Data fusion in remote sensing in the ARM program using Python

ARM uses a vast range of sensors; in-situ, column and volumetric remote sensing. The overarching goal of the program is to build a process level understanding of processes pertinent to the improvement of climate models. To achieve this goal modular software and a flexible development architecture is required to bring a variety of sensors to bear on this problem. This presentation will focus on the understanding of the physical processes surrounding precipitation rocesses and will cover a wide range of topics from operational implementation of retrieval algorithms on mid to large scale resources (> 40 cores) to using forward models integrated within the retrieval framework for both quality control and experiment design purposes. Finally this presentation will underscore the vital role of community-based software in serving the research community in building sustainable long lifecycle code.