Analysis of Cumulus Cloud Updrafts as Observed with 1-Min Resolution Super Rapid Scan GOES Imagery

作者：Mecikalski John R^*; Jewett Christopher P; Apke Jason M; Carey Larry D

来源：Monthly Weather Review, 2016, 144(2): 811-830.

DOI：10.1175/MWR-D-14-00399.1

• Physical Meteorology and Climatology
• Buoyancy
• Convective-scale processes
• Cumulus clouds
• Observational techniques and algorithms
• Cloud tracking
• cloud motion winds
• Kinematics
• Satellite observations

A study was undertaken to examine growing cumulus clouds using 1-min time resolution Super Rapid Scan Operations for Geostationary Operational Environmental Satellite-R (GOES-R) (SRSOR) imagery to diagnose in-cloud processes from cloud-top information. SRSOR data were collected using GOES-14 for events in 2012-14. Use of 1-min resolution SRSOR observations of rapidly changing scenes provides far more insights into cloud processes as compared to when present-day 5-15-min time resolution GOES data are used. For midday times on five days, cloud-top temperatures were cataloged for 71 cumulus clouds as they grew to possess anvils and often overshooting cloud tops, which occurred over 33-152-min time periods. Characteristics of the SRSOR-observed updrafts were examined individually, on a per day basis, and collectively, to reveal unique aspects of updraft behavior, strength, and acceleration as related to the ambient stability profile and cloud-top glaciation. A conclusion is that the 1-min observations capture two specific cumulus cloud growth periods, less rapid cloud growth between the level of free convection and the 0 degrees C isotherm level, followed by more rapid growth shortly after the time of cloud-top glaciation. High correlation is found between estimated vertical motion (w) and the amount of convective available potential energy (CAPE) realized to the cloud-top level as clouds grew, which suggests that updrafts were responding to the local buoyancy quite strongly. Influences of the environmental buoyancy profile shape and evidence of entrainment on cloud growth are also found through these SRSOR data analyses.

• 出版日期2016-2