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Larsen, Brian

Publications and source records attributed to Larsen, Brian.

Science of the Van Allen Probes Science Operations Centers

The Van Allen Probes mission operations materialized through a distributed model in which operational responsibility was divided between the Mission Operations Center (MOC) and separate instrument specific SOCs. The sole MOC handled all aspects of telemetering and receiving tasks as well as certain scientifically relevant ancillary tasks. Each instrument science team developed individual instrument specific SOCs proficient in unique capabilities in support of science data acquisition, data processing, instrument performance, and tools for the instrument team scientists. In parallel activities, project scientists took on the task of providing a significant modeling tool base usable by the instrument science teams and the larger scientific community. With a mission as complex as Van Allen Probes, scientific inquiry occurred due to constant and significant collaboration between the SOCs and in concert with the project science team. Planned cross-instrument coordinated observations resulted in critical discoveries during the seven-year mission. Instrument cross-calibration activities elucidated a more seamless set of data products. Specific topics include post-launch changes and enhancements to the SOCs, discussion of coordination activities between the SOCs, SOC specific analysis software, modeling software provided by the Van Allen Probes project, and a section on lessons learned. One of the most significant lessons learned was the importance of the original decision to implement individual team SOCs providing timely and well-documented instrument data for the NASA Van Allen Probes Mission scientists and the larger magnetospheric and radiation belt scientific community.

47 OTHER INSTRUMENTATION↗

dbprocessing

dbprocessing is a python framework for automating data processing pipelines. The dbprocessing package uses a configuration file to define the input, intermediate, and output data products for a particular data type as well as the processes that connect those data products. In addition, dbprocessing uses python scripts called inspectors to determine if a particular file matches a configured data product. If a particular data product is found, it is ingested into the dbprocessing database (sqlite3 or postgres) which then triggers all of the chained processes to the final data product. While the framework is written in python, it can run software in any language but may require a “wrapper” to translate the dbprocessing command line arguments to the form expected by the software.

Walker, Andrew↗