楊 偉

Large inaccuracies remain in the traditional convective cloud identification system over the plateau area struggles to capture mid- and low-level clouds due to the complex topographic effects influencing cloud pressure. Besides, the lack of efficient nighttime cloud-type products hinders progress in the research on the diurnal cycle and seasonal variation in convective clouds (including deep convection and cumulus clouds) over the Tibet Plateau (TP). In this study, we incorporated Shapley additive explanation (SHAP) tuning into the fundamental machine learning CatBoost Classifier technology, which was applied to a 24-h convective cloud detection algorithm utilizing cloud top temperature (CTT) and optical thickness data derived from the Himawari-8 infrared channels. This specifically tackles the problem of underestimating cumulus clouds in plateau areas. This innovative product enables capturing important processes of deep convection, especially for cumulus clouds, facilitating a comprehensive spatial-temporal analysis of the entire TP region. The results confirm that the new algorithm shows significant improvements in cumulus detection compared to the official cloud product of Himawari-8. In addition, the deep convective clouds have also improved from 35.85% to 63.05% for hit rate (HR) value. The analysis reveals a notable diurnal variation in convective cloud activity over the TP, predominantly occurring from noon to night. This finding underscores the influential heating role of the TP in convective activity.