Scientific analysis and visualization of tropical cyclone's using CloudSat and A-Train data in python

Recent advancements using python to process and visualize data specific to the earth observing satellite mission, CloudSat and other A-Train satellites will be presented. While overpasses of CloudSat's nadir-pointing cloud profiling radar antenna over tropical cyclones occur infrequently in comparison to the much denser sampling of clouds and precipitation, they do happen enough to provide compilations of data that can uniquely serve the research community for examining storm structure and its variation as a function of, for example, the surrounding environment. With over 8,000 CloudSat overpasses of global tropical cyclones, processing and visualizing the data is essential to examining the dataset.

Python is an effective platform for analyzing and visualizing CloudSat and A-Train datasets using modules such as PyNIO, numpy, scipy and matplotlib to name a few. We will explore how the dataset of CloudSat overpasses is created and automated using python techniques. We will present examples using ipython notebooks visualizing the dataset in relation to the storm overpass using different sources of visual and infrared satellite imagery.