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Abstract. Global climate researchers rely upon many forms of sensor data and analytical methods to help profile subtle changes in climate conditions. The U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) program provides researchers with a collection of curated Value Added Products (VAPs) resulting from continuous sensor data streams, data fusion, and modeling. We are leveraging the Open Provenance Model as a foundational construct that serves the needs of both the VAP producers and consumers. We are organizing the provenance in different tiers of granularity to model VAP lineage, causality at the component level within a VAP, and the causality for each time step as samples are being assembled within the VAP. This paper shares our implementation strategy and how the ARM operations staff and the climate research community can greatly benefit from this approach to more effectively assess and quantify VAP provenance.

The Atmospheric Radiation Measurement (ARM) Data Integrator (ADI) is a framework designed to streamline the development of scientific algorithms that analyze, and models that use time-series NetCDF data. ADI automates the process of retrieving and preparing data for analysis, provides a modular, flexible framework that simplifies software development, and supports a data integration workflow. Algorithm and model input data, preprocessing, and output data specifications are defined through a graphical interface. ADI includes a library of software modules to support the workflow, and a source code generator that produces C, IDL, and Python templates to jump start development. While developed for processing climate data, ADI can be applied to any time-series data. This paper discusses the ADI framework, and how ADI"s capabilities can decrease the time and cost of implementing scientific algorithms allowing modelers and scientists to focus their efforts on their research rather than preparing and packaging data.

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