Computational provenance in hydrologic science: a snow mapping example
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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.
The article of record as published may be found at http://dx.doi.org/10.1098/rsta.2008.0187