Server-side Filtering and Aggregation within a Distributed Environment
Abstract
Intercalibration, validation, and data mining use cases require more efficient access to the massive volumes of observation data distributed across multiple agency data centers. The traditional paradigm of downloading large volumes of data to a centralized server or desktop computer for analysis is no longer viable. More analysis should be performed within the host data centers using server-side functions. Many comparative analysis tasks require far less than 1% of the available observation data. The Multi-Instrument Intercalibration (MIIC) Framework provides web services to find, match, filter, and aggregate multi-instrument observation data. Matching measurements from separate spacecraft in time, location, wavelength, and viewing geometry is a difficult task especially when data are distributed across multiple agency data centers. Event prediction services identify near coincident measurements with matched viewing geometries near orbit crossings using complex orbit propagation and spherical geometry calculations. The number and duration of event opportunities depend on orbit inclinations, altitude differences, and requested viewing conditions (e.g., day/night). Event observation information is passed to remote server-side functions to retrieve matched data. Data may be gridded, spatially convolved onto instantaneous field-of-views, or spectrally resampled or convolved. Narrowband instruments are routinely compared to hyperspectal instruments such as AIRS and CRIS using relative spectral response (RSR) functions. Spectral convolution within server-side functions significantly reduces the amount of hyperspectral data needed by the client. This combination of intelligent selection and server-side processing significantly reduces network traffic and data to process on local servers. OPeNDAP is a mature networking middleware already deployed at many of the Earth science data centers. Custom OPeNDAP server-side functions that provide filtering, histogram analysis (1D and 2D), spatial and spectral convolution deployed at NASA and NOAA facilities including LAADS, GES DISC, NCEI, and ASDC will enable efficient access to VIIRS, MODIS, AIRS, CRIS, CERES, and CALIPSO observation data in support of intercalibration, validation, and data mining studies.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2015
- Bibcode:
- 2015AGUFMIN23C1742C
- Keywords:
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- 1914 Data mining;
- INFORMATICS;
- 1918 Decision analysis;
- INFORMATICS;
- 1942 Machine learning;
- INFORMATICS;
- 1968 Scientific reasoning/inference;
- INFORMATICS