Computational provenance in hydrologic science: a snow mapping example
Abstract
Computational provenance—a record of the antecedents and processing history of digital
information—is key to properly documenting computer-based scientific research. To support
investigations in hydrologic science, we produce the daily fractional snow- covered area from
NASA’s moderate-resolution imaging spectroradiometer (MODIS). From the MODIS reflectance data in
seven wavelengths, we estimate the fraction of each 500 m pixel that snow covers. The daily
products have data gaps and errors because of cloud cover and sensor viewing geometry, so we
interpolate and smooth to produce our best estimate of the daily snow cover. To manage the data, we
have developed the Earth System Science Server (ES3), a software environment for data-intensive
Earth science, with unique capabilities for automatically and transparently capturing and managing
the provenance of arbitrary computations. Transparent acquisition avoids the scientists having to
express their computations in specific languages or schemas in order for provenance to be acquired
and maintained. ES3 models provenance as relationships between processes and their input and output
files. It is particularly suited to capturing the provenance of an evolving algorithm whose
components span multiple languages and execution environments.
Description
The article of record as published may be found at http://dx.doi.org/10.1098/rsta.2008.0187
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