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Characteristics of Pre-summer Daytime Cloud Regimes over Coastal South China from the Himawari-8 Satellite
Authors:Mingxin LI  Yali LUO  Min MIN
Abstract:Using the high spatiotemporal resolution (2 km-and-10 min) data from the Advanced Himawari Imager onboard the Himawari-8 satellite, this study documents the fine-scale characteristics of daytime cloud regimes (CRs) over coastal South China during the pre-summer rainy season (April–June). Six CRs (CR1–CR6) are identified based on the joint frequency distribution of cloud top brightness temperature and cloud optical thickness, namely, the optically thin-to-moderate cloud mixture, optically thin warm clouds with cirrus, optically thick warm clouds, weak convective cloud mixture, strong convective clouds, and extreme, deep convective clouds. The optically thick warm clouds are the major CR during April and May, with higher frequencies over land, especially along the urban agglomeration, rather than the offshore which may be an indicator of the higher aerosol concentrations being a contributing factor over the cities. The CRs with weak convective cloud mixtures and strong convective clouds appear more frequently over the land, while the two CRs with optically thinner clouds occur mainly offshore. Synoptic flow patterns (SPs) are objectively identified and examined focusing on those favoring the two major rain-producing CRs (CR5 and CR6) and the highly reflective CR with optically thick warm clouds (CR3). The two SPs favoring CR5 and CR6 are characterized by abundant moisture with low-level jets after monsoon onset, and a northwest high-southeast low pattern with strong dynamic convergence along the coastline, respectively. The non-convective CR3 with high reflectance is related to a SP that features the western North Pacific subtropical high extending more westward, leading to a moderate moisture supply and a wide range of convective available potential energy, but also, large convective inhibition.
Keywords:cloud regime  monsoon onset  synoptic pattern  satellite data  spatiotemporal distribution
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