【摘要】 气象卫星通常能够早于天气雷达发现积云发生发展的前兆信号。为了充分挖掘新一代静止气象卫星多通道数据在石家庄短临天气预报中的作用，本文利用葵花8/9号卫星和天气雷达开展石家庄地区对流初生判别研究，并建立了基于机器学习方法的客观对流初生判别模型。通过分析石家庄地区云图特征，建立了适用于石家庄地区的积云目标识别方法，并结合多目标跟踪算法建立了石家庄地区的对流单体数据集。在进行多目标跟踪的过程中，面对直接剔除卷云和晴空后造成的积云云体较破碎、难以跟踪等难题，本文针对性地提出以完整云团作为研究对象的方法，实现了积云生命周期样本的完整提取。基于对流单体数据集，结合天气雷达观测数据，寻找出现35 dBZ以上回波的积云单体，记录首次出现35 dBZ回波时刻，将之作为对流初生的发生时刻。通过对多通道亮温变化特征与积云发展过程的对照分析，发现积云发展成为强对流过程中，石家庄地区的10.4μm亮温呈现下降趋势，12.4μm和10.4μm亮温差和三通道亮温差呈现上升趋势。据此特征分析筛选出有效的影响因子，建立了随机森林对流初生判别模型，该模型能够有效实现石家庄地区对流初生预报。该模型在对石家庄地区对流初生过程的测试中，实现了92%的有效命中率，相应的虚警率为31%。该算法能够在天气雷达图上没有任何回波的时刻，有效地找到将要发展成为强对流的积云单体，提升了石家庄地区强对流天气预警的时效性。更多还原

【Abstract】 Geostationary meteorological satellites can detect precursor signals of cumulus cloud development earlier than weather radar systems, making them valuable for convective initiation forecasting.To leverage this advantage, various algorithms have been developed, typically involving cloud detection, the removal of cirrus and mature clouds, overlap tracking, and convective initiation identification.Among these steps, the removal of cirrus and mature clouds is particularly crucial, as these cloud types can obscure developing cumulus clouds.However, existing methods face challenges such as cumulus cloud fragmentation after cirrus and mature cloud removal, difficulties in applying overlap tracking to complex cloud imagery, and limitations in threshold-based convective initiation identification.To address these issues, this study introduces several targeted improvements.First, a novel approach is proposed that treats complete cloud clusters as the primary research subject, allowing for the comprehensive extraction of cumulus lifecycle samples.Second, the Hungarian algorithm is incorporated to enhance multi-target tracking capabilities.Third, a random forest algorithm is employed to improve the accuracy of convective initiation identification.This study utilizes data from Himawari-8/9 satellites and weather radar observations to analyze convective initiation in the Shijiazhuang region.A cumulus cloud identification method, specifically tailored to the region, was developed and combined with a multi-target tracking algorithm to construct a detailed dataset of convective cells.By integrating this dataset with radar observations, cumulus clouds associated with weather processes exhibiting reflectivity values above 35 dBZ were identified.The time at which reflectivity first reached 35 dBZ was recorded as the convective initiation time, providing a robust dataset for further analysis.A comparative analysis of multi-channel brightness temperature variations and cumulus cloud development processes revealed key trends.Specifically, as cumulus clouds evolved into strong convective systems, the 10.4 μm brightness temperature in the Shijiazhuang region exhibited a decreasing trend, while the brightness temperature difference between 12.4 μm and 10.4 μm, as well as the three-channel brightness temperature difference(TTD),showed an increasing trend.These patterns were used to identify key factors influencing convective initiation.Based on these findings, a random forest model was developed for convective initiation forecasting in the Shijiazhuang region.The model demonstrated strong performance during testing, achieving a 92% probability of detection(POD) and a 31% false alarm rate(FAR).These results indicate that the model effectively identifies cumulus clouds likely to develop into strong convective systems, even before radar-detectable echoes emerge.A key contribution of this study is its potential to improve the timeliness of severe convective weather warnings in the Shijiazhuang region.By leveraging satellite data and advanced machine learning techniques, the proposed algorithm can detect developing cumulus clouds earlier than traditional radar-based methods.This capability is particularly valuable in regions where severe convective weather significantly impacts agriculture, transportation, and public safety.The integration of Himawari-8/9 satellite data with weather radar observations enhances the understanding of convective processes, leading to more accurate and timely forecasts.In conclusion, this study represents a significant advancement in convective initiation forecasting by addressing key challenges in cloud detection, tracking, and identification while integrating machine learning techniques.The successful application of this model in the Shijiazhuang region demonstrates its potential for broader use in other convective weather-prone areas.Future research could focus on refining the model, expanding the dataset, and exploring additional machine learning approaches to further enhance forecasting accuracy and reliability.This study not only advances the scientific understanding of convective processes but also has practical implications for improving weather warning systems and mitigating severe weather impacts.更多还原