资源环境科技发展态势分析平台

Classifications of cloud data into Cloud Regimes (CRs) and compositing based on meteorological parameters, Dynamic Regimes (DRs), are often used in the analysis of clouds. We compare CR and DR classifications to understand the relative merits of these approaches and develop a comparison methodology for future studies. We apply the Self-Organizing Map technique to International Satellite Cloud Climatology Project (ISCCP) D1 joint histograms to produce a CR and ERA-Interim pressure vertical velocity output to produce a DR. The CR created improves the separation between high-level CRs compared to previous work. Composites of ISCCP joint histogram data using the DR produce coherent groupings similar to those in the CR scheme particularly in regions of ascent. Both classifications display coherent geographical patterns and reproduce relationships between vertical velocity and cloud properties. However, the CR produces more coherent clusters with higher intracluster similarity and a greater range of independent cloud classes. Independent tests of composites using ISCCP FD output show that the regional variability of longwave cloud radiative effect for particular nodes are significantly higher in the DR than the CR scheme suggesting a poorer classification. Composite mean CloudSat reflectivity-altitude joint histograms represent all major cloud types in the CR scheme, while the current DR grouping is less coherent and misses classes. This suggests that the CR scheme is a more useful classification than the DR scheme based solely on vertical velocity data. Contingency table analysis indicates a low association between these classifications, suggesting combining these schemes would be valuable.