An Automated Method to Identify Mesoscale Convective Complexes in the Regional Climate Model Evaluation System

Mesoscale convective complexes (MCCs) are large (2 - 3 x 10⁵ km²) nocturnal convectively-driven weather systems that are generally associated with high precipitation events in short durations (less than 12hrs) in various locations through out the tropics and midlatitudes (Maddox 1980). These systems are particularly important for climate in the West Sahel region, where the precipitation associated with them is a principal component of the rainfall season (Laing and Fritsch 1993). These systems occur on weather timescales and are historically identified from weather data analysis via manual and more recently automated processes (Miller and Fritsch 1991, Nesbett 2006, Balmey and Reason 2012). The Regional Climate Model Evaluation System (RCMES) is an open source tool designed for easy evaluation of climate and Earth system data through access to standardized datasets, and intrinsic tools that perform common analysis and visualization tasks (Hart et al. 2011). The RCMES toolkit also provides the flexibility of user-defined subroutines for further metrics, visualization and even dataset manipulation. The purpose of this study is to present a methodology for identifying MCCs in observation datasets using the RCMES framework. TRMM 3 hourly datasets will be used to demonstrate the methodology for 2005 boreal summer. This method promotes the use of open source software for scientific data systems to address a concern to multiple stakeholders in the earth sciences. A historical MCC dataset provides a platform with regards to further studies of the variability of frequency on various timescales of MCCs that is important for many including climate scientists, meteorologists, water resource managers, and agriculturalists. The methodology of using RCMES for searching and clipping datasets will engender a new realm of studies as users of the system will no longer be restricted to solely using the datasets as they reside in their own local systems; instead will be afforded rapid, effective, and transparent access, processing and visualization of the wealth of remote sensing datasets and climate model outputs available.