Global Weather States and their Properties from Passive and Active Satellite Cloud Retrievals

In this study, we apply a clustering algorithm to ISCCP Cloud Optical Thickness – Cloud Top Pressure histograms in order to derive Weather States (WS) for the global domain. The cloud property distribution within each WS is examined and the geographical variability of each WS is mapped. Once the global weather states are derived, a combination of CloudSat/CALIPSO vertical cloud structure retrievals is used to derive the vertical distribution of the cloud field within each WS. Finally, the dynamic environment and the radiative signature of the weather states are derived and their variability is examined. The cluster analysis produces a comprehensive description of global atmospheric conditions through the derivation of eleven WS, each representing a distinct structure characterized by the horizontal distribution of cloud optical depth and cloud top pressure. Matching those distinct WS with cloud vertical profiles derived from CloudSat and CALIPSO retrievals shows that the ISCCP weather states include unique distributions of vertical layering that correspond well to the horizontal structure of cloud properties. Matching the derived weather states with vertical velocity measurements shows a normal progression in dynamic regime when moving from the most convective to the least convective WS. Time trend analysis of the weather states shows a sharp increase of the fair-weather WS in the 1990s and a flattening of that increase in the 2000s. The fact that the fair-weather WS is the one with the lowest radiative cooling capability implies that this behavior has contributed excess radiative warming to the global radiative budget during the 1990s.