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.
Highlights
IntroductionOf the seasonal changes that occur on the Earth’s land surface, perhaps the most profound is the accumulation and melt of seasonal snow cover, affecting climate, weather and the water balance
Computational provenance—a record of the antecedents and processing history of digital information—is key to properly documenting computer-based scientific research
Daily maps are necessary for hydrologic and climate models because of the dynamic nature of snow cover, which changes at a slower time scale than atmospheric phenomena but faster than other surface covers
Summary
Of the seasonal changes that occur on the Earth’s land surface, perhaps the most profound is the accumulation and melt of seasonal snow cover, affecting climate, weather and the water balance. Daily maps are necessary for hydrologic and climate models because of the dynamic nature of snow cover, which changes at a slower time scale than atmospheric phenomena but faster than other surface covers. At finer spatial resolutions necessary for the mountains, remotely sensing snow-water equivalent is an unsolved problem. Provenance is an essential part of metadata for Earth science data products, where both the source data and the processing algorithms change over time. These changes can result from errors (e.g. sensor malfunctions or incorrect algorithms) and from an evolving understanding of the underlying systems and processes (e.g. sensor recalibration or algorithm improvement).
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