3 Petabytes or Bust - Planning Science Observations for NISAR

摘要

The National Aeronautics and Space Administration (NASA) and the Indian Space Research Organization (ISRO) have formed a joint agency mission, NASA ISRO Synthetic Aperture Radar (NISAR) to fly in the 2020 timeframe, charged with collecting Synthetic Aperture Radar data over nearly all of earth's land and ice, to advance science in ecosystems, solid-earth and cryospheric disciplines with global time-series maps of various phenomenon. Over a three-year mission span, NISAR will collect on the order of 24 Terabits of raw radar data per day. Developing a plan to collect the data necessary for these three primary science disciplines and their sub-disciplines has been challenging in terms of overlapping geographic regions of interest, temporal requirements, competing modes of the radar instrument, and data-volume resources. One of the chief tools in building a plan of observations against these requirements has been a software tool developed at JPL, the Compressed Large-scale Scheduler Planner (CLASP). CLASP intersects the temporo-geometric visibilities of a spaceborne instrument with campaigns of temporospatial maps of scientific interest, in an iterative squeaky-wheel optimization loop. While the overarching strategy for science observations has evolved through the formulation phases of this mission, so has the use of CLASP. We'll show how this problem space and tool has evolved over time, as well as some of the current parameter estimates for NISAR and its overall mission plan.