Integration of Ground Radar and Satellite dataset for Studying Cloud and Radiative Property of Deep Convective Systems

Cirrus anvil clouds associated with Deep Convective Systems (DCS) are dynamically connected to the water budget of the upper troposphere and have strong influence on climate processes through modulating the atmospheric radiation budget. This study have developed a unique technique to classify various precipitating deep cloud and non-precipitating anvil regions of a DCS by using combination of WSR-88D ground radar network and GOES geostationary satellite. With this classification, ground-based and satellite dataset are merged to detect and identify the parent convection and resulting thick/thin anvil clouds simultaneously.

This hybrid classification builds upon a 3-D radar classification algorithm that separates convective and stratiform rain, transition deep clouds, mixed and ice phase anvils, and other thinner clouds that can only be detected by GOES satellite (e.g. thin ice anvil, low level cumulus). Cloud macrophysical and microphysical properties from DCSs in midlatitude over two summer (JJA) seasons are obtained, including cloud areal coverage, cloud-top height and thickness, optical depth, ice water path, effective particle size, and IR temperature. Cloud Radiative Forcing (CRF) at the Top of Atmosphere (TOA) for each classified cloud types are computed. Such accurate separation of various cloud types associated DCS have not been previously possible from passive optical satellites. Quantifying these cloud properties and their CRF for precipitating and non-precipitating DCS clouds will shed lights on assessing their impact on regional climate change in the future.