Characterizaton of convective systems in terms of cloud to ground lightning, cloud kinematics, vertical structure and precipitation

Date of Completion

January 2007


Atmospheric Sciences|Remote Sensing




In vast regions of the world, especially over global oceans and sparsely populated land areas, satellite remote sensing is often the only practical mean of observing and tracking storm systems. This study investigates convective system characteristics from an array of remote sensing observations in two distinct convective regions on Earth (USA and Africa). It employs 112-hourly satellite Infrared observations along with cloud-to-ground lightning and hourly radar rainfall fields over US, and satellite radar (PR) and long-range lightning data over Africa. Convective systems are tracked across both the continental US and Africa for different seasons. ^ The properties of Convective Systems (CSs) at their different life stages are investigated in terms of Cloud to Ground (CG) lightning flashes, cloud kinematics, and vertical structure of radar reflectivity. The rate of area expansion at the initiation stage of the convective systems is found a good proxy for the longevity of the storms. Rain Volume (RV) is found to be highly correlated to the convective systems' area extent and number of CG lightning. The correlation varies with the life cycle of storms and season. The convective rain type derived from PR is found to be the dominant rain type in the growth and mature stages of thunderstorms. The vertical precipitation structure derived from PR is also shown to have storm life cycle dependence. ^ Two main satellite rain retrievals: PERSIANN Neural Network algorithm product at 4-km/hourly resolution and NASA's Multi-Satellite Real-Time algorithm product (3B41-RT) at 25-km/hourly resolution are used to investigate storm maturity effects on satellite retrieval error. PERSIANN and 3B41-RT retrievals are compared with radar rainfall at different life stages of the tracked convective systems. The study shows that there is significant life cycle and storm type/duration dependence in the performance of 3B41-RT, while PERSIANN, an algorithm that accounts for cloud type information, exhibits weaker dependence. ^