Keywords: deep analysis, big data, Earth data
Precipitation, including the process of precipitation, has both operational and fundamental scientific importance. It is the part of the local and global water cycle that concentrates the heat used to evaporate the water that is transported in the atmosphere, and it delivers water to the ocean or land surfaces. Satellite remote sensing of cloud properties and precipitation potentially offers detailed records at a spatial and temporal scale small enough to resolve the local features of precipitating cloud systems over decades and across the planet. Thus, scalable data analysis techniques become ever more critical in anticipation of the deluge of current and future satellite data. In this talk, we present the analysis of two satellite-based data: precipitation on fixed 0.25 deg x 0.25 deg horizontal mesh and 3-hourly time intervals from the tropical rainfall measuring mission (TRMM, 1997--2014) and the 2006--2014 asynoptic CloudSat and CALIPSO cloud and aerosol products from NASA's A-Train mission. In combination with the 6-hourly ECMWF global data assimilation (reanalysis) products of atmospheric motions, temperature, and humidity at 1.5 deg x 1.5 deg horizontal resolution from 1997 to 2014, we attempt to infer the heating in the atmosphere associated with cloud processes, the ensuing precipitation, and the accompanying multiscale air motions. We then examine the time-change of this highly complicated coupled system. These analyses have been facilitated with the open source DeltaRho (\url{http://deltarho.org}) on clusters run on the Hadoop distributed file system.