Deep Convective Systems (DCSs) have profound impacts on the hydrologic cycle and atmospheric radiation budget. The former is a result of the heavy precipitation from the convective cores (CC) and widespread rainfall in the stratiform rain (SR) regions, and the latter is due to the extensive spatial coverage of the non-precipitating anvil clouds (AC). Yet the relationship between these components has not been well understood, leading to large uncertainties for models across all scales in the simulated DCS structures and their associated impacts.
To improve the understanding of the connections between DCS components, multi-platform remote sensing datasets are used to study the morphological structures and radiative impacts of mid-latitude DCSs. A hybrid classification technique to separate CC, SR, and AC has been developed by jointly analyzing NEXRAD radar and GOES satellite data. An automated satellite tracking method has been used with the classification to dissect the evolution of the DCS components.
DCSs occur frequently during late afternoon in the summer. AC covers 3 times the area of SR and almost an order of magnitude the area of CC. Compared to the clear-sky averages at TOA, DCS components have strong longwave warming effect due to cold cloud tops and strong cooling effect due to high reflected-shortwave insolation. Cloud radiative forcing (CRF) of CC, SR, and AC contribute to 4%, 11%, and 31% of total NET CRF, respectively. The NET TOA radiative effect from the ensemble DCSs is zero, suggesting a neutral cloud radiative feedback influence from mid-latitude summer DCSs.
Composite analyses of DCS life cycle show that maximum system size correlates linearly with system lifetime. The majority of DCSs reach maximum convective intensity (CI) early in the life cycle, producing peak rainfall before SR and AC attain their largest extents. Maximum SR and AC sizes lag behind peak CI and the lag increases linearly with system lifetime. Multivariate regression analysis shows that the size of SR, CC, and intense convective updraft have the highest correlation with AC size, while rainfall rate and wind field only explain a small fraction of the AC size variance.
|School||THE UNIVERSITY OF NORTH DAKOTA|
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