Enabling Automated Graph-based Search for the Identification and Characterization of Mesoscale Convective Complexes in Satellite Datasets through Integration with the Apache Open Climate Workbench

Tuesday, 16 December 2014
Lewis John McGibbney1, Kim D Whitehall2, Chris A Mattmann1, Cameron E Goodale3, Michael Joyce1, Paul Ramirez3 and Paul Zimdars1, (1)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (2)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States, (3)Jet Propulsion Laboratory, Pasadena, CA, United States
We detail how Apache Open Climate Workbench (OCW) (recently open sourced by NASA JPL) was adapted to facilitate an ongoing study of Mesoscale Convective Complexes (MCCs) in West Africa and their contributions within the weather-climate continuum as it relates to climate variability. More than 400 MCCs occur annually over various locations on the globe. In West Africa, approximately one-fifth of that total occur during the summer months (June-November) alone and are estimated to contribute more than 50% of the seasonal rainfall amounts. Furthermore, in general the non-discriminatory socio-economic geospatial distribution of these features correlates with currently and projected densely populated locations. As such, the convective nature of MCCs raises questions regarding their seasonal variability and frequency in current and future climates, amongst others. However, in spite of the formal observation criteria of these features in 1980, these questions have remained comprehensively unanswered because of the untimely and subjective methods for identifying and characterizing MCCs due to limitations data-handling limitations. The main outcome of this work therefore documents how a graph-based search algorithm was implemented on top of the OCW stack with the ultimate goal of improving fully automated end-to-end identification and characterization of MCCs in high resolution observational datasets.

Apache OCW as an open source project was demonstrated from inception and we display how it was again utilized to advance understanding and knowledge within the above domain. The project was born out of refactored code donated by NASA JPL from the Earth science community’s Regional Climate Model Evaluation System (RCMES), a joint project between the Joint Institute for Regional Earth System Science and Engineering (JIFRESSE), and a scientific collaboration between the University of California at Los Angeles (UCLA) and NASA JPL. The Apache OCW project was then integrated back into the donor code with the aim of more efficiently powering that project. Notwithstanding, the object-oriented approach to creating a core set of libraries Apache OCW has scaled the usability of the project beyond climate model evaluation as displayed in the MCC use case detailed herewith.