New Observing Strategy (NOS) for Future NASA Earth Science Missions
No abstract available
Le Moigne, Jacqueline↗
Engineering topics
Little, Michael M.
Publications and source records attributed to Little, Michael M..
New Observing Strategy (NOS) for Future Earth Science Missions
One of the new thrusts of the Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST) Program is the New Observing Strategy (NOS) thrust. Its goal is to provide a framework for identifying technology advances needed to exploit newly available observational capabilities, particularly to enable the development of the information technologies needed to support planning, evaluating, implementing, and operating dynamic, multi-element sets of observing assets. In this paper, we will introduce relevant NOS terminology and some key concepts before describing the objectives, driving factors and technology goals of this new thrust.
Advanced Information Systems↗
AIST Overview Advanced Information System Technology Fueling Innovation for Earth Observing
One element of NASA's Earth Science Technology Office is the Advanced Information Systems Technology (AIST) Program, which funds information technology development for use in the 5-20 year timeframe. The needs are identified in conjunction with the NASA research and applied sciences communities and discussions with other, forward-looking organizations, such as ESA. AIST projects must have a US lead, but collaborations with non-US organizations are encouraged. The AIST Program focuses on two major thrusts and a small collection of concept studies and solicits both evolutionary and disruptive development projects. One thrust seeks to leverage the emergence of smallsats to create constellations of instruments to examine phenomena that could not be studied before using conventional single-satellite instruments. The second thrust develops re-usable tools to support scientific investigations through the interaction with data. The study projects formulate relevant theory, bleeding-edge concepts or to articulate need statements more clearly for highly advanced work. This talk will describe the research and development funded by the AIST Program.
Little, Michael M.↗
Benchmark Comparison of Cloud Analytics Methods Applied to Earth Observations
Earth Observation data are a vital resource for studying long term changes, but the large data volumes can be challenging to analyze. Time series analysis in particular is hampered by the typical thin-time-slice file organization. We examine several potential solutions inspired in large part by the data-parallel methods that have arisen with cloud computing. These solutions include various combinations of data re-organization, spatial indexing, distributed storage and pre-computation that we term "Analytics Optimized Data Stores" (AODS). We find that even simple solutions (such as a data cube) produce more than an order of magnitude improvement; the best provide two to three orders of magnitude improvement. The most performant solutions have tradeoffs in terms of generality or storage footprint, but may nonetheless be useful components in data analytics frameworks where performance is critical.
parallel processing (computers)↗