This Behemoth Of A Scientific Instrument Was Launched Into Orbit So It Could Look Down On Earth To Monitor Its Climate
NCEI’s Visible Infrared Imaging Radiometer Suite (VIIRS) Climate Raw Data Record (C-RDR) is an intermediary product between the Raw Data Record (RDR) product and the many Sensor Data Record (SDR) products for the VIIRS instrument.
The VIIRS instrument is a key element of the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite, which was launched in October 2011.
VIIRS collects data in 22 spectral channels, from visible to longwave infrared, at two different spatial resolutions: 375 m and 750 m at nadir.
The VIIRS C-RDR contains all the raw measurements from the VIIRS RDR collected into time series variables. This simplifies access to the data for reprocessing using alternative calibration and geolocation methods.
The VIIRS C-RDR also provides the coefficients and tables used by the NESDIS Interface Data Processing Segment (IDPS) to convert the raw measurements to science units and calibrate them.
These data are all written to files using the Network Common Data Form 4 (netCDF-4) format, which is platform-independent, binary, hierarchical, and self-describing.
Each variable within a VIIRS C-RDR file is annotated with a description of the measurement, information about the source, and specifications of valid limits and fill values.
Each VIIRS C-RDR file also contains file-level metadata conforming to the Climate and Forecast (CF) metadata conventions, the Attribute Convention for Dataset Discovery (ACDD), and the Joint Polar Satellite System (JPSS) standards for Suomi NPP data products.
Metadata elements, such as granule IDs, which are found in Suomi NPP data product files, are also present in C-RDR files as an aid to understanding the provenance and processing history of the VIIRS C-RDR files.
A number of existing software applications (IDL, MATLAB, etc.) can easily read the variables contained within VIIRS C-RDR files.
Users can also easily access the file contents in their own applications by employing netCDF libraries that are available for FORTRAN, C, C++, Java, or Python.
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