Regional Analysis of West African Convective Systems and Characteristics
February 27, 2012
Hosted by Steve Rutledge (advisor), Wei-Kuo Tao (co-advisor), Chris Kummerow, Sue van den Heever, Rob Cifelli, Steve Reising (Electrical and Computer Engineering)
Recent studies of the West African monsoon have resulted in the need for an improved understanding of mesoscale variability to fully appreciate this complex system. Analysis of mesoscale precipitating system properties can lead to a better conceptual and simulated representation of feedback mechanisms between spatial scales. Convective and microphysical characteristics of West African convective systems are explored using various observational data sets. Focus is directed toward meso â€“Î± and â€“Î² scale convective systems to illuminate small-scale variability and contextualize the interaction of small-scale features with the largerâ€“scale.
Results from a month of data at three unique ground-based radar sites in 2006, along with a 13-year (1998â€“2010) climatology of seven regions using precipitation radar and passive microwave data from the NASA Tropical Rainfall Measuring Mission satellite, show large zonal and meridional variability of convective characteristics. Variability is primarily controlled by environmental properties, such as vertical wind shear and CAPE/CIN. Despite the fact that regionality seems to be the primary cause of observed differences, African easterly waves (AEWs) preferentially enhance mesoscale convective system (MCS) strength and modify microphysical processes in certain regions.
The performance of the Goddard Cumulus Ensemble cloud-resolving model to reproduce observed differences between two MCSs (one AEW-forced, the other not) was also analyzed. Results indicate that while simulations exhibit biases in vertical mass distribution, they produce overall skillful statistical depictions of the two MCSs that were simulated. Future global simulations may be able to capture unique aspects of convective systems resulting from thermodynamic and dynamic environmental aspects using embedded cloud-resolving models.