Easy Access to and Analysis of NASA and Model Swath-Like Data
Charlie Zender - PI, University of California, Irvine
Swath-like data (hereafter SLD) are defined by non-rectangular and/or time-varying spatial grids in which one or more coordinates are multi-dimensional. It is often challenging and time-consuming to work with SLD, including all NASA Level 2 satellite-retrieved data, non-rectangular subsets of Level 3 data, and model data on non-rectangular grids. Researchers and data centers would benefit from user-friendly, fast, and powerful methods to specify, extract, serve, manipulate, and thus analyze, SLD. This project addresses these needs by extending the functionality, user-base, and integration into NASA data services of the netCDF Operators (NCO), an open-source scientific data analysis software package which our current Advancing Collaborative Connections for Earth System Science (ACCESS) project has augmented with Hierarchical Data Format (HDF) capabilities applicable to most archived and virtually all new NASA-distributed data.
The remote sensing and the weather and climate modeling and analysis communities face similar problems in handling SLD including how to easily: 1. Specify and mask (include/exclude) irregular regions such as ocean basins and political boundaries in SLD (and rectangular) grids. 2. Bin, interpolate, average, or re-map SLD to regular grids. 3. Derive secondary data from given quality levels of SLD. These common tasks require a data extraction and analysis toolkit that is SLD-friendly and, like NCO, is used in both communities. We will improve NCO to support these tasks so that users can 1. Analyze regions specified by familiar names (including Climate-Forecast standardized region names) or user-supplied boundaries. 2. Access sophisticated statistical and regridding functions that are robust to missing data. 3. Create, save, share, and re-use user-defined functions or recipes. These capabilities will ease data specification, software-reuse, and, because they apply to SLD, minimize transmission, storage, and handling of unwanted data. Proof of these claims will be demonstrated by applying the improved NCO to a prototypical, NASA-relevant, Earth System Science research problem: to characterize, evaluate, and intercompare Earth System Model-simulated and NASA-retrieved surface energy budget trends and variability in Greenland, a cryospheric region experiencing rapid darkening and nearly unprecedented melt.
NCO is a robust element of the scientific software stack used by the community of Earth Science researchers inside and outside of NASA for over fifteen years. We will coordinate infusion of the enhanced NCO into NASA Earth Science data services offered by NASA's Goddard Earth Science Data and Information Services Center (GES DISC) and Atmospheric Science Data Center (ASDC). Collaboration will include working with data portals such as Giovanni (which already uses NCO) to understand, refine, and address data center needs for SLD capabilities. These centers will expand the community of users interested in their Level 2 data, while researchers will benefit from simplified processing of SLD with higher spatial and temporal resolution and information content than Level 3 data. The proposed work will improve users' ability to access and use EOS data, with methods seamlessly adopted by the modeling and model analysis communities (both are ACCESS 2013 goals). The improved NCO capabilities will apply to all geophysical data archived in HDF and netCDF formats.
The PI is a long-standing climate modeler, software developer, and NASA-funded researcher who understands obstacles to model evaluation by and use of NASA data and who has developed, in the form of NCO, an elegant solution to some. One Co-I is a long-standing information systems architect familiar with NASA data services' operations, community, and vision. The other is primary software engineer responsible for serving data from ASDC. We participate in relevant geoscience communities, including ESDS working groups, IPCC climate assessments, and community model development.
Last Updated: Oct 19, 2018 at 2:15 PM EDT