PNW）-台风气象卫星数据集，以验证DGDM在高分辨率区域预报中的有效性。实验结果表明，DGDM不仅在全球预报中取得了最优结果，在区域预报中也实现了领先性能。相关代码已开源：\url{https://github.com/DongGeun-Yoon/DGDM}