623 GRAFIIR – an efficient end-to-end semi automated GOES-R ABI algorithm performance analysis and implementation verification system

Wednesday, 26 January 2011
Washington State Convention Center
Hong Zhang, CIMSS/Univ. of Wisconsin, Madison, WI; and M. Gunshor, W. Straka, G. Martin, S. Wanzong, E. Schiffer, R. Garcia, and A. Huang

Handout (2.2 MB)

The NOAA GOES-R mission is the first of the next generation of national geostationary operational environmental satellites. The Advanced Baseline Imager (ABI) on GOES-R represents a technological leap in the nation's satellite sensing capabilities. In support of this mission CIMSS at the University of Wisconsin-Madison is contributing to the critical role of performing tasks for risk reduction, data processing system framework, proving ground, sensor tradeoff, sensor impacts on algorithm performance, and calibration/validation for the ABI. This work is being done in concert with other major ongoing efforts, such as the GOES-R Algorithm Working Group (AWG).

This presentation will overview the updated capability of GOES-R Analysis Facility for Instrument Impacts on Requirements (GRAFIIR). GRAFIIR is a system facility established to leverage a host of projects including AWG proxy, AWG algorithms, AWG McIDAS visualization, GOES-R Risk Reduction, sensor tradeoff and calibration/validation. GRAFIIR is to support GOES-R analysis of instrument impacts on meeting user and product requirements. GRAFIIR is for “connecting the dots”, the components that have been built and/or are under development, to provide a flexible frame work to effectively adopt component algorithms toward analyzing the sensor measurements with different elements of sensor characteristics (i.e. noise, navigation, band to band co-registration, diffraction, etc.) and their impact on products. GRAFIIR continues to play a key role in assisting government on addressing instrument specification waivers.

One of the components developed for GRAFIIR is GLANCE, an efficient comparison analysis tool built to assess and evaluate many of the GOES-R data and products (i.e. imagery, clouds, derived products, soundings, winds, etc.) in a consistent and semi-automated way. This tool can be used to help characterize the effects of changes in sensor characteristics on product performance. It can also be used to quickly test proper product algorithm implementation as various product algorithms are transferred from developers to operators. Furthermore, the concept and build details of GLANCE will also be highlighted to demonstrate a truly functional and effective end-to-end system which supports NOAA's GOES-R ABI project.

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