ASIS: Aggregation of Services for Ice Sheets
Principal Investigator (PI): Mark Parsons, University of Colorado-Boulder
Ice sheet mass balance is a critical—the critical—parameter for forecasting sea level in the coming decades. While ocean thermal expansion and mountain glacier ice loss currently account for most of the rate of sea level change, sea level changes will be increasingly dominated by changes in the ice sheets, and these changes will increase significantly over this century (in fact, this is happening already). Moreover, changes observed in the ice sheets within the past decade have been larger, faster, and more variable than previously forecast in many areas. The problem of glacier and ice sheet response to changing climate and ocean conditions is more complex than was anticipated. Many research efforts are currently underway, gathering data in support of better assessments and modeling.
Ice sheet mass balance may be determined in several ways: by direct measurement of volume change; direct measurement of mass change; or by an accounting of net inputs and outputs. All the approaches are required to give the best assessment of rates and causes, and each requires management of extensive and distributed data sets. For example, the “accounting” method requires an assessment of snow accumulation rates, ice thickness, ice flow, and basal melt rate. These parameters are measured by a myriad of separate geophysical methods (field work, satellite, model results), spanning many different efforts by many projects and PIs. Data sets may be point measurements, traverse (line) measurements, or spatial grids of modeled/interpolated data. Moreover, the data may vary significantly over time, in particular, for ice flow, and in some areas ice thickness. Understanding, making use, and even just discovering this broad range of data is a fundamental challenge for polar researchers.
The informatics and scientific community have been developing a variety of services to help assess, interpret, and analyze these diverse data. These services (many developed by NASA) include mapping and visualization, automatic data subsetting or aggregation, format conversion, change notification, and so on. However, currently there is no ready way to discover all these services. Current approaches to the issues of data and service discovery, data publishing, and data annotation often rely on centralized mechanisms such as metadata directories or catalogs and data centers that work with investigators to produce and publish formal data and documentation. Conversely, data providers increasingly self-publish their data but often in an ad hoc fashion and without the levels of service and support that formal archives can provide. The result of this is that users often do not have broad knowledge of the services that are available for their data of interest.
Kevin Werbach recently discussed the reasons for the phenomenal success of dotcom companies eBay, Amazon, and Google, noting "all of them aggressively open up their technical interfaces, allowing other sites to plug into them, or projecting themselves out to the rest of the Web ... the new paradigm ... is syndication. Open up your core assets and turn them into a platform; don't hide behind high walls and expect everyone to come to you."
Following these successful models, this project seeks to "aggressively open up" the technical interfaces to polar science data and web services based on those data by implementing several new applications of existing, open-source technologies to create a working prototype of a federated and community-enabled approach to data and service sharing and discovery. The project will focus on data services important to understanding ice sheet mass balance from both field and remote sensing measurements.
However, our approach is generalizable and extensible to other disciplines and other applications. This will help sustain long-term usability and evolution of the tools and services we develop.
Deployed at NASA's National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC)
Last Updated: Jan 22, 2020 at 9:10 AM EST