A Science-Focused, Scalable, Flexible Instrument Simulation (OSSE) Toolkit for Mission Design

Observing System Simulation Experiments (OSSE's) are used to design mission and instrument constellations and evaluate the science return. A critical component in this process is the simulation of both measurements and retrievals, and the assessment of candidate measurement configurations. The trade space that consists of all possible instrument and spacecraft configurations has expanded tremendously with the recent miniaturization of instruments, and the rise of small-sat (and cube-sat) technology. An exhaustive search through all possible instrument configurations is computationally infeasible with our current tools and infrastructure. Our approach allows quantification of elements in a science and applications traceability matrix (SATM), making it distinct from other mission design toolkits. We propose to develop a fast-turnaround, scalable OSSE Toolkit that can support both rapid and thorough exploration of the trade space of possible instrument configurations, with full assessment of the science fidelity. The capability to rapidly explore various instrument configurations is enabled through the use of both lower fidelity and state-of-the-art simulators and radiative transfer codes, along with a scalable parallel computing framework utilizing the Apache PySpark (Map-Reduce analytics) and xarray/dask technologies. The toolkit will automate the entire mission simulation workflow and scale to large analyses by parallelizing many operations, including the search of the parameter space by an ensemble of runs (30 to 1000x speedups), using cluster computing either on-premise or in the Cloud. The toolkit will consist of: - An Apache Spark & xarray Map-Reduce compute framework with pluggable modules in the form of Docker containers for measurement and retrieval simulation, with the flexibility to plug in other instrument simulators and retrieval codes - A scalable system, tested in the cloud, capable of examining large numbers of potential measurement configurations with the Map/Reduce framework - A front end GUI for easy configuration, plotting, and computation of statistics, and a set of "live" python Notebooks that use JupyterLab and Jupyter Hub, a Python client library, and a web services API. - A Knowledge Base (ElasticSearch JSON doc database) set up to track the simulations performed to facilitate rapid exploration of simulation results and ensure reproducibility The target application is quantitative evaluation of science return from candidate measurements made in the Decadal Survey Aerosols, Clouds, Convection, and Precipitation (A-CCP) mission. In our OSSE workflow, many instrument configurations are simulated in parallel (the Map step), including measurements (e.g., from spaceborne radar) and retrievals (e.g., ice water path and precipitation content profile). Then metrics are aggregated that allow quantitative comparison of the possible configurations (the Reduce step). The majority of the computational work is highly parallelizable by segmenting over time (each instrument view) and the ensemble of parallel runs needed to search and characterize the mission parameter space to evaluate tradeoffs. We utilize instrument simulators that have already been packaged for use within virtual machines, and as such are already capable of running on the Cloud. Our toolkit will enable a breakthrough in the number and fidelity of mission simulations undertaken by: automating the entire pipeline, parallelizing many of the steps, and providing fast turnaround for iterative exploration.

Advance Earth system science knowledge through the identification, development, and demonstration of innovative information systems technologies

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