Analysis of Mesoscale Atmospheric Flows above Mature Deep Convection Using Super Rapid Scan Geostationary Satellite Data

作者：Apke Jason M; Mecikalski John R; Jewett Christopher P

来源：Journal of Applied Meteorology and Climatology, 2016, 55(9): 1859-1887.

DOI：10.1175/JAMC-D-15-0253.1

Super Rapid Scan Operations for the Geostationary Operational Environmental Satellite (GOES) R series (SRSOR) using GOES-14 have made experimentation with 1-min time-step data possible prior to the launch of the new satellite. A mesoscale atmospheric motion vector (mAMV) program is utilized in SRSOR with a Barnes analysis to produce objectively analyzed flow fields at the cloud tops of deep convection. Two non-supercell and four supercell storm cases are analyzed. Data from the SRSOR mAMV analysis are compared with both multi-Doppler analyses when available and idealized convection cases within the Weather Research and Forecasting (WRF) Model framework. It is found that using SRSOR data provides several additional trackable targets to produce mAMVs in rapidly "bubbling'' regions at the deep convective cloud-top level not previously available at lower temporal resolutions (, 1 min). Results also show that supercell storm cases produce long-lived maxima in SRSOR cloud-top divergence (CTD) and "couplet'' signatures in cloud-top vorticity (CTV), which when compared with idealized WRF Model simulations appear to form as a result of environmental horizontal vorticity tilting. Nonsupercell convection in contrast produced weaker, short-lived CTD signatures and no "CTV couplet'' signatures. These case study results suggest that with SRSOR data it might be possible to uniquely identify supercells using only mAMV-derived flow fields.

• 出版日期2016-9