371 Methods and Tools for Product Quality Maintenance in JPSS CGS

Monday, 11 January 2016
Kerry Grant, Raytheon Intelligence, Information and Services, Aurora, CO; and W. Ibrahim, K. Brueske, and P. Smit


The National Oceanic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration (NASA) are jointly acquiring the next-generation civilian weather and environmental satellite system: the Joint Polar Satellite System (JPSS). The Joint Polar Satellite System will replace the afternoon orbit component and ground processing system of the current Polar-orbiting Operational Environmental Satellites (POES) managed by the National Oceanic and Atmospheric Administration. The Joint Polar Satellite System satellites will carry a suite of sensors designed to collect meteorological, oceanographic, climatological, and solar-geophysical observations of the earth, atmosphere, and space. The ground processing system for the Joint Polar Satellite System is known as the Common Ground System (JPSS CGS), and provides command, control, and communications (C3) and data processing and product delivery. As a multi-mission system, CGS provides combinations of C3, data processing, and product delivery for numerous NASA, NOAA, Department of Defense (DoD), and international missions, such as NASA's Earth Observation System (EOS), NOAA's current POES, the Japan Aerospace Exploration Agency's (JAXA) Global Change Observation Mission – Water (GCOM-W1), and DoD's Defense Meteorological Satellite Program (DMSP).


CGS's data processing capability processes the satellite data from the Joint Polar Satellite System satellites to provide environmental data products (including Sensor Data Records (SDRs) and Environmental Data Records (EDRs)) to the National Oceanic and Atmospheric Administration and Department of Defense processing centers operated by the United States government. The first satellite in the JPSS constellation, known as the Suomi National Polar-orbiting Partnership (S-NPP) satellite, was launched on 28 October 2011. CGS is currently processing and delivering SDRs and EDRs for S-NPP and will continue through the lifetime of the Joint Polar Satellite System programs. The EDRs for Suomi NPP are currently undergoing an extensive Calibration and Validation (Cal/Val) campaign. As Cal/Val changes migrate into the operational system, long term monitoring activities will begin to track product quality and stability. In conjunction with NOAA's Office of Satellite and Product Operations (OSPO) and the NASA JPSS Project Office, Raytheon is supporting this effort through the development and use of tools, techniques, and processes designed to detect changes in product quality, identify root causes, and rapidly implement changes to the operational system to bring suspect products back into specification. 2.1. Theoretical basis

To provide a basis for this support, Raytheon has developed a theoretical analysis framework, and the application of derived engineering processes, for the maintenance of consistency and integrity of remote sensing operational algorithm outputs. The framework is an abstraction of the operationalization of the science-grade algorithm (Sci2Ops) process currently in use, and is applied towards both Suomi-NPP and JPSS. The framework consists of two data categories – benchmark and experimental– and two analysis variation categories – principle and non-principle. Experimental data advances to benchmark data iteratively as the operational algorithm baseline evolves. This framework led to the development of a process to assess, characterize (qualitatively and quantitatively) and accept updated operational algorithm outputs. The advancement of experimental data to the benchmark state includes only principle variations (i.e., variations due to known external variables). Non-principle variations (e.g., errors in software code, requirements, or interfaces), through rigorous software or systems engineering processes, are identified and removed. By combining software and systems engineering controls, manufacturing disciplines to detect and reduce defects, and a standard process to control analysis, an environment to maintain operational algorithm maturity is achieved. 2.2. Targeted data mining

Modern earth observing systems, such as the Suomi National Polar-orbiting Partnership (Suomi-NPP), are characterized by generational increases in resolution, spectral sampling, and data volume. Continuously increasing data volume poses significant challenges to investigators interested in specific geophysical characteristics for a variety of purposes including the development, test and evaluation of sensor-specific algorithms. For Suomi-NPP, the Visible Infrared Imager Radiometer Suite (VIIRS) instrument poses the greatest challenge due to its unprecedented data volume. Despite its data volume challenges, the VIIRS Cloud Mask (VCM) Intermediate Product (IP) offers a characterizing gateway to identify and extract sub-orbit granules of interest based on geophysical characteristics of interest (e.g., a granule containing a high-percentage of multi-layered, multi-phase clouds, over land at high-latitudes). A “data-mining” tool has been created using the VCM IP Quality Flag (QF) output. A tailored graphical user interface combining user-defined VCM IP QF values, QF bundling logic, as well as geographic and solar/geophysical constraints enable quantitative analysis and identification of a “best match” from multiple-day, multi-orbit datasets for either cloud-or-downstream algorithm processing. The working prototype is an early version of an envisioned, machine-to-machine web map service enhancement to the JPSS data processing system, in support of the maintenance of data quality. This paper will discuss both the theoretical basis and the actual practices used to date to identify, test and incorporate algorithm updates into the CGS processing baseline. This includes the theoretical analysis framework, and the application of derived engineering processes, for the maintenance of consistency and integrity of remote sensing operational algorithm outputs, as well as the results of the use of this approach to implement algorithm changes into operations.

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