发表机构
GFZ Helmholtz Centre for Geosciences; Technical University of Berlin; University of Potsdam; University of California Los Angeles; German Aerospace Center; Columbia University; Karlsruhe Institute of Technology; Einstein Center Digital Future（亥姆霍兹波茨坦地学研究中心; 柏林工业大学; 波茨坦大学; 加利福尼亚大学洛杉矶分校; 德国航空航天中心; 哥伦比亚大学; 卡尔斯鲁厄理工学院; 爱因斯坦数字未来中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出概率模型PROSWIN，结合深度神经网络与分布回归，利用太阳图像和磁图提前四天预报太阳风速度，引入预测得分指标，在14年数据上实现校准良好的不确定性，优于单值模型。
AI 中文摘要
准确预测快速太阳风条件具有挑战性，因为不确定性很大，且传统单值预测模型无法量化这些不确定性。特别是，高速太阳风流（HSSs）可能对技术基础设施造成损害，若无概率预报，则无法可靠评估其风险。我们提出了PROSWIN，一种概率机器学习模型，可提前四天预报地球处每小时太阳风速度（SWS）。该方法通过深度神经网络结合分布回归算法，将太阳图像和磁图相结合。由于标准误差指标低估了HSS峰值的重要性，我们进一步引入了预测得分，这是一种模型选择指标，同时奖励时间线和HSS峰值准确性。在14年的数据上，我们的预报实现了校准良好的不确定性（平均偏差<1%）。使用连续排名概率得分（CRPS）这一评估分布准确性的指标，我们获得了时间线CRPS为41.0 km/s，HSS峰值CRPS为45.3 km/s，预测得分为42.3 km/s。我们发现171 Å通道是对通常使用的193 Å和211 Å通道的重要补充，且用于模型选择的预测得分提高了模型的适用性。与文献中选定的模型相比，我们的模型是唯一一个在时间线和HSS峰值上均准确的模型，而非在两者之间权衡。这些结果支持了概率太阳风模型优于单值模型的优势。所引入的方法也可迁移至其他预报问题。
英文摘要
Accurately predicting fast solar wind conditions is challenging, as uncertainties are large and unquantified by traditional single-value prediction models. In particular, the risks of high-speed solar wind streams (HSSs), which can cause damage to technological infrastructure, cannot be reliably assessed without probabilistic forecasts. We present PROSWIN, a probabilistic machine learning model that forecasts the hourly solar wind speed (SWS) at Earth with a four-day lead time. The approach combines solar images and magnetograms using a deep neural network coupled to a distributional regression algorithm. Because standard error metrics underweight the relevance of HSS peaks, we further introduce the prediction score, a model-selection metric that jointly rewards timeline and HSS peak accuracy. On 14 years of data, our forecast achieves very well-calibrated uncertainties (<1% average deviation). Using the continuous ranked probability score (CRPS), a metric that assesses distributional accuracy, we obtain a timeline CRPS of 41.0 km/s, an HSS peak CRPS of 45.3 km/s, and a prediction score of 42.3 km/s. We find that the 171 Å channel is an important complement to the typically used 193 Å and 211 Å channels and that the prediction score for model selection improves the applicability of the model. Compared to selected models from the literature, ours is the only one that is accurate for both timeline and HSS peak values, rather than trading one off against the other. These results support the advantages of probabilistic over single-value solar wind models. The introduced methods are also transferable to other forecasting problems.
